Mass Collaboration

Turning the Improbable
Into the Exceptional!
Page 2 of 206

The Advocacy Foundation, Inc.
Helping Individuals, Organizations & Communities
Achieve Their Full Potential
Since its founding in 2003, The Advocacy Foundation has become recognized as an effective
provider of support to those who receive our services, having real impact within the communities
we serve. We are currently engaged in community and faith-based collaborative initiatives,
having the overall objective of eradicating all forms of youth violence and correcting injustices
everywhere. In carrying-out these initiatives, we have adopted the evidence-based strategic
framework developed and implemented by the Office of Juvenile Justice & Delinquency
Prevention (OJJDP).
The stated objectives are:
1. Community Mobilization;
2. Social Intervention;
3. Provision of Opportunities;
4. Organizational Change and Development;
5. Suppression [of illegal activities].
Moreover, it is our most fundamental belief that in order to be effective, prevention and
intervention strategies must be Community Specific, Culturally Relevant, Evidence-Based, and
Collaborative. The Violence Prevention and Intervention programming we employ in
implementing this community-enhancing framework include the programs further described
throughout our publications, programs and special projects both domestically and
internationally.
www.TheAdvocacy.Foundation
ISBN: ......... ../2017
......... Printed in the USA
Advocacy Foundation Publishers
Philadlephia, PA
(878) 222-0450 | Voice | Data | SMS
Page 3 of 206

Page 4 of 206

Dedication
______
Every publication in our many series’ is dedicated to everyone, absolutely everyone, who by
virtue of their calling and by Divine inspiration, direction and guidance, is on the battlefield dayafter-day
striving to follow God’s will and purpose for their lives. And this is with particular affinity
for those Spiritual warriors who are being transformed into excellence through daily academic,
professional, familial, and other challenges.
We pray that you will bear in mind:
Matthew 19:26 (NIV)
Jesus looked at them and said, "With man this is impossible,
but with God all things are possible." (Emphasis added)
To all of us who daily look past our circumstances, and naysayers, to what the Lord says we will
accomplish:
Blessings!!
- The Advocacy Foundation, Inc.
Page 5 of 206

Page 6 of 206

The Transformative Justice Project
Eradicating Juvenile Delinquency Requires a Multi-Disciplinary Approach
The way we accomplish all this is a follows:
The Juvenile Justice system is incredibly overloaded, and
Solutions-Based programs are woefully underfunded. Our
precious children, therefore, particularly young people of
color, often get the “swift” version of justice whenever they
come into contact with the law.
Decisions to build prison facilities are often based on
elementary school test results, and our country incarcerates
more of its young than any other nation on earth. So we at
The Foundation labor to pull our young people out of the
“school to prison” pipeline, and we then coordinate the efforts
of the legal, psychological, governmental and educational
professionals needed to bring an end to delinquency.
We also educate families, police, local businesses, elected
officials, clergy, and schools and other stakeholders about
transforming whole communities, and we labor to change
their thinking about the causes of delinquency with the goal
of helping them embrace the idea of restoration for the young
people in our care who demonstrate repentance for their
mistakes.
1. We vigorously advocate for charges reductions, wherever possible, in the adjudicatory (court)
process, with the ultimate goal of expungement or pardon, in order to maximize the chances for
our clients to graduate high school and progress into college, military service or the workforce
without the stigma of a criminal record;
2. We then enroll each young person into an Evidence-Based, Data-Driven Restorative Justice
program designed to facilitate their rehabilitation and subsequent reintegration back into the
community;
3. While those projects are operating, we conduct a wide variety of ComeUnity-ReEngineering
seminars and workshops on topics ranging from Juvenile Justice to Parental Rights, to Domestic
issues to Police friendly contacts, to CBO and FBO accountability and compliance;
4. Throughout the process, we encourage and maintain frequent personal contact between all
parties;
5 Throughout the process we conduct a continuum of events and fundraisers designed to facilitate
collaboration among professionals and community stakeholders; and finally
Page 7 of 206

6. 1 We disseminate Quarterly publications, like our e-Advocate series Newsletter and our e-Advocate
Quarterly electronic Magazine to all regular donors in order to facilitate a lifelong learning process
on the ever-evolving developments in the Justice system.
And in addition to the help we provide for our young clients and their families, we also facilitate
Community Engagement through the Restorative Justice process, thereby balancing the interesrs
of local businesses, schools, clergy, elected officials, police, and all interested stakeholders. Through
these efforts, relationships are rebuilt & strengthened, local businesses and communities are enhanced &
protected from victimization, young careers are developed, and our precious young people are kept out
of the prison pipeline.
This is a massive undertaking, and we need all the help and financial support you can give! We plan to
help 75 young persons per quarter-year (aggregating to a total of 250 per year) in each jurisdiction we
serve) at an average cost of under $2,500 per client, per year.*
Thank you in advance for your support!
* FYI:
1. The national average cost to taxpayers for minimum-security youth incarceration, is around
$43,000.00 per child, per year.
2. The average annual cost to taxpayers for maximun-security youth incarceration is well over
$148,000.00 per child, per year.
- (US News and World Report, December 9, 2014);
3. In every jurisdiction in the nation, the Plea Bargain rate is above 99%.
The Judicial system engages in a tri-partite balancing task in every single one of these matters, seeking
to balance Rehabilitative Justice with Community Protection and Judicial Economy, and, although
the practitioners work very hard to achieve positive outcomes, the scales are nowhere near balanced
where people of color are involved.
We must reverse this trend, which is right now working very much against the best interests of our young.
Our young people do not belong behind bars.
- Jack Johnson
1
In addition to supporting our world-class programming and support services, all regular donors receive our Quarterly e-Newsletter
(The e-Advocate), as well as The e-Advocate Quarterly Magazine.
Page 8 of 206

The Advocacy Foundation, Inc.
Helping Individuals, Organizations & Communities
Achieve Their Full Potential
…a collection of works on
Mass
Collaboration
“Turning the Improbable Into the Exceptional”
Atlanta
Philadelphia
______
John C Johnson III
Founder & CEO
(878) 222-0450
Voice | Data | SMS
www.TheAdvocacy.Foundation
Page 9 of 206

Page 10 of 206

Biblical Authority
______
Philippians 2 (NIV)
Imitating Christ’s Humility
2 Therefore if you have any encouragement from being united with Christ, if any
comfort from his love, if any common sharing in the Spirit, if any tenderness and
compassion, 2 then make my joy complete by being like-minded, having the same
love, being one in spirit and of one mind. 3 Do nothing out of selfish ambition or vain
conceit. Rather, in humility value others above yourselves, 4 not looking to your own
interests but each of you to the interests of the others.
5
In your relationships with one another, have the same mindset as Christ Jesus:
6
Who, being in very nature God,
did not consider equality with God something to be used to his own advantage;
7
rather, he made himself nothing
by taking the very nature of a servant,
being made in human likeness.
8
And being found in appearance as a man,
he humbled himself
by becoming obedient to death—
even death on a cross!
9
Therefore God exalted him to the highest place
and gave him the name that is above every name,
10
that at the name of Jesus every knee should bow,
in heaven and on earth and under the earth,
11
and every tongue acknowledge that Jesus Christ is Lord,
to the glory of God the Father.
Do Everything Without Grumbling
12
Therefore, my dear friends, as you have always obeyed—not only in my presence,
but now much more in my absence—continue to work out your salvation with fear
and trembling, 13 for it is God who works in you to will and to act in order to fulfill his
good purpose.
Page 11 of 206

14 Do everything without grumbling or arguing, 15 so that you may become
blameless and pure, “children of God without fault in a warped and crooked
generation.” Then you will shine among them like stars in the sky 16 as you hold
firmly to the word of life. And then I will be able to boast on the day of Christ that I
did not run or labor in vain. 17 But even if I am being poured out like a drink
offering on the sacrifice and service coming from your faith, I am glad and rejoice
with all of you. 18 So you too should be glad and rejoice with me.
Timothy and Epaphroditus
19
I hope in the Lord Jesus to send Timothy to you soon, that I also may be cheered
when I receive news about you. 20 I have no one else like him, who will show
genuine concern for your welfare. 21 For everyone looks out for their own
interests, not those of Jesus Christ. 22 But you know that Timothy has proved
himself, because as a son with his father he has served with me in the work of the
gospel. 23 I hope, therefore, to send him as soon as I see how things go with
me. 24 And I am confident in the Lord that I myself will come soon.
25
But I think it is necessary to send back to you Epaphroditus, my brother, coworker
and fellow soldier, who is also your messenger, whom you sent to take care
of my needs. 26 For he longs for all of you and is distressed because you heard he
was ill. 27 Indeed he was ill, and almost died. But God had mercy on him, and not on
him only but also on me, to spare me sorrow upon sorrow. 28 Therefore I am all the
more eager to send him, so that when you see him again you may be glad and I
may have less anxiety. 29 So then, welcome him in the Lord with great joy, and
honor people like him, 30 because he almost died for the work of Christ. He risked his
life to make up for the help you yourselves could not give me.
______
See Also:
1 Corinthians 12:1-31 Romans 12:4-5
Romans 15:5-6 Proverbs 11:14 Proverbs 27:17
Ephesians 4:11-16 Ecclesiastes 4:9-12 1 Corinthians 1:10
Hebrews 10:24-25 Nehemiah 4:6
Page 12 of 206

Table of Contents
…a compilation of works on
Mass Collaboration
Biblical Authority
I. Introduction: Mass Collaboration………………………………….. 15
II. Collective Intelligence………………………………………………. 25
III. Digital Collaboration……………………………………………….... 49
IV. Open Collaboration………………………………………………….. 57
V. Crowdmapping & Crowdsensing ………………………………….. 77
Big Data & Data Analysis
VI. Think Tanks…………….……………………………………………. 121
VII. Collaborative Decision-Making……………………………............ 159
VIII. References…………………………………………………….......... 171
______
Attachments
A. Mass Collaboration - Discussion Paper
B. Mass Collaboration Systems on The World Wide Web
C. The Potential of Mass Collaboration to Produce Social Innovation
D. How Field Catalysts Galvanize Social Change
Copyright © 2018 The Advocacy Foundation, Inc. All Rights Reserved.
Page 13 of 206

Page 14 of 206

I. Introduction
Mass Collaboration is a form of collective action that occurs when large
numbers of people work independently on a single project, often modular in its nature.
Such projects typically take place on the internet using social software and computersupported
collaboration tools such as wiki technologies, which provide a potentially
infinite hypertextual substrate within which the collaboration may be situated.
Modularity
Factors
Modularity enables a
mass of experiments
to proceed in parallel, with different
teams working on the same modules, each
proposing different solutions. Modularity
allows different "blocks" to be easily
assembled, facilitating
decentralised
innovation that all fits
together.
Differences
Cooperation
Mass collaboration differs from mass cooperation in that the creative acts taking place
require the joint development of shared understandings. Conversely, group members
involved in cooperation needn't engage in a joint negotiation of understanding; they may
simply execute instructions willingly.
Another important distinction is the borders around which a mass cooperation can be
defined. Due to the extremely general characteristics and lack of need for fine grain
negotiation and consensus when cooperating, the entire internet, a city, and even the
global economy may be regarded as examples of mass cooperation. Thus mass
collaboration is more refined and complex in its process and production on the level of
collective engagement.
Online Forum
Although an online discussion is certainly collaborative, mass collaboration differs from
a large forum, email list, bulletin board, chat session or group discussion in that the
discussion's structure of separate, individual posts generated through turn-taking
communication means the textual content does not take the form of a single, coherent
body. Of course the conceptual domain of the overall discussion exists as a single
unified body, however the textual contributors can be linked back to the understandings
Page 15 of 206

and interpretations of a single author. Though the author's understandings and
interpretations are most certainly a negotiation of the understandings of all who read
and contribute to the discussion, the fact that there was only one author of a given entry
reduces the entry's collaborative complexity to the discursive/interpretive as opposed to
constructive/‘negotiative’ levels.
Coauthoring
From the perspective of individual sites of work within a mass collaboration, the activity
may appear to be identical to that of coauthoring. In fact, it is, with the exception being
the implicit and explicit relationships formed by the interdependence that many sites
within a mass collaboration share through hypertext and coauthorship with differing sets
of collaborators. This interdependence of collaborative sites coauthored by a large
number of people is what gives a mass collaboration one of its most distinguishing
features - a coherent collaboration emerging from the interrelated collection of its parts.
Collective Online Tools
Many of the web applications associated with Bulletin boards, or forums can include a
wide variety of tools that allows individuals to keep track of sites and content that they
find on the internet. Users are able to bookmark from their browser by editing the title,
adding a description and most importantly classifying using tags. Other non-collective
tools are also used in Mass collaborative environments such as commenting, rating and
quick evaluation.
Business
Changes
In the books Wikinomics: How Mass Collaboration Changes Everything and
MacroWikinomics-Rebooting business and the world, Don Tapscott and Anthony
Williams list five powerful new ideas that the new art and science of wikinomics is based
on:
being open
peering
sharing
acting globally
interdependence
The concept of mass collaboration has led to a number of efforts to harness and
commercialize shared tasks. Collectively known as crowdsourcing, these ventures
typically involve on an online system of accounts for coordinating buyers and sellers of
labor. Amazon's Mechanical Turk system follows this model, by enabling employers to
distribute minute tasks to thousands of registered workers. In the advertising industry,
Giant Hydra employs mass collaboration to enable creatives to collaborate on
Page 16 of 206

advertising ideas online and create what they call an 'idea matrix', a highly complex
node of concepts, executions and ideas all connected to each other. In the financial
industry, companies such as the Open Models Valuation Company (OMVCO) also
employ mass collaboration to improve the accuracy of financial forecasts.
The Role of Discussion
In traditional collaborative scenarios discussion plays a key role in the negotiation of
jointly developed, shared understandings (the essence of collaboration), acting as a
point of mediation between the individual collaborators and the outcome which may or
may not eventuate from the discussions. Mass collaboration reverses this relationship
with the work being done providing the point of mediation between collaborators, with
associated discussions being an optional component. It is of course debatable that
discussion is optimal, as most (if not all) mass collaborations have discussions
associated with the content being developed.
However it is possible to contribute (to Wikipedia for instance) without discussing the
content you are contributing to. (Smaller scale collaborations might be conducted
without discussions especially in a non-verbal mode of work - imagine two painters
contributing to the same canvas - but the situation becomes increasingly problematic as
more members are included.)
Page 17 of 206

Non-Textual
Although the only widely successful examples of mass collaboration thus far evaluated
exist in the textual medium, there is no immediate reason why this form of collective
action couldn't work in other creative media. It could be argued that some projects within
the open source software movement provide examples of mass collaboration outside of
the traditional written language (see below), however, the code collaboratively created
still exists as a language utilizing a textual medium. Music is also a possible site for
mass collaboration, for instance on live performance recordings where audience
members' voices have become part of the standard version of a song. Most
"anonymous" folk songs and "traditional" tunes are also arguably sites of long term
mass collaboration.
______
Collaborative Innovation Networking
Collaborative Innovation is a process in which multiple players (within and
outside an organization) contribute towards creating and developing new products,
services, processes and business solutions. It might include the involvement of
customers, suppliers and multiple stakeholders such as agencies and consultants
Usually, firms that promote open forms of collaboration benefit from having access to
different capabilities and knowledge, enhancing their competitiveness and accelerating
their innovation process. On one hand, it enables small companies such as start-ups to
partner with other players, complementing each other and taking advantage of different
perspectives and resources. On the other hand, it helps large companies to speed-up
their innovation process and time-to-market, overcoming bureaucracy and inflexible
procedures.
Collaboration can occur in all aspects of the business cycle, depending on the context:
Procurement and supplier collaboration
Research and development of new products, services and technologies
Marketing, distribution and commercialization
Collaborative innovation network (CoIN) is a type of collaborative innovation practice
that makes use of the internet platforms such as email, chat, social networks, blogs, and
Wikis to promote communication and innovation within self-organizing virtual teams.
The difference is that people that collaborate in CoIN are so intrinsically motivated that
might not be paid nor get any advantage.
Thus, a CoIN is a social construct with a huge potential for innovation. It has been
defined by the originator of the term, Peter Gloor from MIT Sloan's Center for Collective
Intelligence, as "a cyberteam of self-motivated people with a collective vision, enabled
Page 18 of 206

y the Web to collaborate in achieving a common goal by sharing ideas, information,
and work".
Indeed, CoIN is a type of open collaboration that helps organizations to become more
creative, productive and efficient. By adopting CoIN as part of their culture, these
companies accelerate innovation, uncover hidden business opportunities, reduce costs
and enhance synergies. They not only can engage employees from every level of
hierarchy towards a common project (discovering new talents and promoting direct
relation between employees) but also partner with external parties.
Similar is the concept of the "Self-Organizing Innovation Network", it has been
described by author, Robert Rycroft of the Elliott School of International Affairs of
George Washington University.
Overview
CoINs feature internal transparency and direct communication. Members of a COIN
collaborate and share knowledge directly with each other, rather than through
hierarchies. They come together with a shared vision because they are intrinsically
motivated to do so and seek to collaborate in some way to advance an idea.
CoINs work across hierarchies and boundaries in which members can directly and
openly exchange ideas and information. This collaborative and transparent environment
fosters innovation. Gloor describes phenomenon as "swarm creativity". According to
him, "COINs are the best engines to drive innovation".
Page 19 of 206

CoINs existed well before the advent of modern communication technology. However,
internet and instant communication improved productivity and enabled the reach of a
global scale. Today, they rely on Internet, e-mail, and other communications vehicles for
information sharing.
According to Peter Gloor, CoINs have 5 main characteristics:
Dispersed Membership: technology allows members to be spread over the
world. Regardless of the location, members share a common goal and are
convinced of their common cause.
Interdependent Membership: cooperation between members is key to achieve
common goal. The work of one member is affected and interdependent on the
others' work.
No Simple Chain of Command: there is no superior command. It is a
decentralized and self-organized system. Conflicts are solved without the need of
a hierarchy or authority.
Work Towards a Common Goal: members are willing to contribute, work and
share freely. They are intrinsically motivated to donate their work, create and
share knowledge in favor of a common goal.
Dependence on Trust: cooperative behavior and mutual trust is needed to work
efficiently within the network. Members act accordingly to an ethical code that
states the rules and principles to be followed by all members. Usually, ethical
codes include principles related to respect, consistency, reciprocity and
rationality.
There also are five essential elements of collaborative innovation networks (what Gloor
calls as "genetic code"):
1. They Are Learning Networks: they set an informal and flexible
environment which facilitates and stimulates collaboration and the
exchange of ideas, information and knowledge.
2. They Need An Ethical Code: they agree on an ethical code that guides
the conduct and behavior of the members.
3. They Are Based On Trust And Self-Organization: members trust each
other without the need of a centralized management. They are brought
together by mutual respect and strong sense of shared beliefs.
4. They Make Knowledge Accessible To Everyone: CoINs nurture
communication to an extent that information shared with everyone.
Page 20 of 206

Nowadays, with internet and social medias, their ideas and concepts
achieve a global level.
5. They Operate In Internal Honesty And Transparency: they create a
system based on reciprocal trust and mutually established principles.
Examples
CoINs have been developing many disruptive innovations such as the Internet, Linux,
the Web and Wikipedia. These inventions were created in universities or labs by a
group of students with little or no budget. They were not focused on the money but on
the sense of accomplishment.
The Web is the early version of Internet. It was driven by a CoIN of intrinsically
motivated people that wanted to improve technical development and launch a disruptive
solution. Their goal was to link mainframes and allow multiple users simultaneously.
Another contribution was Linux, an operating system for personal computing that
directly competes with Microsoft. It was initially developed by a student called Linus
Torvalds and later became an open source software. The code is publicly available and
anyone can contribute or enhance it. The success of Linux is the constant and
continuous updating which is done at a much lower cost than closed source software.
Page 21 of 206

Wikipedia gathers thousands of volunteers that constantly write and update content.
Although it does not have a hierarchy nor a central authority, the entries are mostly
accurate and complete. Volunteers share a strong feeling of community and willingness
to contribute towards knowledge without being paid for it.
Faced with these creations, large companies such as IBM and Intel have learnt to use
the principles of open innovation to enhance their research learning curve. They
increased or established collaborations with universities, agencies and small companies
to accelerate their processes and launch new services faster.
Collaborative Innovation Network Factors
Asheim and Isaksen (2002) conclude that innovative network contribute to the
achievement of optimal allocation of resources, and promoting knowledge transfer
performance. However, there are four factors of collaborative innovation network that
differentely affect the performance of COINs. Those factors are:
1. Network Size: network size is the number of partners such as enterprises,
universities, research institutions, intermediaries, and government departments in
an innovative network. Previous work reveals that network size has a positive
effect on knowledge transfer as it provides the actor (e.g. firm) with two major
substantive benefits: one is the exposure to a larger amount of external
information, knowledge, and ideas and the other is resource sharing between the
actor and its contacts such as knowledge sharing, reduction of transaction costs,
complementarities, and scale.
2. Network Heterogeneity: network heterogenity refers to differences in the
knowledge, technology, ability, and size of members in the network. Firms in a
more heterogeneous network have a higher probability to acquire external
knowledge resources. When network heterogeneity is higher, getting
complementary resources and accelerating the speed of knowledge transfer are
easier.
3. Network Tie-Strength: Tie-Strength refers to the nature of a relational contact
and includes the degree of intimacy, duration, and frequency; the breadth of topic
usually refers to time length, tie depth, emotional intensity, intimacy frequency,
and interactive connection. A collaborative innovative network with a high level of
tie-strength can provide firms with effective information and knowledge, reduce
risk and uncertainty in the innovation process, and achieve successful knowledge
transfer.
4. Network Centrality: Network centrality refers to an actor's position in a network.
Actors centrally located in a network are in an advantageous position to monitor
the flow of information and have the consequent advantage of having large
numbers of contacts who are willing and able to provide them with important
opportunities and resources.
Page 22 of 206

Current Challenges
Collaborative innovation still needs to be empowered. A more collaborative approach
involving stakeholders such as governments, corporations, entrepreneurs and scholars
is key to tackling the main challenges facing today.
First of all, it is still important to raise awareness of CoIN and its benefits among
companies and major economic fields. Policy makers and corporate leaders could
support the development of programs, strategies and educational plans to stimulate
CoINs in specific sectors, benefiting the whole economy.
Second, the overall legal and regulatory framework still needs to evolve to foster crossfirm
collaboration. Fiscal and intellectual property regimes should be reviewed to
provide the necessary infrastructure to nourish CoINs. A further stimulus is important to
encourage the creation of start-ups and the development of a network of partners
across companies.
Finally, financial aid should be granted to support collaborative projects related to
technology, research and innovation. CoINs have an enormous potential to deliver
innovation and drive significant gains in competitiveness. However, they need resources
in order to fully operate and reach their maximum potential.
Future
As COINs become increasingly popular among governments and corporations, the
ethical, financial, economic, and cognitive issues which drive incentives will inevitably
face challenges. Over time potential innovators may be unwilling to participate in
projects merely on the basis of implied financial gain. As globalization begins to impact
Page 23 of 206

traditional models of planned social progress, the broader political context in which
participants cooperate has become more relevant lately. This suggests an increased
need for independent parties to collaborate on the basis of agreed upon principles and
objectives, ultimately this could encompass the interests of humanity and the
emergence of a global culture.
Page 24 of 206

II. Collective Intelligence
Collective Intelligence (CI) is shared or group intelligence that emerges from
the collaboration, collective efforts, and competition of many individuals and appears in
consensus decision making. The term appears in sociobiology, political science and in
context of mass peer review and crowdsourcing applications. It may involve consensus,
social capital and formalisms such as voting systems, social media and other means of
quantifying mass activity. Collective IQ is a measure of collective intelligence, although
it is often used interchangeably with the term collective intelligence. Collective
intelligence has also been attributed to bacteria and animals.
It can be understood as an emergent property from the synergies among: 1) datainformation-knowledge;
2) software-hardware; and 3) experts (those with new insights
as well as recognized authorities) that continually learns from feedback to produce justin-time
knowledge for better decisions than these three elements acting alone. Or more
narrowly as an emergent property between people and ways of processing information.
This notion of collective intelligence is referred to as "symbiotic intelligence" by Norman
Lee Johnson. The concept is used in sociology, business, computer science and mass
communications: it also appears in science fiction. Pierre Lévy defines collective
intelligence as, "It is a form of universally distributed intelligence, constantly enhanced,
coordinated in real time, and resulting in the effective mobilization of skills. I'll add the
following indispensable characteristic to this definition: The basis and goal of collective
intelligence is mutual recognition and enrichment of individuals rather than the cult of
Page 25 of 206

fetishized or hypostatized communities." According to researchers Pierre Lévy and
Derrick de Kerckhove, it refers to capacity of networked ICTs (Information
communication technologies) to enhance the collective pool of social knowledge by
simultaneously expanding the extent of human interactions.
Collective intelligence strongly contributes to the shift of knowledge and power from the
individual to the collective. According to Eric S. Raymond (1998) and JC Herz (2005),
open source intelligence will eventually generate superior outcomes to knowledge
generated by proprietary software developed within corporations (Flew 2008). Media
theorist Henry Jenkins sees collective intelligence as an 'alternative source of media
power', related to convergence culture. He draws attention to education and the way
people are learning to participate in knowledge cultures outside formal learning settings.
Henry Jenkins criticizes schools which promote 'autonomous problem solvers and selfcontained
learners' while remaining hostile to learning through the means of collective
intelligence. Both Pierre Lévy (2007) and Henry Jenkins (2008) support the claim that
collective intelligence is important for democratization, as it is interlinked with
knowledge-based culture and sustained by collective idea sharing, and thus contributes
to a better understanding of diverse society.
Similar to the g factor (g) for general individual intelligence, a new scientific
understanding of collective intelligence aims to extract a general collective intelligence
factor c factor for groups indicating a group's ability to perform a wide range of tasks.
Definition, operationalization and statistical methods are derived from g. Similarly as g is
highly interrelated with the concept of IQ, this measurement of collective intelligence
can be interpreted as intelligence quotient for groups (Group-IQ) even though the score
is not a quotient per se. Causes for c and predictive validity are investigated as well.
Writers who have influenced the idea of collective intelligence include Douglas
Hofstadter (1979), Peter Russell (1983), Tom Atlee (1993), Pierre Lévy (1994), Howard
Bloom (1995), Francis Heylighen (1995), Douglas Engelbart, Louis Rosenberg, Cliff
Joslyn, Ron Dembo, Gottfried Mayer-Kress (2003).
History
The concept (although not so named) originated in 1785 with the Marquis de Condorcet,
whose "jury theorem" states that if each member of a voting group is more likely than
not to make a correct decision, the probability that the highest vote of the group is the
correct decision increases with the number of members of the group (see Condorcet's
jury theorem). Many theorists have interpreted Aristotle's statement in the Politics that
"a feast to which many contribute is better than a dinner provided out of a single purse"
to mean that just as many may bring different dishes to the table, so in a deliberation
many may contribute different pieces of information to generate a better decision.
Recent scholarship, however, suggests that this was probably not what Aristotle meant
but is a modern interpretation based on what we now know about team intelligence.
Page 26 of 206

A precursor of the concept is found in entomologist William Morton Wheeler's
observation that seemingly independent individuals can cooperate so closely as to
become indistinguishable from a single organism (1911). Wheeler saw this collaborative
process at work in ants that acted like the cells of a single beast he called a
superorganism.
In 1912 Émile Durkheim identified society as the sole source of human logical thought.
He argued in "The Elementary Forms of Religious Life" that society constitutes a higher
intelligence because it transcends the individual over space and time. Other
antecedents are Vladimir Vernadsky's concept of "noosphere" and H.G. Wells's concept
of "world brain" (see also the term "global brain"). Peter Russell, Elisabet Sahtouris, and
Barbara Marx Hubbard (originator of the term "conscious evolution") are inspired by the
visions of a noosphere – a transcendent, rapidly evolving collective intelligence – an
informational cortex of the planet. The notion has more recently been examined by the
philosopher Pierre Lévy. In a 1962 research report, Douglas Engelbart linked collective
intelligence to organizational effectiveness, and predicted that pro-actively 'augmenting
human intellect' would yield a multiplier effect in group problem solving: "Three people
working together in this augmented mode [would] seem to be more than three times as
effective in solving a complex problem as is one augmented person working alone". In
1994, he coined the term 'collective IQ' as a measure of collective intelligence, to focus
attention on the opportunity to significantly raise collective IQ in business and society.
The idea of collective intelligence also forms the framework for contemporary
democratic theories often referred to as epistemic democracy. Epistemic democratic
theories refer to the capacity of the populace, either through deliberation or aggregation
Page 27 of 206

of knowledge, to track the truth and relies on mechanisms to synthesize and apply
collective intelligence.
Collective intelligence was introduced into the machine learning community in the late
20th century, and matured into a broader consideration of how to "design collectives" of
self-interested adaptive agents to meet a system-wide goal. This was related to singleagent
work on "reward shaping" and has been taken forward by numerous researchers
in the game theory and engineering communities.
Dimensions
Howard Bloom has discussed mass behavior – collective behavior from the level of
quarks to the level of bacterial, plant, animal, and human societies. He stresses the
biological adaptations that have turned most of this earth's living beings into
components of what he calls "a learning machine". In 1986 Bloom combined the
concepts of apoptosis, parallel distributed processing, group selection, and the
superorganism to produce a theory of how collective intelligence works. Later he
showed how the collective intelligences of competing bacterial colonies and human
societies can be explained in terms of computer-generated "complex adaptive systems"
and the "genetic algorithms", concepts pioneered by John Holland.
Bloom traced the evolution of collective intelligence to our bacterial ancestors 1 billion
years ago and demonstrated how a multi-species intelligence has worked since the
beginning of life. Ant societies exhibit more intelligence, in terms of technology, than any
other animal except for humans and co-operate in keeping livestock, for example aphids
for "milking". Leaf cutters care for fungi and carry leaves to feed the fungi.
David Skrbina cites the concept of a 'group mind' as being derived from Plato's concept
of panpsychism (that mind or consciousness is omnipresent and exists in all matter). He
develops the concept of a 'group mind' as articulated by Thomas Hobbes in "Leviathan"
and Fechner's arguments for a collective consciousness of mankind. He cites Durkheim
as the most notable advocate of a "collective consciousness" and Teilhard de Chardin
as a thinker who has developed the philosophical implications of the group mind.
Tom Atlee focuses primarily on humans and on work to upgrade what Howard Bloom
calls "the group IQ". Atlee feels that collective intelligence can be encouraged "to
overcome 'groupthink' and individual cognitive bias in order to allow a collective to
cooperate on one process – while achieving enhanced intellectual performance."
George Pór defined the collective intelligence phenomenon as "the capacity of human
communities to evolve towards higher order complexity and harmony, through such
innovation mechanisms as differentiation and integration, competition and
collaboration." Atlee and Pór state that "collective intelligence also involves achieving a
single focus of attention and standard of metrics which provide an appropriate threshold
of action". Their approach is rooted in scientific community metaphor.
Page 28 of 206

The term group intelligence is sometimes used interchangeably with the term collective
intelligence. Anita Woolley presents Collective intelligence as a measure of group
intelligence and group creativity. The idea is that a measure of collective intelligence
covers a broad range of features of the group, mainly group composition and group
interaction. The features of composition that lead to increased levels of collective
intelligence in groups include criteria such as higher numbers of women in the group as
well as increased diversity of the group.
Atlee and Pór suggest that the field of collective intelligence should primarily be seen as
a human enterprise in which mind-sets, a willingness to share and an openness to the
value of distributed intelligence for the common good are paramount, though group
theory and artificial intelligence have something to offer. Individuals who respect
collective intelligence are confident of their own abilities and recognize that the whole is
indeed greater than the sum of any individual parts. Maximizing collective intelligence
relies on the ability of an organization to accept and develop "The Golden Suggestion",
which is any potentially useful input from any member. Groupthink often hampers
collective intelligence by limiting input to a select few individuals or filtering potential
Golden Suggestions without fully developing them to implementation.
Robert David Steele Vivas in The New Craft of Intelligence portrayed all citizens as
"intelligence minutemen," drawing only on legal and ethical sources of information, able
to create a "public intelligence" that keeps public officials and corporate managers
honest, turning the concept of "national intelligence" (previously concerned about spies
and secrecy) on its head.
According to Don Tapscott and Anthony D. Williams, collective intelligence is mass
collaboration. In order for this concept to happen, four principles need to exist;
Openness
Page 29 of 206

Sharing ideas and intellectual property: though these resources provide the edge over
competitors more benefits accrue from allowing others to share ideas and gain
significant improvement and scrutiny through collaboration.
Peering
Horizontal organization as with the 'opening up' of the Linux program where users are
free to modify and develop it provided that they make it available for others. Peering
succeeds because it encourages self-organization – a style of production that works
more effectively than hierarchical management for certain tasks.
Sharing
Companies have started to share some ideas while maintaining some degree of control
over others, like potential and critical patent rights. Limiting all intellectual property shuts
out opportunities, while sharing some expands markets and brings out products faster.
Acting Globally
The advancement in communication technology has prompted the rise of global
companies at low overhead costs. The internet is widespread, therefore a globally
integrated company has no geographical boundaries and may access new markets,
ideas and technology.
Collective Intelligence Factor C
A new scientific understanding of collective intelligence defines it as a group's general
ability to perform a wide range of tasks. Definition, operationalization and statistical
methods are similar to the psychometric approach of general individual intelligence.
Hereby, an individual's performance on a given set of cognitive tasks is used to
measure general cognitive ability indicated by the general intelligence factor g extracted
via factor analysis. In the same vein as g serves to display between-individual
performance differences on cognitive tasks, collective intelligence research aims to find
a parallel intelligence factor for groups 'c factor' (also called 'collective intelligence
factor' (CI)) displaying between-group differences on task performance. The collective
intelligence score then is used to predict how this same group will perform on any other
similar task in the future. Yet tasks, hereby, refer to mental or intellectual tasks
performed by small groups even though the concept is hoped to be transferrable to
other performances and any groups or crowds reaching from families to companies and
even whole cities. Since individuals' g factor scores are highly correlated with full-scale
IQ scores, which are in turn regarded as good estimates of g, this measurement of
collective intelligence can also be seen as an intelligence indicator or quotient
respectively for a group (Group-IQ) parallel to an individual's intelligence quotient (IQ)
even though the score is not a quotient per se.
Page 30 of 206

Mathematically, c and g are both variables summarizing positive correlations among
different tasks supposing that performance on one task is comparable with performance
on other similar tasks. c thus is a source of variance among groups and can only be
considered as a group's standing on the c factor compared to other groups in a given
relevant population. The concept is in contrast to competing hypotheses including other
correlational structures to explain group intelligence, such as a composition out of
several equally important but independent factors as found in individual personality
research.
Besides, this scientific idea also aims to explore the causes affecting collective
intelligence, such as group size, collaboration tools or group members' interpersonal
skills. The MIT Center for Collective Intelligence, for instance, announced the detection
of The Genome of Collective Intelligence as one of its main goals aiming to develop a
taxonomy of organizational building blocks, or genes, that can be combined and
recombined to harness the intelligence of crowds.
Causes
Individual intelligence is shown to be genetically and environmentally influenced.
Analogously, collective intelligence research aims to explore reasons why certain
groups perform more intelligent than other groups given that c is just moderately
correlated with the intelligence of individual group members. According to Woolley et
al.'s results, neither team cohesion nor motivation or satisfaction correlated with c.
However, they claim that three factors were found as significant correlates: the variance
in the number of speaking turns, group members' average social sensitivity and the
proportion of females. All three had similar predictive power for c, but only social
sensitivity was statistically significant (b=0.33, P=0.05).
The number speaking turns indicates that "groups where a few people dominated the
conversation were less collectively intelligent than those with a more equal distribution
Page 31 of 206

of conversational turn-taking". Hence, providing multiple team members the chance to
speak up made a group more intelligent.
Group members' social sensitivity was measured via the Reading the Mind in the Eyes
Test (RME) and correlated .26 with c. Hereby, participants are asked to detect thinking
or feeling expressed in other peoples' eyes presented on pictures and assessed in a
multiple choice format. The test aims to measure peoples' theory of mind (ToM), also
called 'mentalizing' or 'mind reading', which refers to the ability to attribute mental
states, such as beliefs, desires or intents, to other people and in how far people
understand that others have beliefs, desires, intentions or perspectives different from
their own ones. RME is a ToM test for adults that shows sufficient test-retest reliability
and constantly differentiates control groups from individuals with functional autism or
Asperger Syndrome. It is one of the most widely accepted and well-validated tests for
ToM within adults. ToM can be regarded as an associated subset of skills and abilities
within the broader concept of emotional intelligence.
The proportion of females as a predictor of c was largely mediated by social
sensitivity (Sobel z = 1.93, P= 0.03) which is in vein with previous research showing
that women score higher on social sensitivity tests. While a mediation, statistically
speaking, clarifies the mechanism underlying the relationship between a dependent and
an independent variable, Wolley agreed in an interview with the Harvard Business
Review that these findings are saying that groups of women are smarter than
groups of men. However, she relativizes this stating that the actual important thing is
the high social sensitivity of group members.
It is theorized that the collective intelligence factor c is an emergent property resulting
from bottom-up as well as top-down processes. Hereby, bottom-up processes cover
aggregated group-member characteristics. Top-down processes cover group structures
and norms that influence a group's way of collaborating and coordinating.
Processes
Top-Down Processes
Top-down processes cover group interaction, such as structures, processes, and
norms. An example of such top-down processes is conversational turn-taking. Research
further suggest that collectively intelligent groups communicate more in general as well
as more equally; same applies for participation and is shown for face-to-face as well as
online groups communicating only via writing.
Bottom-Up Processes
Bottom-up processes include group composition, namely the characteristics of group
members which are aggregated to the team level encompassing. An example of such
bottom-up processes is the average social sensitivity or the average and maximum
intelligence scores of group members. Furthermore, collective intelligence was found to
be related to a group's cognitive diversity including thinking styles and perspectives.
Page 32 of 206

Groups that are moderately diverse in cognitive style have higher collective intelligence
than those who are very similar in cognitive style or very different. Consequently, groups
where members are too similar to each other lack the variety of perspectives and skills
needed to perform well. On the other hand, groups whose members are too different
seem to have difficulties to communicate and coordinate effectively.
Serial vs Parallel Processes
For most of human history, collective intelligence was confined to small tribal groups in
which opinions were aggregated through real-time parallel interactions among
members. In modern times, mass communication, mass media, and networking
technologies have enabled collective intelligence to span massive groups, distributed
across continents and time-zones. To accommodate this shift in scale, collective
intelligence in large-scale groups been dominated by serialized polling processes such
as aggregating up-votes, likes, and ratings over time. While modern systems benefit
from larger group size, the serialized process has been found to introduce substantial
noise that distorts the collective output of the group. In one significant study of serialized
Page 33 of 206

collective intelligence, it was found that the first vote contributed to a serialized voting
system can distort the final result by 34%.
To address the problems of serialized aggregation of input among large-scale groups,
recent advancements collective intelligence have worked to replace serialized votes,
polls, and markets, with parallel systems such as "human swarms" modeled after
synchronous swarms in nature. Based on natural process of Swarm Intelligence, these
artificial swarms of networked humans enable participants to work together in parallel to
answer questions and make predictions as an emergent collective intelligence. In one
high-profile example, a human swarm challenge by CBS Interactive to predict the
Kentucky Derby. The swarm correctly predicted the first four horses, in order, defying
542–1 odds and turning a $20 bet into $10,800.
Evidence
Woolley, Chabris, Pentland, Hashmi, & Malone (2010), the originators of this scientific
understanding of collective intelligence, found a single statistical factor for collective
intelligence in their research across 192 groups with people randomly recruited from the
public. In Woolley et al.'s two initial studies, groups worked together on different tasks
from the McGrath Task Circumplex, a well-established taxonomy of group tasks. Tasks
were chosen from all four quadrants of the circumplex and included visual puzzles,
brainstorming, making collective moral judgments, and negotiating over limited
resources. The results in these tasks were taken to conduct a factor analysis. Both
studies showed support for a general collective intelligence factor c underlying
differences in group performance with an initial eigenvalue accounting for 43% (44% in
study 2) of the variance, whereas the next factor accounted for only 18% (20%). That
fits the range normally found in research regarding a general individual intelligence
factor g typically accounting for 40% to 50% percent of between-individual performance
differences on cognitive tests. Afterwards, a more complex criterion task was absolved
by each group measuring whether the extracted c factor had predictive power for
performance outside the original task batteries. Criterion tasks were playing checkers
(draughts) against a standardized computer in the first and a complex architectural
design task in the second study. In a regression analysis using both individual
intelligence of group members and c to predict performance on the criterion tasks, c had
a significant effect, but average and maximum individual intelligence had not. While
average (r=0.15, P=0.04) and maximum intelligence (r=0.19, P=0.008) of individual
group members were moderately correlated with c, c was still a much better predictor of
the criterion tasks. According to Woolley et al., this supports the existence of a collective
intelligence factor c, because it demonstrates an effect over and beyond group
members' individual intelligence and thus that c is more than just the aggregation of the
individual IQs or the influence of the group member with the highest IQ.
Engel et al. (2014) replicated Woolley et al.'s findings applying an accelerated battery of
tasks with a first factor in the factor analysis explaining 49% of the between-group
variance in performance with the following factors explaining less than half of this
amount. Moreover, they found a similar result for groups working together online
Page 34 of 206

communicating only via text and confirmed the role of female proportion and social
sensitivity in causing collective intelligence in both cases. Similarly to Wolley et al., they
also measured social sensitivity with the RME which is actually meant to measure
people's ability to detect mental states in other peoples' eyes. The online collaborating
participants, however, did neither know nor see each other at all. The authors conclude
that scores on the RME must be related to a broader set of abilities of social reasoning
than only drawing inferences from other people's eye expressions.
A collective intelligence factor c in the sense of Woolley et al. was further found in
groups of MBA students working together over the course of a semester, in online
gaming groups as well as in groups from different cultures and groups in different
contexts in terms of short-term versus long-term groups. None of these investigations
considered team members' individual intelligence scores as control variables.
Note as well that the field of collective intelligence research is quite young and
published empirical evidence is relatively rare yet. However, various proposals and
working papers are in progress or already completed but (supposedly) still in a scholarly
peer reviewing publication process.
Predictive Validity
Next to predicting a group's performance on more complex criterion tasks as shown in
the original experiments, the collective intelligence factor c was also found to predict
group performance in diverse tasks in MBA classes lasting over several months.
Thereby, highly collectively intelligent groups earned significantly higher scores on their
group assignments although their members did not do any better on other individually
Page 35 of 206

performed assignments. Moreover, highly collective intelligent teams improved
performance over time suggesting that more collectively intelligent teams learn better.
This is another potential parallel to individual intelligence where more intelligent people
are found to acquire new material quicker.
Individual intelligence can be used to predict plenty of life outcomes from school
attainment and career success to health outcomes and even mortality. Whether
collective intelligence is able to predict other outcomes besides group performance on
mental tasks has still to be investigated.
Potential Connections to Individual Intelligence
Gladwell (2008) showed that the relationship between individual IQ and success works
only to a certain point and that additional IQ points over an estimate of IQ 120 do not
translate into real life advantages. If a similar border exists for Group-IQ or if
advantages are linear and infinite, has still to be explored. Similarly, demand for further
research on possible connections of individual and collective intelligence exists within
plenty of other potentially transferable logics of individual intelligence, such as, for
instance, the development over time or the question of improving intelligence. Whereas
it is controversial whether human intelligence can be enhanced via training, a group's
collective intelligence potentially offers simpler opportunities for improvement by
exchanging team members or implementing structures and technologies. Moreover,
social sensitivity was found to be, at least temporarily, improvable by reading literary
fiction as well as watching drama movies. In how far such training ultimately improves
collective intelligence through social sensitivity remains an open question.
There are further more advanced concepts and factor models attempting to explain
individual cognitive ability including the categorization of intelligence in fluid and
crystallized intelligence or the hierarchical model of intelligence differences. Further
supplementing explanations and conceptualizations for the factor structure of the
Genomes' of collective intelligence besides a general c factor', though, are missing
yet.
Controversies
Other scholars explain team performance by aggregating team members' general
intelligence to the team level instead of building an own overall collective intelligence
measure. Devine and Philips (2001) showed in a meta-analysis that mean cognitive
ability predicts team performance in laboratory settings (.37) as well as field settings
(.14) – note that this is only a small effect. Suggesting a strong dependence on the
relevant tasks, other scholars showed that tasks requiring a high degree of
communication and cooperation are found to be most influenced by the team member
with the lowest cognitive ability. Tasks in which selecting the best team member is the
most successful strategy, are shown to be most influenced by the member with the
highest cognitive ability.
Page 36 of 206

Since Woolley et al.'s results do not show any influence of group satisfaction, group
cohesiveness, or motivation, they, at least implicitly, challenge these concepts regarding
the importance for group performance in general and thus contrast meta-analytically
proven evidence concerning the positive effects of group cohesion, motivation and
satisfaction on group performance.
Noteworthy is also that the involved researchers among the confirming findings widely
overlap with each other and with the authors participating in the original first study
around Anita Woolley.
Computational Collective Intelligence
Alternative Mathematical Techniques
In 2001, Tadeusz (Tad) Szuba from the AGH University in Poland proposed a formal
model for the phenomenon of collective intelligence. It is assumed to be an
unconscious, random, parallel, and distributed computational process, run in
mathematical logic by the social structure.
In this model, beings and information are modeled as abstract information molecules
carrying expressions of mathematical logic. They are quasi-randomly displacing due to
their interaction with their environments with their intended displacements. Their
interaction in abstract computational space creates multi-thread inference process
which we perceive as collective intelligence. Thus, a non-Turing model of computation
is used. This theory allows simple formal definition of collective intelligence as the
Page 37 of 206

property of social structure and seems to be working well for a wide spectrum of beings,
from bacterial colonies up to human social structures. Collective intelligence considered
as a specific computational process is providing a straightforward explanation of several
social phenomena. For this model of collective intelligence, the formal definition of IQS
(IQ Social) was proposed and was defined as "the probability function over the time and
domain of N-element inferences which are reflecting inference activity of the social
structure".
While IQS seems to be computationally hard, modeling of social structure in terms of a
computational process as described above gives a chance for approximation.
Prospective applications are optimization of companies through the maximization of
their IQS, and the analysis of drug resistance against collective intelligence of bacterial
colonies.
Collective Intelligence Quotient
One measure sometimes applied, especially by more artificial intelligence focused
theorists, is a "collective intelligence quotient" (or "cooperation quotient") – which can be
normalized from the "individual" intelligence quotient (IQ) – thus making it possible to
determine the marginal intelligence added by each new individual participating in the
collective action, thus using metrics to avoid the hazards of group think and stupidity.
Applications
Elicitation of point estimates – Here, we try to get an estimate (in a single value) of
something. For example, estimating the weight of an object, or the release date of a
product or probability of success of a project etc. as are seen in prediction markets like
Intrade, HSX or InklingMarkets and also in several implementations of crowdsourced
estimation of a numeric outcome. Essentially, we try to get the average value of the
estimates provided by the members in the crowd.
Opinion Aggregation – In this situation, we gather opinions from the crowd regarding
some idea, issue or product. For example, trying to get a rating (on some scale) of a
product sold online (such as Amazon’s star rating system). Here, the emphasis is to
collect and simply aggregate the ratings provided by customers/users.
Idea Collection – In these problems, someone solicits ideas for projects, designs or
solutions from the crowd. For example, ideas on solving a data science problem (as in
Kaggle) or getting a good design for a T-shirt (as in Threadless) or in getting answers to
simple problems that only humans can do well (as in Amazon’s Mechanical Turk). Here,
the objective is to gather the ideas and devise some selection criteria to choose the best
ideas.
James Surowiecki divides the advantages of disorganized decision-making into three
main categories, which are cognition, cooperation and coordination.
Page 38 of 206

Cognition
Market Judgment
Because of the Internet's ability to rapidly convey large amounts of information
throughout the world, the use of collective intelligence to predict stock prices and stock
price direction has become increasingly viable. Websites aggregate stock market
information that is as current as possible so professional or amateur stock analysts can
publish their viewpoints, enabling amateur investors to submit their financial opinions
and create an aggregate opinion.
The opinion of all investor can be weighed equally so that a pivotal premise of the
effective application of collective intelligence can be applied: the masses, including a
broad spectrum of stock market expertise, can be utilized to more accurately predict the
behavior of financial markets.
Collective intelligence underpins the efficient-market hypothesis of Eugene Fama –
although the term collective intelligence is not used explicitly in his paper. Fama cites
research conducted by Michael Jensen in which 89 out of 115 selected funds
underperformed relative to the index during the period from 1955 to 1964. But after
removing the loading charge (up-front fee) only 72 underperformed while after removing
brokerage costs only 58 underperformed.
On the basis of such evidence index funds became popular investment vehicles using
the collective intelligence of the market, rather than the judgement of professional fund
managers, as an investment strategy.
Page 39 of 206

Predictions in Politics and Technology
Political parties mobilize large numbers of people to form policy, select candidates and
finance and run election campaigns. Knowledge focusing through various voting
methods allows perspectives to converge through the assumption that uninformed
voting is to some degree random and can be filtered from the decision process leaving
only a residue of informed consensus. Critics point out that often bad ideas,
misunderstandings, and misconceptions are widely held, and that structuring of the
decision process must favor experts who are presumably less prone to random or
misinformed voting in a given context.
Companies such as Affinnova (acquired by Nielsen), Google, InnoCentive,
Marketocracy, and Threadless have successfully employed the concept of collective
intelligence in bringing about the next generation of technological changes through their
research and development (R&D), customer service, and knowledge management. An
example of such application is Google's Project Aristotle in 2012, where the effect of
collective intelligence on team makeup was examined in hundreds of the company's
R&D teams.
Networks of Trust
Cooperation
In 2012, the Global Futures Collective Intelligence System (GFIS) was created by The
Millennium Project, which epitomizes collective intelligence as the synergistic
intersection among data/information/knowledge, software/hardware, and
expertise/insights that has a recursive learning process for better decision-making than
the individual players alone.
New media are often associated with the promotion and enhancement of collective
intelligence. The ability of new media to easily store and retrieve information,
predominantly through databases and the Internet, allows for it to be shared without
difficulty. Thus, through interaction with new media, knowledge easily passes between
sources (Flew 2008) resulting in a form of collective intelligence. The use of interactive
new media, particularly the internet, promotes online interaction and this distribution of
knowledge between users.
Francis Heylighen, Valentin Turchin, and Gottfried Mayer-Kress are among those who
view collective intelligence through the lens of computer science and cybernetics. In
their view, the Internet enables collective intelligence at the widest, planetary scale, thus
facilitating the emergence of a global brain.
The developer of the World Wide Web, Tim Berners-Lee, aimed to promote sharing and
publishing of information globally. Later his employer opened up the technology for free
use. In the early '90s, the Internet's potential was still untapped, until the mid-1990s
when 'critical mass', as termed by the head of the Advanced Research Project Agency
Page 40 of 206

(ARPA), Dr. J.C.R. Licklider, demanded more accessibility and utility. The driving force
of this Internet-based collective intelligence is the digitization of information and
communication. Henry Jenkins, a key theorist of new media and media convergence
draws on the theory that collective intelligence can be attributed to media convergence
and participatory culture (Flew 2008). He criticizes contemporary education for failing to
incorporate online trends of collective problem solving into the classroom, stating
"whereas a collective intelligence community encourages ownership of work as a group,
schools grade individuals". Jenkins argues that interaction within a knowledge
community builds vital skills for young people, and teamwork through collective
intelligence communities contribute to the development of such skills. Collective
intelligence is not merely a quantitative contribution of information from all cultures, it is
also qualitative.
Lévy and de Kerckhove consider CI from a mass communications perspective, focusing
on the ability of networked information and communication technologies to enhance the
community knowledge pool. They suggest that these communications tools enable
humans to interact and to share and collaborate with both ease and speed (Flew 2008).
With the development of the Internet and its widespread use, the opportunity to
contribute to knowledge-building communities, such as Wikipedia, is greater than ever
before. These computer networks give participating users the opportunity to store and to
retrieve knowledge through the collective access to these databases and allow them to
"harness the hive" Researchers at the MIT Center for Collective Intelligence research
and explore collective intelligence of groups of people and computers.
In this context collective intelligence is often confused with shared knowledge. The
former is the sum total of information held individually by members of a community while
the latter is information that is believed to be true and known by all members of the
community. Collective intelligence as represented by Web 2.0 has less user
Page 41 of 206

engagement than collaborative intelligence. An art project using Web 2.0 platforms is
"Shared Galaxy", an experiment developed by an anonymous artist to create a
collective identity that shows up as one person on several platforms like MySpace,
Facebook, YouTube and Second Life. The password is written in the profiles and the
accounts named "Shared Galaxy" are open to be used by anyone. In this way many
take part in being one. Another art project using collective intelligence to produce artistic
work is Curatron, where a large group of artists together decides on a smaller group that
they think would make a good collaborative group. The process is used based on an
algorithm computing the collective preferences In creating what he calls 'CI-Art', Nova
Scotia based artist Mathew Aldred follows Pierry Lévy's definition of collective
intelligence. Aldred's CI-Art event in March 2016 involved over four hundred people
from the community of Oxford, Nova Scotia, and internationally. Later work developed
by Aldred used the UNU swarm intelligence system to create digital drawings and
paintings. The Oxford Riverside Gallery (Nova Scotia) held a public CI-Art event in May
2016, which connected with online participants internationally.
In social bookmarking (also called collaborative tagging), users assign tags to resources
shared with other users, which gives rise to a type of information organisation that
emerges from this crowdsourcing process. The resulting information structure can be
seen as reflecting the collective knowledge (or collective intelligence) of a community of
users and is commonly called a "Folksonomy", and the process can be captured by
models of collaborative tagging.
Recent research using data from the social bookmarking website Delicious, has shown
that collaborative tagging systems exhibit a form of complex systems (or selforganizing)
dynamics. Although there is no central controlled vocabulary to constrain
the actions of individual users, the distributions of tags that describe different resources
has been shown to converge over time to a stable power law distributions. Once such
stable distributions form, examining the correlations between different tags can be used
to construct simple folksonomy graphs, which can be efficiently partitioned to obtained a
form of community or shared vocabularies. Such vocabularies can be seen as a form of
collective intelligence, emerging from the decentralised actions of a community of users.
The Wall-it Project is also an example of social bookmarking.
P2P Business
Research performed by Tapscott and Williams has provided a few examples of the
benefits of collective intelligence to business:
Talent Utilization
At the rate technology is changing, no firm can fully keep up in the innovations needed
to compete. Instead, smart firms are drawing on the power of mass collaboration to
involve participation of the people they could not employ. This also helps generate
continual interest in the firm in the form of those drawn to new idea creation as well as
investment opportunities.
Page 42 of 206

Demand Creation
Firms can create a new market for complementary goods by engaging in open source
community. Firms also are able to expand into new fields that they previously would not
have been able to without the addition of resources and collaboration from the
community. This creates, as mentioned before, a new market for complementary goods
for the products in said new fields.
Costs Reduction
Mass collaboration can help to reduce costs dramatically. Firms can release a specific
software or product to be evaluated or debugged by online communities. The results will
be more personal, robust and error-free products created in a short amount of time and
costs. New ideas can also be generated and explored by collaboration of online
communities creating opportunities for free R&D outside the confines of the company.
Open Source Software
Cultural theorist and online community developer, John Banks considered the
contribution of online fan communities in the creation of the Trainz product. He argued
that its commercial success was fundamentally dependent upon "the formation and
growth of an active and vibrant online fan community that would both actively promote
the product and create content- extensions and additions to the game software".
The increase in user created content and interactivity gives rise to issues of control over
the game itself and ownership of the player-created content. This gives rise to
Page 43 of 206

fundamental legal issues, highlighted by Lessig and Bray and Konsynski, such as
intellectual property and property ownership rights.
Gosney extends this issue of Collective Intelligence in videogames one step further in
his discussion of alternate reality gaming. This genre, he describes as an "across-media
game that deliberately blurs the line between the in-game and out-of-game
experiences" as events that happen outside the game reality "reach out" into the
player's lives in order to bring them together. Solving the game requires "the collective
and collaborative efforts of multiple players"; thus the issue of collective and
collaborative team play is essential to ARG. Gosney argues that the Alternate Reality
genre of gaming dictates an unprecedented level of collaboration and "collective
intelligence" in order to solve the mystery of the game.
Benefits of Co-Operation
Co-operation helps to solve most important and most interesting multi-science
problems. In his book, James Surowiecki mentioned that most scientists think that
benefits of co-operation have much more value when compared to potential costs. Cooperation
works also because at best it guarantees number of different viewpoints.
Because of the possibilities of technology global co-operation is nowadays much easier
and productive than before. It is clear that, when co-operation goes from university level
to global it has significant benefits.
For example, why do scientists co-operate? Science has become more and more
isolated and each science field has spread even more and it is impossible for one
person to be aware of all developments. This is true especially in experimental research
where highly advanced equipment requires special skills. With co-operation scientists
can use information from different fields and use it effectively instead of gathering all the
information just by reading by themselves."
Ad-Hoc Communities
Coordination
Military, trade unions, and corporations satisfy some definitions of CI – the most
rigorous definition would require a capacity to respond to very arbitrary conditions
without orders or guidance from "law" or "customers" to constrain actions. Online
advertising companies are using collective intelligence to bypass traditional marketing
and creative agencies.
The UNU open platform for "human swarming" (or "social swarming") establishes realtime
closed-loop systems around groups of networked users molded after biological
swarms, enabling human participants to behave as a unified collective intelligence.
When connected to UNU, groups of distributed users collectively answer questions and
make predictions in real-time. Early testing shows that human swarms can out-predict
individuals. In 2016, an UNU swarm was challenged by a reporter to predict the winners
Page 44 of 206

of the Kentucky Derby, and successfully picked the first four horses, in order, beating
540 to 1 odds.
Specialized information sites such as Digital Photography Review or Camera Labs is an
example of collective intelligence. Anyone who has an access to the internet can
contribute to distributing their knowledge over the world through the specialized
information sites.
In learner-generated context a group of users marshal resources to create an ecology
that meets their needs often (but not only) in relation to the co-configuration, co-creation
and co-design of a particular learning space that allows learners to create their own
context. Learner-generated contexts represent an ad hoc community that facilitates
coordination of collective action in a network of trust. An example of learner-generated
context is found on the Internet when collaborative users pool knowledge in a "shared
intelligence space". As the Internet has developed so has the concept of CI as a shared
public forum. The global accessibility and availability of the Internet has allowed more
people than ever to contribute and access ideas. (Flew 2008)
Games such as The Sims Series, and Second Life are designed to be non-linear and to
depend on collective intelligence for expansion. This way of sharing is gradually
evolving and influencing the mindset of the current and future generations. For them,
collective intelligence has become a norm. In Terry Flew's discussion of 'interactivity' in
the online games environment, the ongoing interactive dialogue between users and
game developers, he refers to Pierre Lévy's concept of Collective Intelligence (Lévy
1998) and argues this is active in videogames as clans or guilds in MMORPG
constantly work to achieve goals. Henry Jenkins proposes that the participatory cultures
emerging between games producers, media companies, and the end-users mark a
fundamental shift in the nature of media production and consumption. Jenkins argues
that this new participatory culture arises at the intersection of three broad new media
trends. Firstly, the development of new media tools/technologies enabling the creation
of content. Secondly, the rise of subcultures promoting such creations, and lastly, the
growth of value adding media conglomerates, which foster image, idea and narrative
flow.
Page 45 of 206

Coordinating Collective Actions
Improvisational actors also experience a type of collective intelligence which they term
"group mind", as theatrical improvisation relies on mutual cooperation and agreement,
leading to the unity of "group mind".
Growth of the Internet and mobile telecom has also produced "swarming" or
"rendezvous" events that enable meetings or even dates on demand. The full impact
has yet to be felt but the anti-globalization movement, for example, relies heavily on e-
mail, cell phones, pagers, SMS and other means of organizing. The Indymedia
organization does this in a more journalistic way. Such resources could combine into a
form of collective intelligence accountable only to the current participants yet with some
strong moral or linguistic guidance from generations of contributors – or even take on a
more obviously democratic form to advance shared goal.
A further application of collective intelligence is found in the "Community Engineering for
Innovations". In such an integrated framework proposed by Ebner et al., idea
competitions and virtual communities are combined to better realize the potential of the
collective intelligence of the participants, particularly in open-source R&D.
Coordination in Different Types of Tasks
Collective actions or tasks require different amounts of coordination depending on the
complexity of the task. Tasks vary from being highly independent simple tasks that
require very little coordination to complex interdependent tasks that are built by many
individuals and require a lot of coordination. In the article written by Kittur, Lee and
Kraut the writers introduce a problem in cooperation: "When tasks require high
coordination because the work is highly interdependent, having more contributors can
increase process losses, reducing the effectiveness of the group below what individual
members could optimally accomplish". Having a team too large the overall effectiveness
may suffer even when the extra contributors increase the resources. In the end the
overall costs from coordination might overwhelm other costs.
Group collective intelligence is a property that emerges through coordination from both
bottom-up and top-down processes. In a bottom-up process the different characteristics
of each member are involved in contributing and enhancing coordination. Top-down
processes are more strict and fixed with norms, group structures and routines that in
their own way enhance the group's collective work.
A Tool for Combating Self-Preservation
Alternative Views
Tom Atlee reflects that, although humans have an innate ability to gather and analyze
data, they are affected by culture, education and social institutions. A single person
Page 46 of 206

tends to make decisions motivated by self-preservation. Therefore, without collective
intelligence, humans may drive themselves into extinction based on their selfish needs.
Separation from IQism
Phillip Brown and Hugh Lauder quotes Bowles and Gintis (1976) that in order to truly
define collective intelligence, it is crucial to separate 'intelligence' from IQism. They go
on to argue that intelligence is an achievement and can only be developed if allowed to.
For example, earlier on, groups from the lower levels of society are severely restricted
from aggregating and pooling their intelligence. This is because the elites fear that the
collective intelligence would convince the people to rebel. If there is no such capacity
and relations, there would be no infrastructure on which collective intelligence is built
(Brown & Lauder 2000, p. 230). This reflects how powerful collective intelligence can be
if left to develop.
Artificial Intelligence Views
Skeptics, especially those critical of artificial intelligence and more inclined to believe
that risk of bodily harm and bodily action are the basis of all unity between people, are
more likely to emphasize the capacity of a group to take action and withstand harm as
one fluid mass mobilization, shrugging off harms the way a body shrugs off the loss of a
few cells. This strain of thought is most obvious in the anti-globalization movement and
characterized by the works of John Zerzan, Carol Moore, and Starhawk, who typically
Page 47 of 206

shun academics. These theorists are more likely to refer to ecological and collective
wisdom and to the role of consensus process in making ontological distinctions than to
any form of "intelligence" as such, which they often argue does not exist, or is mere
"cleverness".
Harsh critics of artificial intelligence on ethical grounds are likely to promote collective
wisdom-building methods, such as the new tribalists and the Gaians. Whether these
can be said to be collective intelligence systems is an open question. Some, e.g. Bill
Joy, simply wish to avoid any form of autonomous artificial intelligence and seem willing
to work on rigorous collective intelligence in order to remove any possible niche for AI.
In contrast to these views, Artificial Intelligence companies such as Amazon Mechanical
Turk and CrowdFlower are using collective intelligence and crowdsourcing or
consensus-based assessment to collect the enormous amounts of data for machine
learning algorithms such as Keras and IBM Watson.
Solving Climate Change
Global collective intelligence is also seen as the key in solving the challenges that the
humankind faces now and in the future. Climate change is an example of a global issue
which collective intelligence is currently trying to tackle. With the help of collective
intelligence applications such as online crowdsourcing people across the globe are
collaborating in developing solutions to climate change.
Page 48 of 206

III. Digital Collaboration
Digital Collaboration is using digital technologies for collaboration. Dramatically
different from traditional collaboration, it connects a broader network of participants who
can accomplish much more than they would on their own.
Examples
Online meetings and webinar
Co-authoring documents and shared spreadsheets
Mind maps
Social media
Shared task lists or issue tracking systems
Wikis
Background
21st century mobile devices such as apps, social media, bandwidth and open data,
connect people on a global level. This has led to an increase in information and at the
same time increased levels of stress.
As a result, workplace innovators and visionaries want to discover new digital tools and
are rethinking how, when and where they work.
Page 49 of 206

Processes
E-mail
A collaborative system through electronic devices which allows users to exchange
messages and information online by way of computer, tablet, or smartphone. Users
develop accounts and use E-mail for work and leisure related topics. A great reliance is
placed on e-mail to communicate, gone are the days when a message can go unread.
Adapting digital tools such as notetaking apps, task lists and ical to David Allen's
Getting Things Done (GTD) productivity workflow, users can find "weird time", to
process the e-mail in box. GTD principles can be difficult to maintain over the long term.
Examples of providers for e-mail are Gmail, Comcast, and Outlook.
Social media
Social Media networks foster collaboration as well as manage and share knowledge
between peers and interested groups. Participation in these networks builds trust
among peers which leads to open sharing of ideas. News and information can be
activity filtered through subscription allowing users to focus on what interests them, as
opposed to passively receiving information. Events, activities, files and discussions are
searchable and presented as a timeline. Platforms such as Facebook, Twitter, and
Instagram bring users together by connecting them on the internet.
Open data sources
Applications that can deliver data to help make decisions. Public agencies and GIS
services provide, what was once thought of as proprietary data, to the private sector
developers to present useful context and decision making. People themselves can also
provide data about their location or experience which has social value to interested
users.
Wikis
Wikis are websites which allow collaborative modification of its content and structure
directly from the web browser. In a typical wiki, text is written using a simplified markup
language (known as "wiki markup"), and often edited with the help of a rich-text editor. A
wiki is run using wiki software, otherwise known as a wiki engine. There are dozens of
different wiki engines in use, both standalone and part of other software, such as bug
tracking systems. Some wiki engines are open source, whereas others are proprietary.
Identity and adoption
Innovators and visionaries of both Generations X and Y are leading the mainstream
pragmatist to digitally collaborative tools. The Net Generation is growing up with digital
collaborative tools such as Wikipedia, Twitter, Facebook, Flipboard and Pinterest,
building trust among peers and openness in their on-line communities. Influenced by
cautious optimism about employment, post turbulent 2008 economy, and trust among
Page 50 of 206

peers this generation will culturally tend to share and sustain resources. These factors
contribute to increased adoption of digitally collaborative tools and active participation
over the previous Generation X.
Cloud Collaboration
Cloud Collaboration is a way of sharing and co-authoring computer files through the
use of cloud computing, whereby documents are uploaded to a central "cloud" for
storage, where they can then be accessed by others. Cloud collaboration technologies
allow users to upload, comment and collaborate on documents and even amend the
document itself, evolving the document. Businesses in the last few years have
increasingly been switching to use of cloud collaboration.
Overview
Cloud computing is a marketing term for technologies that provide software, data
access, and storage services that do not require end-user knowledge of the physical
location and configuration of the system that delivers the services. A parallel to this
concept can be drawn with the electricity grid, where end-users consume power without
needing to understand the component devices or infrastructure required to utilize the
technology.
Collaboration refers to the ability of workers to work together simultaneously on a
particular task. Document collaboration can be completed face to face. However,
collaboration has become more complex, with the need to work with people all over the
world in real time on a variety of different types of documents, using different devices. A
2003 report mapped out five reasons why workers are reluctant to collaborate more.
Page 51 of 206

These are:
People resist sharing their knowledge.
Safety issues
Users are most comfortable using e-mail as their primary electronic collaboration
tool.
People do not have incentive to change their behaviour.
Teams that want to or are selected to use the software do not have strong team
leaders who push for more collaboration.
Senior management is not actively involved in or does not support the team
collaboration initiative.
As a result, many providers created cloud collaboration tools. These include the
integration of email alerts into collaboration software and the ability to see who is
viewing the document at any time. All the tools a team could need are put into one piece
of software so workers no longer have to rely on email.
Origins
Before cloud file sharing and collaboration software, most collaboration was limited to
more primitive and less effective methods such as email and FTP among others. These
did not work particularly well.
Very early moves into cloud computing were made by Amazon Web Services who, in
2006, began offering IT infrastructure services to businesses in the form of web
services. Cloud computing only began to come to prominence in 2007 when Google
decided to move parts of its email service to a public cloud. It was not long before IBM
and Microsoft followed suit with LotusLive and Business Productivity Online Standard
Suite (BPOS) respectively. With an increase in cloud computing services, cloud
collaboration was able to evolve. Since 2007, many firms entered the industry offering
many features.
Many analysts explain the rise of cloud collaboration by pointing to the increasing use
by workers of non-authorised websites and online tools to do their jobs. This includes
the use of instant messaging and social networks. In a survey taken in early 2011, 22%
of workers admitted to having used one or more of these external non-authorised
websites. Cloud collaboration packages provide the ability to collaborate on documents
together in real time, making the use of non-authorised instant messaging redundant. IT
managers can now properly regulate internet based collaboration with a system tailor
made for the office.
Page 52 of 206

It has also been noted that cloud collaboration has become more and more necessary
for IT departments as workforces have become more mobile and now need access to
important documents wherever they are, whether this is through an internet browser, or
through newer technologies such as smartphones and tablet devices.
The tech industry saw several large paradigm changes:
The mainframe computing era enabled business growth to be untethered from
the number of employees needed to process transactions manually.
The personal computing era empowered business users to run their businesses
based on individual data and applications on their PCs.
A decade of network computing established an unprecedented level of
transparency of information across multiple groups inside a company and an
amazing rate of data exchange between enterprises.
Each of these revolutions brought with it new economies of scale. The cost-pertransaction,
the cost of automating office and desktop processes, and finally the cost of
network bandwidth fell quickly and enabled business users to apply ICT solutions more
broadly to create business value. Most analysts (Forrester, Gartner, etc.) believe that
cloud computing will help unleash the next wave of tech-enabled business innovation.
Page 53 of 206

During the mainframe era, client/server was initially viewed as a "toy" technology, not
viable as a mainframe replacement. Yet, over time the client/server technology found its
way into the enterprise. Similarly, when virtualization technology was first proposed,
application compatibility concerns and potential vendor lock-in were cited as barriers to
adoption. Yet underlying economics of 20 to 30 percent savings compelled CIOs to
overcome these concerns, and adoption quickly accelerated.
Recent Developments
Early cloud collaboration tools were quite basic with limited features. Newer packages
are much more document-centric in their approach to collaboration. More sophisticated
tools allow users to "tag" specific areas of a document for comments which are
delivered real time to those viewing the document. In some cases, the collaboration
software can even be integrated into Microsoft Office, or allow users to set up video
conferences.
Furthermore, the trend now is for firms to employ a single software tool to solve all their
collaboration needs, rather than having to rely on multiple different techniques. Single
cloud collaboration providers are now replacing a complicated tangle of instant
messengers, email and FTP.
Cloud collaboration today is promoted as a tool for collaboration internally between
different departments within a firm, but also externally as a means for sharing
documents with end-clients as receiving feedback. This makes cloud computing a very
versatile tool for firms with many different applications in a business environment.
The best cloud collaboration tools:
Use real-time commenting and messaging features to enhance speed of project
delivery
Leverage presence indicators to identify when others are active on documents
owned by another person
Allow users to set permissions and manage other users' activity profiles
Allow users to set personal activity feeds and email alert profiles to keep abreast
of latest activities per file or user
Allow users to collaborate and share files with users outside the company firewall
Comply with company security and compliance framework
Ensure full auditability of files and documents shared within and outside the
organization
Page 54 of 206

Reduce workarounds for sharing and collaboration on large files
A 2011 report by Gartner outlines a five stage model on the maturity of firms when it
comes to the uptake of cloud collaboration tools. A firm in the first stage is said to be
"reactive", with only email as a collaboration platform and a culture which resists
information sharing. A firm in the fifth stage is called "pervasive", and has universal
access to a rich collaboration toolset and a strong collaborative culture. The article
argues that most firms are in the second stage, but as cloud collaboration becomes
more important, most analysts expect to see the majority of firms moving up in the
model.
Page 55 of 206

Page 56 of 206

IV. Open Collaboration
Open Collaboration is "any system of innovation or production that relies on
goal-oriented yet loosely coordinated participants who interact to create a product (or
service) of economic value, which they make available to contributors and
noncontributors alike." It is prominently observed in open source software, but can also
be found in many other instances, such as in Internet forums, mailing lists and online
communities. Open collaboration is also thought to be the operating principle
underlining a gamut of diverse ventures, including bitcoin, TEDx, and Wikipedia.
Open collaboration is the principle
underlying peer production, mass
collaboration, and wikinomics. It was
observed initially in open source
software, but can also be found in
many other instances, such as in
Internet forums, mailing lists,
Internet communities, and many
instances of open content, such as
creative commons. It also explains
some instances of crowdsourcing,
collaborative consumption, and open
innovation.
Riehle et al. define open collaboration as
collaboration based on three principles of
egalitarianism, meritocracy, and selforganization.
Levine and Prietula define open
collaboration
as "any system of innovation or production that relies
on goal-oriented yet loosely coordinated participants who interact to create a product (or
service) of economic value, which they make available to contributors and
noncontributors alike." This definition captures multiple instances, all joined by similar
principles. For example, all of the elements — goods of economic value, open access to
contribute and consume, interaction and exchange, purposeful yet loosely coordinated
work — are present in an open source software project, in Wikipedia, or in a user forum
or community. They can also be present in a commercial website that is based on usergenerated
content. In all of these instances of open collaboration, anyone can
contribute and anyone can freely partake in the fruits of sharing, which are produced by
interacting participants who are loosely coordinated.
An annual conference dedicated to the research and practice of open collaboration is
the International Symposium on Wikis and Open Collaboration (OpenSym, formerly
WikiSym). As per its website, the group defines open collaboration as "collaboration that
is egalitarian (everyone can join, no principled or artificial barriers to participation exist),
meritocratic (decisions and status are merit-based rather than imposed) and self-
Page 57 of 206

organizing (processes adapt to people rather than people adapt to pre-defined
processes)."
______
The Open-Source Model
The open-source model is a decentralized software-development model that
encourages open collaboration. A main principle of open-source software development
is peer production, with products such as source code, blueprints, and documentation
freely available to the public. The open-source movement in software began as a
response to the limitations of proprietary code. The model is used for projects such as in
open-source appropriate technology, and open-source drug discovery.
Open source promotes universal access via an open-source or free license to a
product's design or blueprint, and universal redistribution of that design or blueprint.
Before the phrase open source became widely adopted, developers and producers
used a variety of other terms. Open source gained hold with the rise of the Internet. The
open-source software movement arose to clarify copyright, licensing, domain, and
consumer issues.
Generally, open source refers to a computer program in which the source code is
available to the general public for use or modification from its original design. Opensource
code is meant to be a collaborative effort, where programmers improve upon the
source code and share the changes within the community. Code is released under the
terms of a software license. Depending on the license terms, others may then
download, modify, and publish their version (fork) back to the community.
Many large formal institutions have sprung up to support the development of the opensource
movement, including the Apache Software Foundation, which supports
community projects such as the open-source framework Apache Hadoop and the opensource
HTTP server Apache HTTP.
History
The sharing of technical information predates the Internet and the personal computer
considerably. For instance, in the early years of automobile development a group of
capital monopolists owned the rights to a 2-cycle gasoline-engine patent originally filed
by George B. Selden. By controlling this patent, they were able to monopolize the
industry and force car manufacturers to adhere to their demands, or risk a lawsuit.
In 1911, independent automaker Henry Ford won a challenge to the Selden patent. The
result was that the Selden patent became virtually worthless and a new association
(which would eventually become the Motor Vehicle Manufacturers Association) was
formed. The new association instituted a cross-licensing agreement among all US
automotive manufacturers: although each company would develop technology and file
patents, these patents were shared openly and without the exchange of money among
Page 58 of 206

all the manufacturers. By the time the US entered World War II, 92 Ford patents and
515 patents from other companies were being shared among these manufacturers,
without any exchange of money (or lawsuits).
Early instances of the free sharing of source code include IBM's source releases of its
operating systems and other programs in the 1950s and 1960s, and the SHARE user
group that formed to facilitate the exchange of software. Beginning in the 1960s,
ARPANET researchers used an open "Request for Comments" (RFC) process to
encourage feedback in early telecommunication network protocols. This led to the birth
of the early Internet in 1969.
The sharing of source code on the Internet began when the Internet was relatively
primitive, with software distributed via UUCP, Usenet, IRC, and Gopher. BSD, for
example, was first widely distributed by posts to comp.os.linux on the Usenet, which is
also where its development was discussed. Linux followed in this model.
Open Source as A Term
The term "open source" was first proposed by a group of people in the free software
movement who were critical of the political agenda and moral philosophy implied in the
term "free software" and sought to reframe the discourse to reflect a more commercially
minded position. In addition, the ambiguity of the term "free software" was seen as
discouraging business adoption. The group included Christine Peterson, Todd
Anderson, Larry Augustin, Jon Hall, Sam Ockman, Michael Tiemann and Eric S.
Raymond. Peterson suggested "open source" at a meeting held at Palo Alto, California,
in reaction to Netscape's announcement in January 1998 of a source code release for
Navigator. Linus Torvalds gave his support the following day, and Phil Hughes backed
the term in Linux Journal. Richard Stallman, the founder of the free software movement,
Page 59 of 206

initially seemed to adopt the term, but later changed his mind. Netscape released its
source code under the Netscape Public License and later under the Mozilla Public
License.
Raymond was especially active in the effort to popularize the new term. He made the
first public call to the free software community to adopt it in February 1998. Shortly after,
he founded The Open Source Initiative in collaboration with Bruce Perens.
The term gained further visibility through an event organized in April 1998 by technology
publisher Tim O'Reilly. Originally titled the "Freeware Summit" and later known as the
"Open Source Summit", the event was attended by the leaders of many of the most
important free and open-source projects, including Linus Torvalds, Larry Wall, Brian
Behlendorf, Eric Allman, Guido van Rossum, Michael Tiemann, Paul Vixie, Jamie
Zawinski, and Eric Raymond. At that meeting, alternatives to the term "free software"
were discussed. Tiemann argued for "sourceware" as a new term, while Raymond
argued for "open source". The assembled developers took a vote, and the winner was
announced at a press conference the same evening.
"Open source" has never managed to entirely supersede the older term "free software",
giving rise to the combined term free and open-source software (FOSS).
Economics
Some economists agree that open-source is an information good or "knowledge good"
with original work involving a significant amount of time, money, and effort. The cost of
reproducing the work is low enough that additional users may be added at zero or near
zero cost – this is referred to as the marginal cost of a product. Copyright creates a
monopoly so the price charged to consumers can be significantly higher than the
marginal cost of production. This allows the author to recoup the cost of making the
original work. Copyright thus creates access costs for consumers who value the work
more than the marginal cost but less than the initial production cost. Access costs also
pose problems for authors who wish to create a derivative work—such as a copy of a
software program modified to fix a bug or add a feature, or a remix of a song—but are
unable or unwilling to pay the copyright holder for the right to do so.
Being organized as effectively a "consumers' cooperative", open source eliminates
some of the access costs of consumers and creators of derivative works by reducing
the restrictions of copyright. Basic economic theory predicts that lower costs would lead
to higher consumption and also more frequent creation of derivative works.
Organizations such as Creative Commons host websites where individuals can file for
alternative "licenses", or levels of restriction, for their works. These self-made
protections free the general society of the costs of policing copyright infringement.
Others argue that since consumers do not pay for their copies, creators are unable to
recoup the initial cost of production and thus have little economic incentive to create in
the first place. By this argument, consumers would lose out because some of the goods
Page 60 of 206

they would otherwise purchase would not be available. In practice, content producers
can choose whether to adopt a proprietary license and charge for copies, or an open
license. Some goods which require large amounts of professional research and
development, such as the pharmaceutical industry (which depends largely on patents,
not copyright for intellectual property protection) are almost exclusively proprietary,
although increasingly sophisticated technologies are being developed on open-source
principles.
There is evidence that open-source development creates enormous value. For
example, in the context of open-source hardware design, digital designs are shared for
free and anyone with access to digital manufacturing technologies (e.g. RepRap 3D
printers) can replicate the product for the cost of materials. The original sharer may
receive feedback and potentially improvements on the original design from the peer
production community.
Licensing Alternatives
Alternative arrangements have also been shown to result in good creation outside of the
proprietary license model.
Examples include:
Creation for its own sake – For example, Wikipedia editors add content for
recreation. Artists have a drive to create. Both communities benefit from free
starting material.
Voluntary after-the-fact donations – used by shareware, street performers, and
public broadcasting in the United States.
Page 61 of 206

Patron – For example, open access publishing relies on institutional and
government funding of research faculty, who also have a professional incentive
to publish for reputation and career advancement. Works of the U.S. federal
government are automatically released into the public domain.
Freemium – Give away a limited version for free and charge for a premium
version (potentially using a dual license).
Give away the product and charge something related – Charge for support of
open-source enterprise software, give away music but charge for concert
admission.
Give away work in order to gain market share – Used by artists, in corporate
software to spoil a dominant competitor (for example in the browser wars and the
Android operating system).
For own use – Businesses or individual software developers often create
software to solve a problem, bearing the full cost of initial creation. They will then
open source the solution, and benefit from the improvements others make for
their own needs. Communalizing the maintenance burden distributes the cost
across more users; free riders can also benefit without undermining the creation
process.
Open-Source Applications
Social and political views have been affected by the growth of the concept of open
source. Advocates in one field often support the expansion of open source in other
fields. But Eric Raymond and other founders of the open-source movement have
sometimes publicly argued against speculation about applications outside software,
saying that strong arguments for software openness should not be weakened by
overreaching into areas where the story may be less compelling. The broader impact of
the open-source movement, and the extent of its role in the development of new
information sharing procedures, remain to be seen.
The open-source movement has inspired increased transparency and liberty in
biotechnology research, for example by open therapeutics and CAMBIA Even the
research methodologies themselves can benefit from the application of open-source
principles. It has also given rise to the rapidly-expanding open-source hardware
movement.
Computer software
Open-source software is software whose source code is published and made available
to the public, enabling anyone to copy, modify and redistribute the source code without
paying royalties or fees. Open-source code can evolve through community cooperation.
These communities are composed of individual programmers as well as large
Page 62 of 206

companies. Some of the individual programmers who start an open-source project may
end up establishing companies offering products or services incorporating open-source
programs. Examples of open-source software products are:
Linux (that much of worlds server parks are running)
MediaWiki (that wikipedia is based upon)
many more
Electronics
Open-source hardware is hardware whose initial specification, usually in a software
format, are published and made available to the public, enabling anyone to copy, modify
and redistribute the hardware and source code without paying royalties or fees. Opensource
hardware evolves through community cooperation. These communities are
composed of individual hardware/software developers, hobbyists, as well as very large
companies. Examples of open-source hardware initiatives are:
Openmoko: a family of open-source mobile phones, including the hardware
specification and the operating system.
Page 63 of 206

OpenRISC: an open-source microprocessor family, with architecture specification
licensed under GNU GPL and implementation under LGPL.
Sun Microsystems's OpenSPARC T1 Multicore processor. Sun has released it
under GPL.
Arduino, a microcontroller platform for hobbyists, artists and designers.
GizmoSphere, an open-source development platform for the embedded design
community; the site includes code downloads and hardware schematics along
with free user guides, spec sheets and other documentation.
Simputer, an open hardware handheld computer, designed in India for use in
environments where computing devices such as personal computers are deemed
inappropriate.
LEON: A family of open-source microprocessors distributed in a library with
peripheral IP cores, open SPARC V8 specification, implementation available
under GNU GPL.
Tinkerforge: A system of open-source stackable microcontroller building blocks.
Allows control of motors and read out sensors with the programming languages
C, C++, C#, Object Pascal, Java, PHP, Python and Ruby over a USB or Wifi
connection on Windows, Linux and Mac OS X. All of the hardware is licensed
under CERN OHL (CERN Open Hardware License).
Open Compute Project: designs for computer data center including power
supply, Intel motherboard, AMD motherboard, chassis, racks, battery cabinet,
and aspects of electrical and mechanical design.
Lasersaur, an open-source laser cutter.
Food and Beverages
Some publishers of open-access journals have argued that data from food science and
gastronomy studies should be freely available to aid reproducibility. A number of people
have published creative commons licensed recipe books.
Open-source colas – cola soft drinks, similar to Coca-Cola and Pepsi, whose
recipe is open source and developed by volunteers. The taste is said to be
comparable to that of the standard beverages. Most corporations producing
beverages hold their formulas as closely guarded secrets.
Free Beer (originally Vores Øl) – is an open-source beer created by students at
the IT-University in Copenhagen together with Superflex, an artist collective, to
illustrate how open-source concepts might be applied outside the digital world.
Page 64 of 206

In 2002, the beer company Brewtopia in Australia started an open-source
brewery and invited the general population to be involved in the development and
ownership of the brewery, and to vote on the development of every aspect of its
beer, Blowfly, and its road to market. In return for their feedback and input,
individuals received shares in the company, which is now publicly traded on a
stock exchange in Australia. The company has always adhered to its opensource
roots and is the only beer company in the world that allows the public to
design, customise and develop its own beers online.
Digital Content
Open-content projects organized by the Wikimedia Foundation – Sites such as
Wikipedia and Wiktionary have embraced the open-content Creative Commons content
licenses. These licenses were designed to adhere to principles similar to various opensource
software development licenses. Many of these licenses ensure that content
remains free for re-use, that source documents are made readily available to interested
parties, and that changes to content are accepted easily back into the system. Important
sites embracing open-source-like ideals are Project Gutenberg and Wikisource, both of
which post many books on which the copyright has expired and are thus in the public
domain, ensuring that anyone has free, unlimited access to that content.
Open ICEcat is an open catalog for the IT, CE and Lighting sectors with product
data-sheets based on Open Content License agreement. The digital content are
distributed in XML and URL formats.
Page 65 of 206

Google Sketchup's 3D Warehouse is an open-source design community
centered around the use of proprietary software that's free.
The University of Waterloo Stratford Campus invites students every year to use
its three-storey Christie MicroTiles wall as a digital canvas for their creative work.
Medicine
Pharmaceuticals – There have been several proposals for open-source
pharmaceutical development, which led to the establishment of the Tropical
Disease Initiative and the Open Source Drug Discovery for Malaria Consortium.
Genomics – The term "open-source genomics" refers to the combination of rapid
release of sequence data (especially raw reads) and crowdsourced analyses
from bioinformaticians around the world that characterised the analysis of the
2011 E. coli O104:H4 outbreak.
OpenEMR – OpenEMR is an ONC-ATB Ambulatory EHR 2011-2012 certified
electronic health records and medical practice management application. It
features fully integrated electronic health, records, practice management,
scheduling, electronic billing, and is the base for many EHR programs.
http://www.open-emr.org/
Science and Engineering
Research – The Science Commons was created as an alternative to the
expensive legal costs of sharing and reusing scientific works in journals etc.
Research – The Open Source Science Project was created to increase the ability
for students to participate in the research process by providing them access to
microfunding – which, in turn, offers non-researchers the opportunity to directly
invest, and follow, cutting-edge scientific research. All data and methodology is
subsequently published in an openly accessible manner under a Creative
Commons fair use license.
Research – The Open Solar Outdoors Test Field (OSOTF) is a grid-connected
photovoltaic test system, which continuously monitors the output of a number of
photovoltaic modules and correlates their performance to a long list of highly
accurate meteorological readings. The OSOTF is organized under open-source
principles – All data and analysis is to be made freely available to the entire
photovoltaic community and the general public.
Engineering – Hyperloop, a form of high-speed transport proposed by
entrepreneur Elon Musk, which he describes as "an elevated, reduced-pressure
Page 66 of 206

tube that contains pressurized capsules driven within the tube by a number of
linear electric motors".
Construction – WikiHouse is an open-source project for designing and building
houses.
Energy research - The Open Energy Modelling Initiative promotes open-source
models and open data in energy research and policy advice.
Robotics
An open-source robot is a robot whose blueprints, schematics, or source code are
released under an open-source model.
Transport
Open Trip Planner - this code
base is growing rapidly, with
adoption in Portland, New
York, The Netherlands and
Helsinki.
Fashion
TravelSpirit – a greater
level of 'superarchitecture'
ambition, to
bring a range of open
source projects together,
in order to deliver 'Mobility
as a Service'
based
Eyewear – In June 2013, an opensource
eyewear brand, Botho,
has started trading under the UK
Open Optics Ltd company.
Other
Open-source principles can be applied to technical areas such as digital
communication protocols and data storage formats.
Open design – which involves applying open-source methodologies to the design
of artifacts and systems in the physical world. It is very nascent but has huge
potential.
Page 67 of 206

Open-source-appropriate technology (OSAT) refers to technologies that are
designed in the same fashion as free and open-source software. [58] These
technologies must be "appropriate technology" (AT) – meaning technology that is
designed with special consideration to the environmental, ethical, cultural, social,
political, and economic aspects of the community it is intended for. An example
of this application is the use of open-source 3D printers like the RepRap to
manufacture appropriate technology.
Teaching – which involves applying the concepts of open source to instruction
using a shared web space as a platform to improve upon learning,
organizational, and management challenges. An example of an Open-source
courseware is the Java Education & Development Initiative (JEDI). Other
examples include Khan Academy and wikiversity. At the university level, the use
of open-source-appropriate technology classroom projects has been shown to be
successful in forging the connection between science/engineering and social
benefit: This approach has the potential to use university students' access to
resources and testing equipment in furthering the development of appropriate
technology. Similarly OSAT has been used as a tool for improving service
learning.
There are few examples of business information (methodologies, advice,
guidance, practices) using the open-source model, although this is another case
where the potential is enormous. ITIL is close to open source. It uses the
Cathedral model (no mechanism exists for user contribution) and the content
must be bought for a fee that is small by business consulting standards
(hundreds of British pounds). Various checklists are published by government,
banks or accounting firms.
An open-source group emerged in 2012 that is attempting to design a firearm
that may be downloaded from the internet and "printed" on a 3D Printer. [64]
Calling itself Defense Distributed, the group wants to facilitate "a working plastic
gun that could be downloaded and reproduced by anybody with a 3D printer".
Agrecol, a German NGO has developed an open-source licence for seeds
operating with copyleft and created OpenSourceSeeds as a respective service
provider. Breeders that apply the license to their new invented material prevent it
from the threat of privatisation and help to establish a commons-based breeding
sector as an alternative to the commercial sector.
Society and Culture
The rise of open-source culture in the 20th century resulted from a growing tension
between creative practices that involve require access to content that is often
copyrighted, and restrictive intellectual property laws and policies governing access to
copyrighted content. The two main ways in which intellectual property laws became
more restrictive in the 20th century were extensions to the term of copyright (particularly
Page 68 of 206

in the United States) and penalties, such as those articulated in the Digital Millennium
Copyright Act (DMCA), placed on attempts to circumvent anti-piracy technologies.
Although artistic appropriation is often permitted under fair-use doctrines, the complexity
and ambiguity of these doctrines creates an atmosphere of uncertainty among cultural
practitioners. Also, the protective actions of copyright owners create what some call a
"chilling effect" among cultural practitioners.
The idea of an "open-source" culture runs parallel to "Free Culture," but is substantively
different. Free culture is a term derived from the free software movement, and in
contrast to that vision of culture, proponents of open-source culture (OSC) maintain that
some intellectual property law needs to exist to protect cultural producers. Yet they
propose a more nuanced position than corporations have traditionally sought. Instead of
seeing intellectual property law as an expression of instrumental rules intended to
uphold either natural rights or desirable outcomes, an argument for OSC takes into
account diverse goods (as in "the Good life") and ends.
Sites such as ccMixter offer up free web space for anyone willing to license their work
under a Creative Commons license. The resulting cultural product is then available to
download free (generally accessible) to anyone with an Internet connection. Older
analog technologies such as the telephone or television have limitations on the kind of
interaction users can have.
Through various technologies such as peer-to-peer networks and blogs, cultural
producers can take advantage of vast social networks to distribute their products. As
opposed to traditional media distribution, redistributing digital media on the Internet can
be virtually costless. Technologies such as BitTorrent and Gnutella take advantage of
various characteristics of the Internet protocol (TCP/IP) in an attempt to totally
decentralize file distribution.
Government
Open politics (sometimes known as Open-source politics) is a political process
that uses Internet technologies such as blogs, email and polling to provide for a
Page 69 of 206

apid feedback mechanism between political organizations and their supporters.
There is also an alternative conception of the term Open-source politics which
relates to the development of public policy under a set of rules and processes
similar to the open-source software movement.
Open-source governance is similar to open-source politics, but it applies more to
the democratic process and promotes the freedom of information.
The South Korean government wants to increase its use of free and open-source
software, in order to decrease its dependence on proprietary software solutions.
It plans to make open standards a requirement, to allow the government to
choose between multiple operating systems and web browsers. Korea's Ministry
of Science, ICT & Future Planning is also preparing ten pilots on using opensource
software distributions.
Ethics
Open-source ethics is split into two strands:
Open-source ethics as an ethical school – Charles Ess and David Berry are
researching whether ethics can learn anything from an open-source approach.
Ess famously even defined the AoIR Research Guidelines as an example of
open-source ethics.
Open-source ethics as a professional body of rules – This is based principally on
the computer ethics school, studying the questions of ethics and professionalism
in the computer industry in general and software development in particular. [72]
Religion
Irish philosopher Richard Kearney has used the term "open-source Hinduism" to refer to
the way historical figures such as Mohandas Gandhi and Swami Vivekananda worked
upon this ancient tradition.
Media
Open-source journalism formerly referred to the standard journalistic techniques of
news gathering and fact checking, reflecting open-source intelligence a similar term
used in military intelligence circles. Now, open-source journalism commonly refers to
forms of innovative publishing of online journalism, rather than the sourcing of news
stories by a professional journalist. In the 25 December 2006 issue of TIME magazine
this is referred to as user created content and listed alongside more traditional opensource
projects such as OpenSolaris and Linux.
Weblogs, or blogs, are another significant platform for open-source culture. Blogs
consist of periodic, reverse chronologically ordered posts, using a technology that
makes webpages easily updatable with no understanding of design, code, or file
Page 70 of 206

transfer required. While corporations, political campaigns and other formal institutions
have begun using these tools to distribute information, many blogs are used by
individuals for personal expression, political organizing, and socializing. Some, such as
LiveJournal or WordPress, utilize open-source software that is open to the public and
can be modified by users to fit their own tastes. Whether the code is open or not, this
format represents a nimble tool for people to borrow and re-present culture; whereas
traditional websites made the illegal reproduction of culture difficult to regulate, the
mutability of blogs makes "open sourcing" even more uncontrollable since it allows a
larger portion of the population to replicate material more quickly in the public sphere.
Messageboards are another platform for open-source culture. Messageboards (also
known as discussion boards or forums), are places online where people with similar
interests can congregate and post messages for the community to read and respond to.
Messageboards sometimes have moderators who enforce community standards of
etiquette such as banning users who are spammers. Other common board features are
private messages (where users can send messages to one another) as well as chat (a
way to have a real time conversation online) and image uploading. Some
messageboards use phpBB, which is a free open-source package. Where blogs are
more about individual expression and tend to revolve around their authors,
messageboards are about creating a conversation amongst its users where information
can be shared freely and quickly. Messageboards are a way to remove intermediaries
from everyday life—for instance, instead of relying on commercials and other forms of
advertising, one can ask other users for frank reviews of a product, movie or CD. By
removing the cultural middlemen, messageboards help speed the flow of information
and exchange of ideas.
Page 71 of 206

OpenDocument is an open document file format for saving and exchanging editable
office documents such as text documents (including memos, reports, and books),
spreadsheets, charts, and presentations. Organizations and individuals that store their
data in an open format such as OpenDocument avoid being locked into a single
software vendor, leaving them free to switch software if their current vendor goes out of
business, raises their prices, changes their software, or changes their licensing terms to
something less favorable.
Open-source movie production is either an open call system in which a changing crew
and cast collaborate in movie production, a system in which the end result is made
available for re-use by others or in which exclusively open-source products are used in
the production. The 2006 movie Elephants Dream is said to be the "world's first open
movie", created entirely using open-source technology.
An open-source documentary film has a production process allowing the open
contributions of archival material footage, and other filmic elements, both in unedited
and edited form, similar to crowdsourcing. By doing so, on-line contributors become part
of the process of creating the film, helping to influence the editorial and visual material
to be used in the documentary, as well as its thematic development. The first opensource
documentary film is the non-profit "The American Revolution", which went into
development in 2006, and will examine the role media played in the cultural, social and
political changes from 1968 to 1974 through the story of radio station WBCN-FM in
Boston. The film is being produced by Lichtenstein Creative Media and the non-profit
Filmmakers Collaborative. Open Source Cinema is a website to create Basement
Tapes, a feature documentary about copyright in the digital age, co-produced by the
National Film Board of Canada. Open-source film-making refers to a form of film-making
that takes a method of idea formation from open-source software, but in this case the
'source' for a filmmaker is raw unedited footage rather than programming code. It can
also refer to a method of film-making where the process of creation is 'open' i.e. a
disparate group of contributors, at different times contribute to the final piece.
Open-IPTV is IPTV that is not limited to one recording studio, production studio, or cast.
Open-IPTV uses the Internet or other means to pool efforts and resources together to
create an online community that all contributes to a show.
Education
Within the academic community, there is discussion about expanding what could be
called the "intellectual commons" (analogous to the Creative Commons). Proponents of
this view have hailed the Connexions Project at Rice University, OpenCourseWare
project at MIT, Eugene Thacker's article on "open-source DNA", the "Open Source
Cultural Database", Salman Khan's Khan Academy and Wikipedia as examples of
applying open source outside the realm of computer software.
Open-source curricula are instructional resources whose digital source can be freely
used, distributed and modified.
Page 72 of 206

Another strand to the academic community is in the area of research. Many funded
research projects produce software as part of their work. There is an increasing interest
in making the outputs of such projects available under an open-source license. In the
UK the Joint Information Systems Committee (JISC) has developed a policy on opensource
software. JISC also funds a development service called OSS Watch which acts
as an advisory service for higher and further education institutions wishing to use,
contribute to and develop open-source software.
On 30 March 2010, President Barack Obama signed the Health Care and Education
Reconciliation Act, which included $2 billion over four years to fund the TAACCCT
program, which is described as "the largest OER (open education resources) initiative in
the world and uniquely focused on creating curricula in partnership with industry for
credentials in vocational industry sectors like manufacturing, health, energy,
transportation, and IT".
Innovation Communities
The principle of sharing pre-dates the open-source movement; for example, the free
sharing of information has been institutionalized in the scientific enterprise since at least
the 19th century. Open-source principles have always been part of the scientific
community. The sociologist Robert K. Merton described the four basic elements of the
community—universal ism (an international perspective), communal ism (sharing
information), disinterestedness (removing one's personal views from the scientific
inquiry) and organized skepticism (requirements of proof and review) that accurately
describe the scientific community today.
Page 73 of 206

These principles are, in part, complemented by US law's focus on protecting expression
and method but not the ideas themselves. There is also a tradition of publishing
research results to the scientific community instead of keeping all such knowledge
proprietary. One of the recent initiatives in scientific publishing has been open access—
the idea that research should be published in such a way that it is free and available to
the public. There are currently many open access journals where the information is
available free online, however most journals do charge a fee (either to users or libraries
for access). The Budapest Open Access Initiative is an international effort with the goal
of making all research articles available free on the Internet.
The National Institutes of Health has recently proposed a policy on "Enhanced Public
Access to NIH Research Information". This policy would provide a free, searchable
resource of NIH-funded results to the public and with other international repositories six
months after its initial publication. The NIH's move is an important one because there is
significant amount of public funding in scientific research. Many of the questions have
yet to be answered—the balancing of profit vs. public access, and ensuring that
desirable standards and incentives do not diminish with a shift to open access.
Farmavita.Net is a community of pharmaceuticals executives that has recently proposed
a new business model of open-source pharmaceuticals. The project is targeted to
development and sharing of know-how for manufacture of essential and life-saving
medicines. It is mainly dedicated to the countries with less developed economies where
local pharmaceutical research and development resources are insufficient for national
needs. It will be limited to generic (off-patent) medicines with established use. By the
definition, medicinal product have a "well-established use" if is used for at least 15
years, with recognized efficacy and an acceptable level of safety. In that event, the
expensive clinical test and trial results could be replaced by appropriate scientific
literature.
Benjamin Franklin was an early contributor eventually donating all his inventions
including the Franklin stove, bifocals, and the lightning rod to the public domain.
New NGO communities are starting to use the open-source technology as a tool. One
example is the Open Source Youth Network started in 2007 in Lisboa by ISCA
members.
Open innovation is also a new emerging concept which advocate putting R&D in a
common pool. The Eclipse platform is openly presenting itself as an Open innovation
network.
Arts and Recreation
Copyright protection is used in the performing arts and even in athletic activities. Some
groups have attempted to remove copyright from such practices.
Page 74 of 206

In 2012, Russian music composer, scientist and Russian Pirate Party member Victor
Argonov presented detailed raw files of his electronic opera "2032" under free license
CC-BY-NC 3.0. This opera was originally composed and published in 2007 by Russian
label MC Entertainment as a commercial product, but then the author changed its status
to free. In his blog he said that he decided to open raw files (including wav, midi and
other used formats) to the public in order to support worldwide pirate actions against
SOPA and PIPA. Several Internet resources, called "2032" the first open-source
musical opera in history.
Other Related Movements
The following are events and applications that have been developed via the open
source community, and echo the ideologies of the open source movement.
Open Education Consortium — an organization composed of various colleges
that support open source and share some of their material online. This
organization, headed by Massachusetts Institute of Technology, was established
to aid in the exchange of open source educational materials.
Wikipedia — user-generated online encyclopedia with sister projects in
academic areas, such as Wikiversity — a community dedicated to the creation
and exchange of learning materials
Project Gutenberg — prior to the existence of Google Scholar Beta, this was
the first supplier of electronic books and the very first free library project
Synthetic Biology- This new technology is potentially important because it
promises to enable cheap, lifesaving new drugs as well as helping to yield
biofuels that may help to solve our energy problem. Although synthetic biology
has not yet come out of its "lab" stage, it has potential to become industrialized in
the near future. In order to industrialize open source science, there are some
scientists who are trying to build their own brand of it.
Page 75 of 206

Ideologically-Related Movements
The open-access movement is a movement that is similar in ideology to the open
source movement. Members of this movement maintain that academic material should
be readily available to provide help with “future research, assist in teaching and aid in
academic purposes.” The Open access movement aims to eliminate subscription fees
and licensing restrictions of academic materials
The free-culture movement is a movement that seeks to achieve a culture that engages
in collective freedom via freedom of expression, free public access to knowledge and
information, full demonstration of creativity and innovation in various arenas and
promotion of citizen liberties.
Creative Commons is an organization that “develops, supports, and stewards legal and
technical infrastructure that maximizes digital creativity, sharing, and innovation.” It
encourages the use of protected properties online for research, education, and creative
purposes in pursuit of a universal access. Creative Commons provides an infrastructure
through a set of copyright licenses and tools that creates a better balance within the
realm of “all rights reserved” properties. The Creative Commons license offers a slightly
more lenient alternative to “all rights reserved” copyrights for those who do not wish to
exclude the use of their material.
The Zeitgeist Movement is an international social movement that advocates a transition
into a sustainable "resource-based economy" based on collaboration in which monetary
incentives are replaced by commons-based ones with everyone having access to
everything (from code to products) as in "open source everything". While its activism
and events are typically focused on media and education, TZM is a major supporter of
open source projects worldwide since they allow for uninhibited advancement of science
and technology, independent of constraints posed by institutions of patenting and
capitalist investment.
P2P Foundation is an “international organization focused on studying, researching,
documenting and promoting peer to peer practices in a very broad sense”. Its objectives
incorporate those of the open source movement, whose principles are integrated in a
larger socio-economic model.
Page 76 of 206

V. Crowdmapping & Crowdsensing
Big Data & Data Analysis
Crowdmapping is a subtype of crowdsourcing by which aggregation of crowdgenerated
inputs such as captured communications and social media feeds are
combined with geographic data to create a digital map that is as up-to-date as possible
on events such as wars, humanitarian crises, crime, elections, or natural disasters.
Such maps are typically created collaboratively by people coming together over the
Internet.
The information can typically be sent to the map initiator or initiators by SMS or by filling
out a form online and are then gathered on a map online automatically or by a
dedicated group. In 2010 Ushahidi released "Crowdmap" − a free and open-source
platform by which anyone can start crowdmapping projects.
Uses
Page 77 of 206

Crowdmapping can be used to track fires, floods, pollution, crime, political violence, the
spread of disease and bring a level of transparency to fast-moving events that are
difficult for traditional media to adequately cover, or problem areas and longer-term
trends and that may be difficult to identify through the reporting of individual events.
During disasters the timeliness of relevant maps is critical as the needs and locations of
victims may change rapidly.
The use of crowdmapping by authorities can improve situational awareness during an
incident and be used to support incident response.
Crowdmaps are an efficient way to visually demonstrate the geographical spread of a
phenomenon.
Examples
HealthMap is a freely accessible, automated electronic information system in
operation since 2006 that monitors, organizes, and visualizes reports of global
disease outbreaks according to geography, time, and infectious disease agent
that also crowdsources user data.
2007–08 Kenyan crisis
In the 2010 Haiti earthquake the Ushahidi crowdmapping platform was used to
map more than 3584 events in close to real time, including breakout of fires and
people trapped under buildings.
One week after the Fukushima Daiichi nuclear disaster in 2011 the Safecast
project was launched that loaned volunteers cheap Geiger counters to measure
local levels of radioactivity (or volunteers purchased their own device). This data
was mapped and made publicly available through their website.
Hurricane Irene in 2011
In 2012 the Danish daily newspaper and online title Dagbladet Information
mapped the positions of surveillance cameras by encouraging readers to use a
free Android and iOS app to photograph and geolocate CCTV cameras.
In 2013, predict the reemergence of cicada swarms, WNYC—a public radio
station in New York City—asked residents of certain areas to use sensors to
track the soil temperature. The crowd-reported temperatures were displayed on a
map on WNYC’s website.
April 2015 Nepal earthquake
______
Page 78 of 206

Crowdsensing
Crowdsensing, sometimes referred to as Mobile Crowdsensing, is a
technique where a large group of individuals having mobile devices capable of sensing
and computing (such as smartphones, tablet computers, wearables) collectively share
data and extract information to measure, map, analyze, estimate or infer (predict) any
processes of common interest. In short, this means crowdsourcing of sensor data from
mobile devices.
Devices equipped with various sensors have become ubiquitous. Most smartphones
can sense ambient light, noise (through the microphone), location (through the GPS),
movement (through the accelerometer), and more. These sensors can collect vast
quantities of data that are useful in a variety of ways. For example, GPS and
accelerometer data can be used to locate potholes in cities, and microphones can be
used with GPS to map noise pollution.
The term "mobile crowdsensing" was coined by Raghu Ganti, Fan Ye, and Hui Lei in
2011. Mobile crowdsensing belongs to three main types: environmental (such as
Page 79 of 206

monitoring pollution), infrastructure (such as locating potholes), and social (such as
tracking exercise data within a community).
Based on the type of involvement from the users, mobile crowdsensing can be
classified into two types:
Participatory crowdsensing, where the users voluntarily participate in contributing
information.
Opportunistic crowdsensing, where the data is sensed, collected and shared
automatically without user intervention and in some cases, even without the
user's explicit knowledge.
Taking advantage of the ubiquitous presence of powerful mobile computing devices
(especially smartphones) in the recent years, it has become an appealing method to
businesses that wish to collect data without making large-scale investments. Numerous
technology companies use this technique to offer services based on the big data
collected, some of the most notable examples being Facebook, Google and Uber.
Process
Mobile crowdsensing occurs in three stages: data collection, data storage and data
upload.
Data collection draws on sensors available through the Internet of things. There are
three main strategies for collecting this data:
The user of a device collects data manually. This can include taking pictures or
using smartphone applications.
The user can manually control data collection, but some data can be collected
automatically, such as when a user opens an application.
Data sensing is triggered by a particular context that has been predefined (e.g., a
device begins to collect data when the user is in a particular place at a particular
time).
The data collection phase can also involve a process called deduplication, which
involves removing redundant information from a data set in order to lower costs and
improve user experience. The deduplication process filters and compresses the data
that has been collected before it gets uploaded.
Resource Limitations
Challenges
Mobile crowdsensing potential is limited by constraints involving energy, bandwidth and
computation power. Using the GPS, for example, drains batteries, but location can also
Page 80 of 206

e tracked using Wi-Fi and GSM, although these are less accurate. Eliminating
redundant data can also reduce energy and bandwidth costs, as can restricting data
sensing when quality is unlikely to be high (e.g., when two photos are taken in the same
location, the second is unlikely to provide new information).
Privacy, Security, and Data Integrity
The data collected through mobile crowdsensing can be sensitive to individuals,
revealing personal information such as home and work locations and the routes used
when commuting between the two. Ensuring the privacy and security of personal
information collected through mobile crowdsensing is therefore important.
Mobile crowdsensing can use three main methods to protect privacy:
Anonymization, which removes identifying information from the data before it is
sent to a third party. This method does not prevent inferences being made based
on details that remain in the data.
Secure Multiparty Computation, which transforms data using cryptographic
techniques. This method is not scalable and requires the generation and
mainenance of multiple keys, which in return requires more energy.
Page 81 of 206

Data Perturbation, which adds noise to sensor data before sharing it with a
community. Noise can be added to data without compromising the accuracy of
the data.
Aggregation-Free Data Collection, which decentralizes the spatial-temporal
sensor data recovery through message-passing. This mechanism intends to
recover spatial-temporal sensor data the without aggregating participants'
sensor/location data to a center node (e.g., organizer), so as to protect the
privacy.
Data integrity can also be a problem when using mobile crowdsensing, especially when
the program is opt in; in these situations, people can either unintentionally or maliciously
contribute false data. Protecting data integrity can involve filtering, quality estimation,
etc. Other solutions include installing collocated infrastructure to act as a witness or by
using trusted hardware that is already installed on smartphones. However, both of these
methods can be expensive or energy intensive.
______
Big Data
Big data is data sets that are so voluminous and complex that traditional dataprocessing
application software are inadequate to deal with them. Big data challenges
include capturing data, data storage, data analysis, search, sharing, transfer,
visualization, querying, updating, information privacy and data source. There are five
concepts associated with big data: volume, variety, velocity and, the recently added,
veracity and value.
Lately, the term "big data" tends to refer to the use of predictive analytics, user behavior
analytics, or certain other advanced data analytics methods that extract value from data,
and seldom to a particular size of data set. "There is little doubt that the quantities of
data now available are indeed large, but that’s not the most relevant characteristic of
this new data ecosystem." Analysis of data sets can find new correlations to "spot
business trends, prevent diseases, combat crime and so on." Scientists, business
executives, practitioners of medicine, advertising and governments alike regularly meet
difficulties with large data-sets in areas including Internet search, fintech, urban
informatics, and business informatics. Scientists encounter limitations in e-Science
work, including meteorology, genomics, connectomics, complex physics simulations,
biology and environmental research.
Data sets grow rapidly - in part because they are increasingly gathered by cheap and
numerous information-sensing Internet of things devices such as mobile devices, aerial
(remote sensing), software logs, cameras, microphones, radio-frequency identification
(RFID) readers and wireless sensor networks. The world's technological per-capita
capacity to store information has roughly doubled every 40 months since the 1980s; as
Page 82 of 206

of 2012, every day 2.5 exabytes (2.5×10 18 ) of data are generated. Based on an IDC
report prediction, the global data volume will grow exponentially from 4.4 zettabytes to
44 zettabytes between 2013 to 2020. By 2025, IDC predicts there will be 163 zettabytes
of data. One question for large enterprises is determining who should own big-data
initiatives that affect the entire organization.
Relational database management systems and desktop statistics and software
packages to visualize data often have difficulty handling big data. The work may require
"massively parallel software running on tens, hundreds, or even thousands of servers".
What counts as "big data" varies depending on the capabilities of the users and their
tools, and expanding capabilities make big data a moving target. "For some
organizations, facing hundreds of gigabytes of data for the first time may trigger a need
to reconsider data management options. For others, it may take tens or hundreds of
terabytes before data size becomes a significant consideration."
Definition
The term has been in use since the 1990s, with some giving credit to John Mashey for
coining or at least making it popular. Big data usually includes data sets with sizes
beyond the ability of commonly used software tools to capture, curate, manage, and
process data within a tolerable elapsed time. Big data philosophy encompasses
unstructured, semi-structured and structured data, however the main focus is on
unstructured data. Big data "size" is a constantly moving target, as of 2012 ranging from
a few dozen terabytes to many exabytes of data. Big data requires a set of techniques
and technologies with new forms of integration to reveal insights from datasets that are
diverse, complex, and of a massive scale.
Page 83 of 206

A 2016 definition states that "Big data represents the information assets characterized
by such a high volume, velocity and variety to require specific technology and analytical
methods for its transformation into value". Additionally, a new V, veracity, is added by
some organizations to describe it, revisionism challenged by some industry authorities.
The three Vs (volume, variety and velocity) have been further expanded to other
complementary characteristics of big data:
Machine learning: big data often doesn't ask why and simply detects patterns
Digital footprint: big data is often a cost-free byproduct of digital interaction
A 2018 definition states "Big data is where parallel computing tools are needed to
handle data", and notes, "This represents a distinct and clearly defined change in the
computer science used, via parallel programming theories, and losses of some of the
guarantees and capabilities made by Codd’s relational model."
The growing maturity of the concept more starkly delineates the difference between "big
data" and "Business Intelligence":
Business Intelligence uses descriptive statistics with data with high information
density to measure things, detect trends, etc.
Big data uses inductive statistics and concepts from nonlinear system
identification to infer laws (regressions, nonlinear relationships, and causal
effects) from large sets of data with low information density to reveal
relationships and dependencies, or to perform predictions of outcomes and
behaviors.
Characteristics
Big data can be described by the following characteristics:
Volume: The quantity of generated and stored data. The size of the data
determines the value and potential insight, and whether it can be considered big
data or not.
Variety: The type and nature of the data. This helps people who analyze it to
effectively use the resulting insight. Big data draws from text, images, audio,
video; plus it completes missing pieces through data fusion.
Velocity: In this context, the speed at which the data is generated and processed
to meet the demands and challenges that lie in the path of growth and
development. Big data is often available in real-time.
Variability: Inconsistency of the data set can hamper processes to handle and
manage it.
Page 84 of 206

Veracity: The data quality of captured data can vary greatly, affecting the
accurate analysis.
Factory work and Cyber-physical systems may have a 6C system:
Connection (sensor and networks)
Cloud (computing and data on demand)
Cyber (model and memory)
Content/context (meaning and correlation)
Community (sharing and collaboration)
Customization (personalization and value)
Data must be processed with advanced tools (analytics and algorithms) to reveal
meaningful information. For example, to manage a factory one must consider both
visible and invisible issues with various components. Information generation algorithms
must detect and address invisible issues such as machine degradation, component
wear, etc. on the factory floor.
Architecture
Big data repositories have existed in many forms, often built by corporations with a
special need. Commercial vendors historically offered parallel database management
systems for big data beginning in the 1990s. For many years, WinterCorp published a
largest database report.
Teradata Corporation in 1984 marketed the parallel processing DBC 1012 system.
Teradata systems were the first to store and analyze 1 terabyte of data in 1992. Hard
disk drives were 2.5 GB in 1991 so the definition of big data continuously evolves
according to Kryder's Law. Teradata installed the first petabyte class RDBMS based
Page 85 of 206

system in 2007. As of 2017, there are a few dozen petabyte class Teradata relational
databases installed, the largest of which exceeds 50 PB. Systems up until 2008 were
100% structured relational data. Since then, Teradata has added unstructured data
types including XML, JSON, and Avro.
In 2000, Seisint Inc. (now LexisNexis Group) developed a C++-based distributed filesharing
framework for data storage and query. The system stores and distributes
structured, semi-structured, and unstructured data across multiple servers. Users can
build queries in a C++ dialect called ECL. ECL uses an "apply schema on read" method
to infer the structure of stored data when it is queried, instead of when it is stored. In
2004, LexisNexis acquired Seisint Inc. and in 2008 acquired ChoicePoint, Inc. and their
high-speed parallel processing platform. The two platforms were merged into HPCC (or
High-Performance Computing Cluster) Systems and in 2011, HPCC was open-sourced
under the Apache v2.0 License. Quantcast File System was available about the same
time.
CERN and other physics experiments have collected big data sets for many decades,
usually analyzed via high performance computing (supercomputers) rather than the
commodity map-reduce architectures usually meant by the current "big data"
movement.
In 2004, Google published a paper on a process called MapReduce that uses a similar
architecture. The MapReduce concept provides a parallel processing model, and an
associated implementation was released to process huge amounts of data. With
MapReduce, queries are split and distributed across parallel nodes and processed in
parallel (the Map step). The results are then gathered and delivered (the Reduce step).
The framework was very successful, so others wanted to replicate the algorithm.
Therefore, an implementation of the MapReduce framework was adopted by an Apache
open-source project named Hadoop. Apache Spark was developed in 2012 in response
to limitations in the MapReduce paradigm, as it adds the ability to set up many
operations (not just map followed by reduce).
MIKE2.0 is an open approach to information management that acknowledges the need
for revisions due to big data implications identified in an article titled "Big Data Solution
Offering". The methodology addresses handling big data in terms of useful permutations
of data sources, complexity in interrelationships, and difficulty in deleting (or modifying)
individual records.
2012 studies showed that a multiple-layer architecture is one option to address the
issues that big data presents. A distributed parallel architecture distributes data across
multiple servers; these parallel execution environments can dramatically improve data
processing speeds. This type of architecture inserts data into a parallel DBMS, which
implements the use of MapReduce and Hadoop frameworks. This type of framework
looks to make the processing power transparent to the end user by using a front-end
application server.
Page 86 of 206

Big data analytics for manufacturing applications is marketed as a 5C architecture
(connection, conversion, cyber, cognition, and configuration).
The data lake allows an organization to shift its focus from centralized control to a
shared model to respond to the changing dynamics of information management. This
enables quick segregation of data into the data lake, thereby reducing the overhead
time.
Technologies
A 2011 McKinsey Global Institute report characterizes the main components and
ecosystem of big data as follows:
Techniques for analyzing data, such as A/B testing, machine learning and natural
language processing
Big data technologies, like business intelligence, cloud computing and databases
Visualization, such as charts, graphs and other displays of the data
Page 87 of 206

Multidimensional big data can also be represented as tensors, which can be more
efficiently handled by tensor-based computation, such as multilinear subspace learning.
Additional technologies being applied to big data include massively parallel-processing
(MPP) databases, search-based applications, data mining, distributed file systems,
distributed databases, cloud and HPC-based infrastructure (applications, storage and
computing resources) and the Internet. Although, many approaches and technologies
have been developed, it still remains difficult to carry out machine learning with big data.
Some MPP relational databases have the ability to store and manage petabytes of data.
Implicit is the ability to load, monitor, back up, and optimize the use of the large data
tables in the RDBMS.
DARPA's Topological Data Analysis program seeks the fundamental structure of
massive data sets and in 2008 the technology went public with the launch of a company
called Ayasdi.
The practitioners of big data analytics processes are generally hostile to slower shared
storage, preferring direct-attached storage (DAS) in its various forms from solid state
drive (SSD) to high capacity SATA disk buried inside parallel processing nodes. The
perception of shared storage architectures—Storage area network (SAN) and Networkattached
storage (NAS) —is that they are relatively slow, complex, and expensive.
These qualities are not consistent with big data analytics systems that thrive on system
performance, commodity infrastructure, and low cost.
Real or near-real time information delivery is one of the defining characteristics of big
data analytics. Latency is therefore avoided whenever and wherever possible. Data in
memory is good—data on spinning disk at the other end of a FC SAN connection is not.
The cost of a SAN at the scale needed for analytics applications is very much higher
than other storage techniques.
There are advantages as well as disadvantages to shared storage in big data analytics,
but big data analytics practitioners as of 2011 did not favour it.
Applications
Big data has increased the demand of information management specialists so much so
that Software AG, Oracle Corporation, IBM, Microsoft, SAP, EMC, HP and Dell have
spent more than $15 billion on software firms specializing in data management and
analytics. In 2010, this industry was worth more than $100 billion and was growing at
almost 10 percent a year: about twice as fast as the software business as a whole.
Developed economies increasingly use data-intensive technologies. There are
4.6 billion mobile-phone subscriptions worldwide, and between 1 billion and 2 billion
people accessing the internet. Between 1990 and 2005, more than 1 billion people
worldwide entered the middle class, which means more people became more literate,
which in turn led to information growth. The world's effective capacity to exchange
Page 88 of 206

information through telecommunication networks was 281 petabytes in 1986, 471
petabytes in 1993, 2.2 exabytes in 2000, 65 exabytes in 2007 and predictions put the
amount of internet traffic at 667 exabytes annually by 2014. According to one estimate,
one-third of the globally stored information is in the form of alphanumeric text and still
image data, which is the format most useful for most big data applications. This also
shows the potential of yet unused data (i.e. in the form of video and audio content).
While many vendors offer off-the-shelf solutions for big data, experts recommend the
development of in-house solutions custom-tailored to solve the company's problem at
hand if the company has sufficient technical capabilities.
Government
The use and adoption of big data within governmental processes allows efficiencies in
terms of cost, productivity, and innovation, but does not come without its flaws. Data
analysis often requires multiple parts of government (central and local) to work in
collaboration and create new and innovative processes to deliver the desired outcome.
International Development
Research on the effective usage of information and communication technologies for
development (also known as ICT4D) suggests that big data technology can make
Page 89 of 206

important contributions but also present unique challenges to International
development. Advancements in big data analysis offer cost-effective opportunities to
improve decision-making in critical development areas such as health care,
employment, economic productivity, crime, security, and natural disaster and resource
management. Additionally, user-generated data offers new opportunities to give the
unheard a voice. However, longstanding challenges for developing regions such as
inadequate technological infrastructure and economic and human resource scarcity
exacerbate existing concerns with big data such as privacy, imperfect methodology, and
interoperability issues.
Manufacturing
Based on TCS 2013 Global Trend Study, improvements in supply planning and product
quality provide the greatest benefit of big data for manufacturing. Big data provides an
infrastructure for transparency in manufacturing industry, which is the ability to unravel
uncertainties such as inconsistent component performance and availability. Predictive
manufacturing as an applicable approach toward near-zero downtime and transparency
requires vast amount of data and advanced prediction tools for a systematic process of
data into useful information. A conceptual framework of predictive manufacturing begins
with data acquisition where different type of sensory data is available to acquire such as
acoustics, vibration, pressure, current, voltage and controller data. Vast amount of
sensory data in addition to historical data construct the big data in manufacturing. The
generated big data acts as the input into predictive tools and preventive strategies such
as Prognostics and Health Management (PHM).
Healthcare
Big data analytics has helped healthcare improve by providing personalized medicine
and prescriptive analytics, clinical risk intervention and predictive analytics, waste and
care variability reduction, automated external and internal reporting of patient data,
standardized medical terms and patient registries and fragmented point solutions. Some
areas of improvement are more aspirational than actually implemented. The level of
data generated within healthcare systems is not trivial. With the added adoption of
mHealth, eHealth and wearable technologies the volume of data will continue to
increase. This includes electronic health record data, imaging data, patient generated
data, sensor data, and other forms of difficult to process data. There is now an even
greater need for such environments to pay greater attention to data and information
quality. "Big data very often means `dirty data' and the fraction of data inaccuracies
increases with data volume growth." Human inspection at the big data scale is
impossible and there is a desperate need in health service for intelligent tools for
accuracy and believability control and handling of information missed. While extensive
information in healthcare is now electronic, it fits under the big data umbrella as most is
unstructured and difficult to use.
Education
Page 90 of 206

A McKinsey Global Institute study found a shortage of 1.5 million highly trained data
professionals and managers and a number of universities including University of
Tennessee and UC Berkeley, have created masters programs to meet this demand.
Private bootcamps have also developed programs to meet that demand, including free
programs like The Data Incubator or paid programs like General Assembly. In the
specific field of marketing, one of the problems stressed by Wedel and Kannan is that
marketing has several subdomains (e.g., advertising, promotions, product development,
branding) that all use different types of data. Because one-size-fits-all analytical
solutions are not desirable, business schools should prepare marketing managers to
have wide knowledge on all the different techniques used in these subdomains to get a
big picture and work effectively with analysts.
Media
To understand how the media utilizes big data, it is first necessary to provide some
context into the mechanism used for media process. It has been suggested by Nick
Couldry and Joseph Turow that practitioners in Media and Advertising approach big
data as many actionable points of information about millions of individuals. The industry
appears to be moving away from the traditional approach of using specific media
environments such as newspapers, magazines, or television shows and instead taps
into consumers with technologies that reach targeted people at optimal times in optimal
locations. The ultimate aim is to serve or convey, a message or content that is
(statistically speaking) in line with the consumer's mindset. For example, publishing
environments are increasingly tailoring messages (advertisements) and content
(articles) to appeal to consumers that have been exclusively gleaned through various
data-mining activities.
Targeting of consumers (for advertising by marketers)
Data-capture
Data journalism: publishers and journalists use big data tools to provide unique
and innovative insights and infographics.
Channel 4, the British public-service television broadcaster, is a leader in the field of big
data and data analysis.
Page 91 of 206

Internet of Things (IoT)
Big data and the IoT work in conjunction. Data extracted from IoT devices provides a
mapping of device interconnectivity. Such mappings have been used by the media
industry, companies and governments to more accurately target their audience and
increase media efficiency. IoT is also increasingly adopted as a means of gathering
sensory data, and this sensory data has been used in medical and manufacturing
contexts.
Kevin Ashton, digital innovation expert who is credited with coining the term, defines the
Internet of Things in this quote: “If we had computers that knew everything there was to
know about things—using data they gathered without any help from us—we would be
able to track and count everything, and greatly reduce waste, loss and cost. We would
know when things needed replacing, repairing or recalling, and whether they were fresh
or past their best.”
Information Technology
Especially since 2015, big data has come to prominence within Business Operations as
a tool to help employees work more efficiently and streamline the collection and
distribution of Information Technology (IT). The use of big data to resolve IT and data
collection issues within an enterprise is called IT Operations Analytics (ITOA). By
applying big data principles into the concepts of machine intelligence and deep
computing, IT departments can predict potential issues and move to provide solutions
before the problems even happen. In this time, ITOA businesses were also beginning to
play a major role in systems management by offering platforms that brought individual
data silos together and generated insights from the whole of the system rather than
from isolated pockets of data.
Government
United States of America
Case studies
In 2012, the Obama administration announced the Big Data Research and
Development Initiative, to explore how big data could be used to address
important problems faced by the government. The initiative is composed of 84
different big data programs spread across six departments.
Big data analysis played a large role in Barack Obama's successful 2012 reelection
campaign.
The United States Federal Government owns four of the ten most powerful
supercomputers in the world.
Page 92 of 206

The Utah Data Center has been constructed by the United States National
Security Agency. When finished, the facility will be able to handle a large amount
of information collected by the NSA over the Internet. The exact amount of
storage space is unknown, but more recent sources claim it will be on the order
of a few exabytes.
India
Big data analysis was tried out for the BJP to win the Indian General Election
2014.
The Indian government utilizes numerous techniques to ascertain how the Indian
electorate is responding to government action, as well as ideas for policy
augmentation.
United Kingdom
Examples of uses of big data in public services:
Data on prescription drugs: by connecting origin, location and the time of each
prescription, a research unit was able to exemplify the considerable delay
between the release of any given drug, and a UK-wide adaptation of the National
Institute for Health and Care Excellence guidelines. This suggests that new or
most up-to-date drugs take some time to filter through to the general patient.
Page 93 of 206

Joining up data: a local authority blended data about services, such as road
gritting rotas, with services for people at risk, such as 'meals on wheels'. The
connection of data allowed the local authority to avoid any weather-related delay.
Israel
A big data application was designed by Agro Web Lab to aid irrigation regulation.
Personalized diabetic treatments can be created through GlucoMe's big data
solution.
Retail
Walmart handles more than 1 million customer transactions every hour, which
are imported into databases estimated to contain more than 2.5 petabytes (2560
terabytes) of data—the equivalent of 167 times the information contained in all
the books in the US Library of Congress.
Windermere Real Estate uses location information from nearly 100 million drivers
to help new home buyers determine their typical drive times to and from work
throughout various times of the day.
FICO Card Detection System protects accounts worldwide.
Science
The Large Hadron Collider experiments represent about 150 million sensors
delivering data 40 million times per second. There are nearly 600 million
collisions per second. After filtering and refraining from recording more than
99.99995% of these streams, there are 100 collisions of interest per second.
o
o
As a result, only working with less than 0.001% of the sensor stream data,
the data flow from all four LHC experiments represents 25 petabytes
annual rate before replication (as of 2012). This becomes nearly 200
petabytes after replication.
If all sensor data were recorded in LHC, the data flow would be extremely
hard to work with. The data flow would exceed 150 million petabytes
annual rate, or nearly 500 exabytes per day, before replication. To put the
number in perspective, this is equivalent to 500 quintillion (5×10 20 ) bytes
per day, almost 200 times more than all the other sources combined in the
world.
The Square Kilometre Array is a radio telescope built of thousands of antennas.
It is expected to be operational by 2024. Collectively, these antennas are
Page 94 of 206

expected to gather 14 exabytes and store one petabyte per day. It is considered
one of the most ambitious scientific projects ever undertaken.
When the Sloan Digital Sky Survey (SDSS) began to collect astronomical data in
2000, it amassed more in its first few weeks than all data collected in the history
of astronomy previously. Continuing at a rate of about 200 GB per night, SDSS
has amassed more than 140 terabytes of information. When the Large Synoptic
Survey Telescope, successor to SDSS, comes online in 2020, its designers
expect it to acquire that amount of data every five days.
Decoding the human genome originally took 10 years to process, now it can be
achieved in less than a day. The DNA sequencers have divided the sequencing
cost by 10,000 in the last ten years, which is 100 times cheaper than the
reduction in cost predicted by Moore's Law.
The NASA Center for Climate Simulation (NCCS) stores 32 petabytes of climate
observations and simulations on the Discover supercomputing cluster.
Google's DNAStack compiles and organizes DNA samples of genetic data from
around the world to identify diseases and other medical defects. These fast and
exact calculations eliminate any 'friction points,' or human errors that could be
made by one of the numerous science and biology experts working with the
DNA. DNAStack, a part of Google Genomics, allows scientists to use the vast
sample of resources from Google's search server to scale social experiments
that would usually take years, instantly.
23andme's DNA database contains genetic information of over 1,000,000 people
worldwide. The company explores selling the "anonymous aggregated genetic
data" to other researchers and pharmaceutical companies for research purposes
if patients give their consent. Ahmad Hariri, professor of psychology and
neuroscience at Duke University who has been using 23andMe in his research
since 2009 states that the most important aspect of the company's new service is
that it makes genetic research accessible and relatively cheap for scientists. A
study that identified 15 genome sites linked to depression in 23andMe's database
lead to a surge in demands to access the repository with 23andMe fielding nearly
20 requests to access the depression data in the two weeks after publication of
the paper.
Computational Fluid Dynamics (CFD) and hydrodynamic turbulence research
generate massive datasets. The Johns Hopkins Turbulence Databases (JHTDB)
contains over 350 terabytes of spatiotemporal fields from Direct Numerical
simulations of various turbulent flows. Such data have been difficult to share
using traditional methods such as downloading flat simulation output files. The
data within JHTDB can be accessed using "virtual sensors" with various access
modes ranging from direct web-browser queries, access through Matlab, Python,
Page 95 of 206

Sports
Fortran and C programs executing on clients' platforms, to cut out services to
download raw data. The data have been used in over 150 scientific publications.
Big data can be used to improve training and understanding competitors, using sport
sensors. It is also possible to predict winners in a match using big data analytics. Future
performance of players could be predicted as well. Thus, players' value and salary is
determined by data collected throughout the season.
The movie MoneyBall demonstrates how big data could be used to scout players and
also identify undervalued players.
In Formula One races, race cars with hundreds of sensors generate terabytes of data.
These sensors collect data points from tire pressure to fuel burn efficiency. Based on
the data, engineers and data analysts decide whether adjustments should be made in
order to win a race. Besides, using big data, race teams try to predict the time they will
finish the race beforehand, based on simulations using data collected over the season.
Technology
eBay.com uses two data warehouses at 7.5 petabytes and 40PB as well as a
40PB Hadoop cluster for search, consumer recommendations, and
merchandising.
Amazon.com handles millions of back-end operations every day, as well as
queries from more than half a million third-party sellers. The core technology that
keeps Amazon running is Linux-based and as of 2005 they had the world's three
largest Linux databases, with capacities of 7.8 TB, 18.5 TB, and 24.7 TB.
Facebook handles 50 billion photos from its user base.
Google was handling roughly 100 billion searches per month as of August 2012.
Research Activities
Encrypted search and cluster formation in big data were demonstrated in March 2014 at
the American Society of Engineering Education. Gautam Siwach engaged at Tackling
the challenges of Big Data by MIT Computer Science and Artificial Intelligence
Laboratory and Dr. Amir Esmailpour at UNH Research Group investigated the key
features of big data as the formation of clusters and their interconnections. They
focused on the security of big data and the orientation of the term towards the presence
of different type of data in an encrypted form at cloud interface by providing the raw
definitions and real time examples within the technology. Moreover, they proposed an
approach for identifying the encoding technique to advance towards an expedited
search over encrypted text leading to the security enhancements in big data.
Page 96 of 206

In March 2012, The White House announced a national "Big Data Initiative" that
consisted of six Federal departments and agencies committing more than $200 million
to big data research projects.
The initiative included a National Science Foundation "Expeditions in Computing" grant
of $10 million over 5 years to the AMPLab at the University of California, Berkeley. The
AMPLab also received funds from DARPA, and over a dozen industrial sponsors and
uses big data to attack a wide range of problems from predicting traffic congestion to
fighting cancer.
The White House Big Data Initiative also included a commitment by the Department of
Energy to provide $25 million in funding over 5 years to establish the Scalable Data
Management, Analysis and Visualization (SDAV) Institute, led by the Energy
Department’s Lawrence Berkeley National Laboratory. The SDAV Institute aims to bring
together the expertise of six national laboratories and seven universities to develop new
tools to help scientists manage and visualize data on the Department's supercomputers.
The U.S. state of Massachusetts announced the Massachusetts Big Data Initiative in
May 2012, which provides funding from the state government and private companies to
a variety of research institutions. The Massachusetts Institute of Technology hosts the
Intel Science and Technology Center for Big Data in the MIT Computer Science and
Artificial Intelligence Laboratory, combining government, corporate, and institutional
funding and research efforts.
The European Commission is funding the 2-year-long Big Data Public Private Forum
through their Seventh Framework Program to engage companies, academics and other
stakeholders in discussing big data issues. The project aims to define a strategy in
Page 97 of 206

terms of research and innovation to guide supporting actions from the European
Commission in the successful implementation of the big data economy. Outcomes of
this project will be used as input for Horizon 2020, their next framework program.
The British government announced in March 2014 the founding of the Alan Turing
Institute, named after the computer pioneer and code-breaker, which will focus on new
ways to collect and analyse large data sets.
At the University of Waterloo Stratford Campus Canadian Open Data Experience
(CODE) Inspiration Day, participants demonstrated how using data visualization can
increase the understanding and appeal of big data sets and communicate their story to
the world.
To make manufacturing more competitive in the United States (and globe), there is a
need to integrate more American ingenuity and innovation into manufacturing ;
Therefore, National Science Foundation has granted the Industry University cooperative
research center for Intelligent Maintenance Systems (IMS) at university of Cincinnati to
focus on developing advanced predictive tools and techniques to be applicable in a big
data environment. In May 2013, IMS Center held an industry advisory board meeting
focusing on big data where presenters from various industrial companies discussed
their concerns, issues and future goals in big data environment.
Computational social sciences – Anyone can use Application Programming Interfaces
(APIs) provided by big data holders, such as Google and Twitter, to do research in the
social and behavioral sciences. Often these APIs are provided for free. Tobias Preis et
al. used Google Trends data to demonstrate that Internet users from countries with a
higher per capita gross domestic product (GDP) are more likely to search for
information about the future than information about the past.
The findings suggest there may be a link between online behaviour and real-world
economic indicators. The authors of the study examined Google queries logs made by
ratio of the volume of searches for the coming year ('2011') to the volume of searches
for the previous year ('2009'), which they call the 'future orientation index'. They
compared the future orientation index to the per capita GDP of each country, and found
a strong tendency for countries where Google users inquire more about the future to
have a higher GDP. The results hint that there may potentially be a relationship
between the economic success of a country and the information-seeking behavior of its
citizens captured in big data.
Tobias Preis and his colleagues Helen Susannah Moat and H. Eugene Stanley
introduced a method to identify online precursors for stock market moves, using trading
strategies based on search volume data provided by Google Trends. Their analysis of
Google search volume for 98 terms of varying financial relevance, published in Scientific
Reports, suggests that increases in search volume for financially relevant search terms
tend to precede large losses in financial markets.
Page 98 of 206

Big data sets come with algorithmic challenges that previously did not exist. Hence,
there is a need to fundamentally change the processing ways.
The Workshops on Algorithms for Modern Massive Data Sets (MMDS) bring together
computer scientists, statisticians, mathematicians, and data analysis practitioners to
discuss algorithmic challenges of big data.
Sampling Big Data
An important research question that can be asked about big data sets is whether you
need to look at the full data to draw certain conclusions about the properties of the data
or is a sample good enough. The name big data itself contains a term related to size
and this is an important characteristic of big data. But Sampling (statistics) enables the
selection of right data points from within the larger data set to estimate the
characteristics of the whole population. For example, there are about 600 million tweets
produced every day. Is it necessary to look at all of them to determine the topics that
are discussed during the day? Is it necessary to look at all the tweets to determine the
sentiment on each of the topics? In manufacturing different types of sensory data such
as acoustics, vibration, pressure, current, voltage and controller data are available at
short time intervals. To predict downtime it may not be necessary to look at all the data
but a sample may be sufficient. Big Data can be broken down by various data point
categories such as demographic, psychographic, behavioral, and transactional data.
With large sets of data points, marketers are able to create and utilize more customized
segments of consumers for more strategic targeting.
There has been some work done in Sampling algorithms for big data. A theoretical
formulation for sampling Twitter data has been developed.
Page 99 of 206

Critique
Critiques of the big data paradigm come in two flavors, those that question the
implications of the approach itself, and those that question the way it is currently done.
One approach to this criticism is the field of Critical data studies.
Critiques of the Big Data Paradigm
"A crucial problem is that we do not know much about the underlying empirical microprocesses
that lead to the emergence of the[se] typical network characteristics of Big
Data". In their critique, Snijders, Matzat, and Reips point out that often very strong
assumptions are made about mathematical properties that may not at all reflect what is
really going on at the level of micro-processes. Mark Graham has leveled broad
critiques at Chris Anderson's assertion that big data will spell the end of theory: focusing
in particular on the notion that big data must always be contextualized in their social,
economic, and political contexts. Even as companies invest eight- and nine-figure sums
to derive insight from information streaming in from suppliers and customers, less than
40% of employees have sufficiently mature processes and skills to do so. To overcome
this insight deficit, big data, no matter how comprehensive or well analysed, must be
complemented by "big judgment," according to an article in the Harvard Business
Review.
Much in the same line, it has been pointed out that the decisions based on the analysis
of big data are inevitably "informed by the world as it was in the past, or, at best, as it
currently is". Fed by a large number of data on past experiences, algorithms can predict
future development if the future is similar to the past. If the systems dynamics of the
future change (if it is not a stationary process), the past can say little about the future. In
order to make predictions in changing environments, it would be necessary to have a
thorough understanding of the systems dynamic, which requires theory. As a response
to this critique Alemany Oliver and Vayre suggested to use "abductive reasoning as a
first step in the research process in order to bring context to consumers’ digital traces
and make new theories emerge". Additionally, it has been suggested to combine big
data approaches with computer simulations, such as agent-based models and Complex
Systems. Agent-based models are increasingly getting better in predicting the outcome
of social complexities of even unknown future scenarios through computer simulations
that are based on a collection of mutually interdependent algorithms. Finally, use of
multivariate methods that probe for the latent structure of the data, such as factor
analysis and cluster analysis, have proven useful as analytic approaches that go well
beyond the bi-variate approaches (cross-tabs) typically employed with smaller data
sets.
In health and biology, conventional scientific approaches are based on experimentation.
For these approaches, the limiting factor is the relevant data that can confirm or refute
the initial hypothesis. A new postulate is accepted now in biosciences: the information
provided by the data in huge volumes (omics) without prior hypothesis is
complementary and sometimes necessary to conventional approaches based on
Page 100 of 206

experimentation. In the massive approaches it is the formulation of a relevant
hypothesis to explain the data that is the limiting factor. The search logic is reversed
and the limits of induction ("Glory of Science and Philosophy scandal", C. D. Broad,
1926) are to be considered.
Privacy advocates are concerned about the threat to privacy represented by increasing
storage and integration of personally identifiable information; expert panels have
released various policy recommendations to conform practice to expectations of
privacy.
Nayef Al-Rodhan argues that a new kind of social contract will be needed to protect
individual liberties in a context of Big Data and giant corporations that own vast amounts
of information. The use of Big Data should be monitored and better regulated at the
national and international levels. Barocas and Nissenbaum argue that one way of
protecting individual users is by being informed about the types of information being
collected, with whom it is shared, under what constrains and for what purposes.
Critiques of the 'V' Model
The 'V' model of Big Data is concerting as it centres around computational scalability
and lacks in a loss around the perceptibility and understandability of information. This
Page 101 of 206

led to the framework of Cognitive Big Data, which characterises Big Data application
according to:
Data completeness: understanding of the non-obvious from data;
Data correlation, causation, and predictability: causality as not essential
requirement to achieve predictability;
Explainability and interpretability: humans desire to understand and accept what
they understand, where algorithms don't cope with this;
Level of automated decision making: algorithms that support automated decision
making and algorithmic self-learning;
Critiques of Novelty
Large data sets have been analyzed by computing machines for well over a century,
including the 1890s US census analytics performed by IBM's punch card machines
which computed statistics including means and variances of populations across the
whole continent. In more recent decades, science experiments such as CERN have
produced data on similar scales to current commercial "big data". However science
experiments have tended to analyse their data using specialized custom-built high
performance computing (supercomputing) clusters and grids, rather than clouds of
cheap commodity computers as in the current commercial wave, implying a difference
in both culture and technology stack.
Critiques of Big Data Execution
Ulf-Dietrich Reips and Uwe Matzat wrote in 2014 that big data had become a "fad" in
scientific research. Researcher Danah Boyd has raised concerns about the use of big
data in science neglecting principles such as choosing a representative sample by
being too concerned about handling the huge amounts of data. This approach may lead
to results bias in one way or another. Integration across heterogeneous data
resources—some that might be considered big data and others not—presents
formidable logistical as well as analytical challenges, but many researchers argue that
such integrations are likely to represent the most promising new frontiers in science. In
the provocative article "Critical Questions for Big Data", the authors title big data a part
of mythology: "large data sets offer a higher form of intelligence and knowledge [...], with
the aura of truth, objectivity, and accuracy". Users of big data are often "lost in the sheer
volume of numbers", and "working with Big Data is still subjective, and what it quantifies
does not necessarily have a closer claim on objective truth". Recent developments in BI
domain, such as pro-active reporting especially target improvements in usability of big
data, through automated filtering of non-useful data and correlations.
Page 102 of 206

Big data analysis is often shallow compared to analysis of smaller data sets. In many
big data projects, there is no large data analysis happening, but the challenge is the
extract, transform, load part of data preprocessing.
Big data is a buzzword and a "vague term", but at the same time an "obsession" with
entrepreneurs, consultants, scientists and the media. Big data showcases such as
Google Flu Trends failed to deliver good predictions in recent years, overstating the flu
outbreaks by a factor of two. Similarly, Academy awards and election predictions solely
based on Twitter were more often off than on target. Big data often poses the same
challenges as small data; adding more data does not solve problems of bias, but may
emphasize other problems. In particular data sources such as Twitter are not
representative of the overall population, and results drawn from such sources may then
lead to wrong conclusions. Google Translate—which is based on big data statistical
analysis of text—does a good job at translating web pages. However, results from
specialized domains may be dramatically skewed. On the other hand, big data may also
introduce new problems, such as the multiple comparisons problem: simultaneously
testing a large set of hypotheses is likely to produce many false results that mistakenly
appear significant. Ioannidis argued that "most published research findings are false"
due to essentially the same effect: when many scientific teams and researchers each
perform many experiments (i.e. process a big amount of scientific data; although not
with big data technology), the likelihood of a "significant" result being false grows fast –
even more so, when only positive results are published. Furthermore, big data analytics
results are only as good as the model on which they are predicated. In an example, big
data took part in attempting to predict the results of the 2016 U.S. Presidential Election
with varying degrees of success. Forbes predicted "If you believe in Big Data analytics,
it’s time to begin planning for a Hillary Clinton presidency and all that entails.".
______
Data Analysis
Data analysis is a process of inspecting, cleansing, transforming, and modeling data
with the goal of discovering useful information, suggesting conclusions, and supporting
decision-making. Data analysis has multiple facets and approaches, encompassing
diverse techniques under a variety of names, in different business, science, and social
science domains.
Data mining is a particular data analysis technique that focuses on modeling and
knowledge discovery for predictive rather than purely descriptive purposes, while
business intelligence covers data analysis that relies heavily on aggregation, focusing
on business information. In statistical applications, data analysis can be divided into
descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis
(CDA). EDA focuses on discovering new features in the data and CDA on confirming or
falsifying existing hypotheses. Predictive analytics focuses on application of statistical
models for predictive forecasting or classification, while text analytics applies statistical,
Page 103 of 206

linguistic, and structural techniques to extract and classify information from textual
sources, a species of unstructured data. All are varieties of data analysis.
Data integration is a precursor to data analysis, and data analysis is closely linked to
data visualization and data dissemination. The term data analysis is sometimes used as
a synonym for data modeling.
The Process of Data Analysis
Data science process flowchart
from "Doing Data Science",
Cathy O'Neil and Rachel
Schutt, 2013
Analysis refers to breaking a
whole into its separate
components for individual
examination. Data analysis is a
process for obtaining raw data
and converting it into
information useful for decisionmaking
by users. Data is
collected and analyzed to answer questions, test hypotheses or disprove theories.
Statistician John Tukey defined data analysis in 1961 as: "Procedures for analyzing
data, techniques for interpreting the results of such procedures, ways of planning the
gathering of data to make its analysis easier, more precise or more accurate, and all the
machinery and results of (mathematical) statistics which apply to analyzing data."
There are several phases that can be distinguished, described below. The phases are
iterative, in that feedback from later phases may result in additional work in earlier
phases.
Data Requirements
The data is necessary as inputs to the analysis, which is specified based upon the
requirements of those directing the analysis or customers (who will use the finished
product of the analysis). The general type of entity upon which the data will be collected
is referred to as an experimental unit (e.g., a person or population of people). Specific
variables regarding a population (e.g., age and income) may be specified and obtained.
Data may be numerical or categorical (i.e., a text label for numbers).
Data Collection
Data is collected from a variety of sources. The requirements may be communicated by
analysts to custodians of the data, such as information technology personnel within an
Page 104 of 206

organization. The data may also be collected from sensors in the environment, such as
traffic cameras, satellites, recording devices, etc. It may also be obtained through
interviews, downloads from online sources, or reading documentation.
Data Processing
The phases of the intelligence cycle used to convert raw information into actionable
intelligence or knowledge are conceptually similar to the phases in data analysis.
Data initially obtained must be processed or organised for analysis. For instance, these
may involve placing data into rows and columns in a table format (i.e., structured data)
for further analysis, such as
within a spreadsheet or
statistical software.
Data Cleaning
Once processed and
organised, the data may be
incomplete, contain
duplicates, or contain errors.
The need for data cleaning
will arise from problems in
the way that data is entered
and stored. Data cleaning is
the process of preventing
and correcting these errors.
Common tasks include
record matching, identifying
inaccuracy of data, overall quality of existing data, deduplication, and column
segmentation. Such data problems can also be identified through a variety of analytical
techniques. For example, with financial information, the totals for particular variables
may be compared against separately published numbers believed to be reliable.
Unusual amounts above or below pre-determined thresholds may also be reviewed.
There are several types of data cleaning that depend on the type of data such as phone
numbers, email addresses, employers etc. Quantitative data methods for outlier
detection can be used to get rid of likely incorrectly entered data. Textual data spell
checkers can be used to lessen the amount of mistyped words, but it is harder to tell if
the words themselves are correct.
Exploratory Data Analysis
Once the data is cleaned, it can be analyzed. Analysts may apply a variety of
techniques referred to as exploratory data analysis to begin understanding the
messages contained in the data. The process of exploration may result in additional
data cleaning or additional requests for data, so these activities may be iterative in
nature. Descriptive statistics, such as the average or median, may be generated to help
Page 105 of 206

understand the data. Data visualization may also be used to examine the data in
graphical format, to obtain additional insight regarding the messages within the data.
Modeling and Algorithms
Mathematical formulas or models called algorithms may be applied to the data to
identify relationships among the variables, such as correlation or causation. In general
terms, models may be developed to evaluate a particular variable in the data based on
other variable(s) in the data, with some residual error depending on model accuracy
(i.e., Data = Model + Error).
Inferential statistics includes techniques to measure relationships between particular
variables. For example, regression analysis may be used to model whether a change in
advertising (independent variable X) explains the variation in sales (dependent variable
Y). In mathematical terms, Y (sales) is a function of X (advertising).
It may be described as Y = aX + b + error, where the model is designed such that a and
b minimize the error when the model predicts Y for a given range of values of X.
Analysts may attempt to build models that are descriptive of the data to simplify analysis
and communicate results.
Data Product
A data product is a computer application that takes data inputs and generates outputs,
feeding them back into the environment. It may be based on a model or algorithm. An
example is an application that analyzes data about customer purchasing history and
recommends other purchases the customer might enjoy.
Communication
Data visualization to understand the results of a data analysis.
Once the data is analyzed, it may be reported in many formats to the users of the
analysis to support their requirements. The users may have feedback, which results in
additional analysis. As such, much of the analytical cycle is iterative.
When determining how to communicate the results, the analyst may consider data
visualization techniques to help clearly and efficiently communicate the message to the
audience. Data visualization uses information displays (such as tables and charts) to
help communicate key messages contained in the data.
Tables are helpful to a user who might lookup specific numbers, while charts (e.g., bar
charts or line charts) may help explain the quantitative messages contained in the data.
Page 106 of 206

Quantitative Messages
A time series illustrated with a line chart demonstrating trends in U.S. federal spending
and revenue over time.
A scatterplot illustrating correlation between two variables (inflation and unemployment)
measured at points in time.
Stephen Few described eight types of quantitative messages that users may attempt to
understand or communicate from a set of data and the associated graphs used to help
communicate the message. Customers specifying requirements and analysts
performing the data analysis may consider these messages during the course of the
process.
1. Time-Series: A single variable is captured over a period of time, such as the
unemployment rate over a 10-year period. A line chart may be used to
demonstrate the trend.
2. Ranking: Categorical subdivisions are ranked in ascending or descending order,
such as a ranking of sales performance (the measure) by sales persons (the
category, with each sales person a categorical subdivision) during a single
period. A bar chart may be used to show the comparison across the sales
persons.
3. Part-to-Whole: Categorical subdivisions are measured as a ratio to the whole
(i.e., a percentage out of 100%). A pie chart or bar chart can show the
comparison of ratios, such as the market share represented by competitors in a
market.
Page 107 of 206

4. Deviation: Categorical subdivisions are compared against a reference, such as a
comparison of actual vs. budget expenses for several departments of a business
for a given time period. A bar chart can show comparison of the actual versus the
reference amount.
5. Frequency Distribution: Shows the number of observations of a particular
variable for given interval, such as the number of years in which the stock market
return is between intervals such as 0–10%, 11–20%, etc. A histogram, a type of
bar chart, may be used for this analysis.
6. Correlation: Comparison between observations represented by two variables
(X,Y) to determine if they tend to move in the same or opposite directions. For
example, plotting unemployment (X) and inflation (Y) for a sample of months. A
scatter plot is typically used for this message.
7. Nominal Comparison: Comparing categorical subdivisions in no particular
order, such as the sales volume by product code. A bar chart may be used for
this comparison.
8. Geographic or Geospatial: Comparison of a variable across a map or layout,
such as the unemployment rate by state or the number of persons on the various
floors of a building. A cartogram is a typical graphic used.
Techniques for Analyzing Quantitative Data
Author Jonathan Koomey has recommended a series of best practices for
understanding quantitative data. These include:
Check raw data for anomalies prior to performing your analysis;
Re-perform important calculations, such as verifying columns of data that are
formula driven;
Confirm main totals are the sum of subtotals;
Check relationships between numbers that should be related in a predictable
way, such as ratios over time;
Normalize numbers to make comparisons easier, such as analyzing amounts per
person or relative to GDP or as an index value relative to a base year;
Break problems into component parts by analyzing factors that led to the results,
such as DuPont analysis of return on equity.
Page 108 of 206

For the variables under examination, analysts typically obtain descriptive statistics for
them, such as the mean (average), median, and standard deviation. They may also
analyze the distribution of the key variables to see how the individual values cluster
around the mean.
The consultants at McKinsey and Company named a technique for breaking a
quantitative problem down into its component parts called the MECE principle. Each
layer can be broken down into its components; each of the sub-components must be
mutually exclusive of each other and collectively add up to the layer above them. The
relationship is referred to as "Mutually Exclusive and Collectively Exhaustive" or MECE.
For example, profit by definition can be broken down into total revenue and total cost. In
turn, total revenue can be analyzed by its components, such as revenue of divisions A,
B, and C (which are mutually exclusive of each other) and should add to the total
revenue (collectively exhaustive).
Analysts may use robust statistical measurements to solve certain analytical problems.
Hypothesis testing is used when a particular hypothesis about the true state of affairs is
made by the analyst and data is gathered to determine whether that state of affairs is
true or false. For example, the hypothesis might be that "Unemployment has no effect
on inflation", which relates to an economics concept called the Phillips Curve.
Hypothesis testing involves considering the likelihood of Type I and type II errors, which
relate to whether the data supports accepting or rejecting the hypothesis.
Regression analysis may be used when the analyst is trying to determine the extent to
which independent variable X affects dependent variable Y (e.g., "To what extent do
changes in the unemployment rate (X) affect the inflation rate (Y)?"). This is an attempt
to model or fit an equation line or curve to the data, such that Y is a function of X.
Necessary condition analysis (NCA) may be used when the analyst is trying to
determine the extent to which independent variable X allows variable Y (e.g., "To what
extent is a certain unemployment rate (X) necessary for a certain inflation rate (Y)?").
Whereas (multiple) regression analysis uses additive logic where each X-variable can
produce the outcome and the X's can compensate for each other (they are sufficient but
not necessary), necessary condition analysis (NCA) uses necessity logic, where one or
more X-variables allow the outcome to exist, but may not produce it (they are necessary
but not sufficient). Each single necessary condition must be present and compensation
is not possible.
Analytical Activities of Data Users
Users may have particular data points of interest within a data set, as opposed to
general messaging outlined above. Such low-level user analytic activities are presented
in the following table.
The taxonomy can also be organized by three poles of activities: retrieving values,
finding data points, and arranging data points.
Page 109 of 206

# Task General
Description
1 Retrieve Value Given a set of
specific
cases, find
attributes of
those cases.
2 Filter Given some
concrete
conditions on
attribute
values, find
data cases
satisfying
those
conditions.
3 Compute Derived Given a set of
Value
data cases,
compute an
aggregate
numeric
representation
of those data
cases.
4 Find Extremum Find data
cases
possessing an
extreme value
of an attribute
over its range
within the
data set.
5 Sort Given a set of
data cases,
rank them
according to
some ordinal
metric.
6 Determine Range Given a set of
data cases
and an
attribute of
interest, find
the span of
values within
the set.
7 Characterize Given a set of
Distribution data cases
and a
quantitative
attribute of
interest,
characterize
the
distribution of
Pro
Forma
Abstract
What are the values of
attributes {X, Y, Z, ...}
in the data cases {A, B,
C, ...}?
Which data cases
satisfy conditions {A, B,
C...}?
What is the value of
aggregation function F
over a given set S of
data cases?
What are the
top/bottom N data
cases with respect to
attribute A?
What is the sorted
order of a set S of data
cases according to
their value of attribute
A?
What is the range of
values of attribute A in
a set S of data cases?
What is the distribution
of values of attribute A
in a set S of data
cases?
Examples
- What is the mileage per gallon of
the Ford Mondeo?
- How long is the movie Gone with
the Wind?
- What Kellogg's cereals have high
fiber?
- What comedies have won
awards?
- Which funds underperformed the
SP-500?
- What is the average calorie
content of Post cereals?
- What is the gross income of all
stores combined?
- How many manufacturers of cars
are there?
- What is the car with the highest
MPG?
- What director/film has won the
most awards?
- What Marvel Studios film has the
most recent release date?
- Order the cars by weight.
- Rank the cereals by calories.
- What is the range of film lengths?
- What is the range of car
horsepowers?
- What actresses are in the data
set?
- What is the distribution of
carbohydrates in cereals?
- What is the age distribution of
shoppers?
Page 110 of 206

that attribute’s
values over
the set.
8 Find Anomalies Identify any
anomalies
within a given
set of data
cases with
respect to a
given
relationship or
expectation,
e.g. statistical
outliers.
9 Cluster Given a set of
data cases,
find clusters
of similar
attribute
values.
10 Correlate Given a set of
data cases
and two
attributes,
determine
useful
relationships
between the
values of
those
attributes.
11 Contextualization Given a set of
[17]
data cases,
find
contextual
relevancy of
the data to the
users.
Barriers to Effective Analysis
Which data cases in a
set S of data cases
have
unexpected/exceptional
values?
Which data cases in a
set S of data cases are
similar in value for
attributes {X, Y, Z, ...}?
What is the correlation
between attributes X
and Y over a given set
S of data cases?
Which data cases in a
set S of data cases are
relevant to the current
users' context?
- Are there exceptions to the
relationship between horsepower
and acceleration?
- Are there any outliers in protein?
- Are there groups of cereals w/
similar fat/calories/sugar?
- Is there a cluster of typical film
lengths?
- Is there a correlation between
carbohydrates and fat?
- Is there a correlation between
country of origin and MPG?
- Do different genders have a
preferred payment method?
- Is there a trend of increasing film
length over the years?
- Are there groups of restaurants
that have foods based on my
current caloric intake?
Barriers to effective analysis may exist among the analysts performing the data analysis
or among the audience. Distinguishing fact from opinion, cognitive biases, and
innumeracy are all challenges to sound data analysis.
Confusing Fact and Opinion
You are entitled to your own opinion, but you are not entitled to your own facts.
- Daniel Patrick Moynihan
Effective analysis requires obtaining relevant facts to answer questions, support a
conclusion or formal opinion, or test hypotheses. Facts by definition are irrefutable,
Page 111 of 206

meaning that any person involved in the analysis should be able to agree upon them.
For example, in August 2010, the Congressional Budget Office (CBO) estimated that
extending the Bush tax cuts of 2001 and 2003 for the 2011–2020 time period would add
approximately $3.3 trillion to the national debt. Everyone should be able to agree that
indeed this is what CBO reported; they can all examine the report. This makes it a fact.
Whether persons agree or disagree with the CBO is their own opinion.
As another example, the auditor of a public company must arrive at a formal opinion on
whether financial statements of publicly traded corporations are "fairly stated, in all
material respects." This requires extensive analysis of factual data and evidence to
support their opinion. When making the leap from facts to opinions, there is always the
possibility that the opinion is erroneous.
Cognitive Biases
There are a variety of cognitive biases that can adversely affect analysis. For example,
confirmation bias is the tendency to search for or interpret information in a way that
confirms one's preconceptions. In addition, individuals may discredit information that
does not support their views.
Analysts may be trained specifically to be aware of these biases and how to overcome
them. In his book Psychology of Intelligence Analysis, retired CIA analyst Richards
Heuer wrote that analysts should clearly delineate their assumptions and chains of
inference and specify the degree and source of the uncertainty involved in the
conclusions. He emphasized procedures to help surface and debate alternative points
of view.
Innumeracy
Effective analysts are generally adept with a variety of numerical techniques. However,
audiences may not have such literacy with numbers or numeracy; they are said to be
innumerate. Persons communicating the data may also be attempting to mislead or
misinform, deliberately using bad numerical techniques.
For example, whether a number is rising or falling may not be the key factor. More
important may be the number relative to another number, such as the size of
government revenue or spending relative to the size of the economy (GDP) or the
amount of cost relative to revenue in corporate financial statements. This numerical
technique is referred to as normalization or common-sizing. There are many such
techniques employed by analysts, whether adjusting for inflation (i.e., comparing real vs.
nominal data) or considering population increases, demographics, etc. Analysts apply a
variety of techniques to address the various quantitative messages described in the
section above.
Analysts may also analyze data under different assumptions or scenarios. For example,
when analysts perform financial statement analysis, they will often recast the financial
Page 112 of 206

statements under different assumptions to help arrive at an estimate of future cash flow,
which they then discount to present value based on some interest rate, to determine the
valuation of the company or its stock. Similarly, the CBO analyzes the effects of various
policy options on the government's revenue, outlays and deficits, creating alternative
future scenarios for key measures.
Smart Buildings
Other Topics
A data analytics approach can be used in order to predict energy consumption in
buildings. The different steps of the data analysis process are carried out in order to
realise smart buildings, where the building management and control operations
including heating, ventilation, air conditioning, lighting and security are realised
automatically by miming the needs of the building users and optimising resources like
energy and time.
Analytics and Business Intelligence
Analytics is the "extensive use of data, statistical and quantitative analysis, explanatory
and predictive models, and fact-based management to drive decisions and actions." It is
a subset of business intelligence, which is a set of technologies and processes that use
data to understand and analyze business performance.
Education
In education, most educators have access to a data system for the purpose of analyzing
student data. These data systems present data to educators in an over-the-counter data
format (embedding labels, supplemental documentation, and a help system and making
key package/display and content decisions) to improve the accuracy of educators’ data
analyses.
Practitioner Notes
This section contains rather technical explanations that may assist practitioners but are
beyond the typical scope of a Wikipedia article.
Initial Data Analysis
The most important distinction between the initial data analysis phase and the main
analysis phase, is that during initial data analysis one refrains from any analysis that is
aimed at answering the original research question. The initial data analysis phase is
guided by the following four questions:
Page 113 of 206

Quality of Data
The quality of the data should be checked as early as possible. Data quality can be
assessed in several ways, using different types of analysis: frequency counts,
descriptive statistics (mean, standard deviation, median), normality (skewness, kurtosis,
frequency histograms, n: variables are compared with coding schemes of variables
external to the data set, and possibly corrected if coding schemes are not comparable.
Test for common-method variance.
The choice of analyses to assess the data quality during the initial data analysis phase
depends on the analyses that will be conducted in the main analysis phase.
Quality of Measurements
The quality of the measurement instruments should only be checked during the initial
data analysis phase when this is not the focus or research question of the study. One
should check whether structure of measurement instruments corresponds to structure
reported in the literature.
There are two ways to assess measurement: [NOTE: only one way seems to be listed]
Analysis of homogeneity (internal consistency), which gives an indication of the
reliability of a measurement instrument. During this analysis, one inspects the
variances of the items and the scales, the Cronbach's α of the scales, and the
change in the Cronbach's alpha when an item would be deleted from a scale
Initial Transformations
After assessing the quality of the data and of the measurements, one might decide to
impute missing data, or to perform initial transformations of one or more variables,
although this can also be done during the main analysis phase.
Possible transformations of variables are:
Square root transformation (if the distribution differs moderately from normal)
Log-transformation (if the distribution differs substantially from normal)
Inverse transformation (if the distribution differs severely from normal)
Make categorical (ordinal / dichotomous) (if the distribution differs severely from
normal, and no transformations help)
Did the implementation of the study fulfill the intentions of the research design?
One should check the success of the randomization procedure, for instance by checking
whether background and substantive variables are equally distributed within and across
Page 114 of 206

groups.
If the study did not need or use a randomization procedure, one should check the
success of the non-random sampling, for instance by checking whether all subgroups of
the population of interest are represented in sample.
Other possible data distortions that should be checked are:
dropout (this should be identified during the initial data analysis phase)
Item nonresponse (whether this is random or not should be assessed during the
initial data analysis phase)
Treatment quality (using manipulation checks).
Characteristics of Data Sample
In any report or article, the structure of the sample must be accurately described. It is
especially important to exactly determine the structure of the sample (and specifically
the size of the subgroups) when subgroup analyses will be performed during the main
analysis phase.
The characteristics of the data sample can be assessed by looking at:
Basic statistics of important variables
Scatter plots
Correlations and associations
Cross-tabulations
Final Stage of The Initial Data Analysis
During the final stage, the findings of the initial data analysis are documented, and
necessary, preferable, and possible corrective actions are taken.
Also, the original plan for the main data analyses can and should be specified in more
detail or rewritten.
In order to do this, several decisions about the main data analyses can and should be
made:
In the case of non-normals: should one transform variables; make variables
categorical (ordinal/dichotomous); adapt the analysis method?
In the case of missing data: should one neglect or impute the missing data; which
imputation technique should be used?
In the case of outliers: should one use robust analysis techniques?
Page 115 of 206

In case items do not fit the scale: should one adapt the measurement instrument
by omitting items, or rather ensure comparability with other (uses of the)
measurement instrument(s)?
In the case of (too) small subgroups: should one drop the hypothesis about intergroup
differences, or use small sample techniques, like exact tests or
bootstrapping?
In case the randomization procedure seems to be defective: can and should one
calculate propensity scores and include them as covariates in the main
analyses?
Analysis
Several analyses can be used during the initial data analysis phase:
Univariate statistics (single variable)
Bivariate associations (correlations)
Graphical techniques (scatter plots)
It is important to take the measurement levels of the variables into account for the
analyses, as special statistical techniques are available for each level:
Nominal and ordinal variables
o
o
Frequency counts (numbers and percentages)
Associations
• circumambulations (crosstabulations)
• hierarchical loglinear analysis (restricted to a maximum of 8
variables)
• loglinear analysis (to identify relevant/important variables and
possible confounders)
o
o
Exact tests or bootstrapping (in case subgroups are small)
Computation of new variables
Continuous variables
o
Distribution
• Statistics (M, SD, variance, skewness, kurtosis)
• Stem-and-leaf displays
• Box plots
Page 116 of 206

Nonlinear Analysis
Nonlinear analysis will be necessary when the data is recorded from a nonlinear
system. Nonlinear systems can exhibit complex dynamic effects including bifurcations,
chaos, harmonics and subharmonics that cannot be analyzed using simple linear
methods. Nonlinear data analysis is closely related to nonlinear system identification.
Main Data Analysis
In the main analysis phase analyses aimed at answering the research question are
performed as well as any other relevant analysis needed to write the first draft of the
research report.
Exploratory and Confirmatory Approaches
In the main analysis phase either an exploratory or confirmatory approach can be
adopted. Usually the approach is decided before data is collected. In an exploratory
analysis no clear hypothesis is stated before analysing the data, and the data is
searched for models that describe the data well. In a confirmatory analysis clear
hypotheses about the data are tested.
Exploratory data analysis should be interpreted carefully. When testing multiple models
at once there is a high chance on finding at least one of them to be significant, but this
can be due to a type 1 error. It is important to always adjust the significance level when
testing multiple models with, for example, a Bonferroni correction. Also, one should not
follow up an exploratory analysis with a confirmatory analysis in the same dataset. An
exploratory analysis is used to find ideas for a theory, but not to test that theory as well.
When a model is found exploratory in a dataset, then following up that analysis with a
confirmatory analysis in the same dataset could simply mean that the results of the
confirmatory analysis are due to the same type 1 error that resulted in the exploratory
model in the first place. The confirmatory analysis therefore will not be more informative
than the original exploratory analysis.
Stability of Results
It is important to obtain some indication about how generalizable the results are. While
this is hard to check, one can look at the stability of the results. Are the results reliable
and reproducible? There are two main ways of doing this:
Cross-Validation: By splitting the data in multiple parts we can check if an
analysis (like a fitted model) based on one part of the data generalizes to another
part of the data as well.
Sensitivity Analysis: A procedure to study the behavior of a system or model
when global parameters are (systematically) varied. One way to do this is with
bootstrapping.
Page 117 of 206

Statistical Methods
Many statistical methods have been used for statistical analyses. A very brief list of four
of the more popular methods is:
General linear model: A widely used model on which various methods are based
(e.g. t test, ANOVA, ANCOVA, MANOVA). Usable for assessing the effect of
several predictors on one or more continuous dependent variables.
Generalized linear model: An extension of the general linear model for discrete
dependent variables.
Structural equation modelling: Usable for assessing latent structures from
measured manifest variables.
Item response theory: Models for (mostly) assessing one latent variable from
several binary measured variables (e.g. an exam).
Free Software for Data Analysis
DataMelt (DMelt) – a data-analysis framework written in Java with support of
scripting languages.
DevInfo – a database system endorsed by the United Nations Development
Group for monitoring and analyzing human development.
ELKI – data mining framework in Java with data mining oriented visualization
functions.
KNIME – the Konstanz Information Miner, a user friendly and comprehensive
data analytics framework.
Orange – A visual programming tool featuring interactive data visualization and
methods for statistical data analysis, data mining, and machine learning.
PAST – free software for scientific data analysis
PAW – FORTRAN/C data analysis framework developed at CERN
R – a programming language and software environment for statistical computing
and graphics.
ROOT – C++ data analysis framework developed at CERN
SciPy and Pandas – Python libraries for data analysis
Page 118 of 206

International Data Analysis Contests
Different companies or organizations hold a data analysis contests to encourage
researchers utilize their data or to solve a particular question using data analysis. A few
examples of well-known international data analysis contests are as follows.
Kaggle competition held by Kaggle
LTPP data analysis contest held by FHWA and ASCE.
Page 119 of 206

Page 120 of 206

VI. Think Tanks
A Think Tank, Policy Institute, or Research Institute is an
organisation that performs research and advocacy concerning topics such as social
policy, political strategy, economics, military, technology, and culture. Most policy
institutes are non-profit organisations, which some countries such as the United
States and Canada provide with tax exempt status. Other think tanks are funded
by governments, advocacy groups, or corporations, and derive revenue from
consulting or research work related to their projects.
The following article lists global policy institutes according to continental categories, and
then sub-categories by country within those areas. These listings are not
comprehensive, given that more than 6,800 think tanks exist worldwide.
History
According to University of Southern California historian Jacob Soll, the term "think tank"
is modern, but "it can be traced to the humanist academies and scholarly networks of
the 16th and 17th centuries." Soll notes that "in Europe, the origins of think tanks go
Page 121 of 206

ack to the 800s, when emperors and kings began arguing with the Catholic Church
about taxes. A tradition of hiring teams of independent lawyers to advise monarchs
about their financial and political prerogatives against the church spans from
Charlemagne all the way to the 17th century, when the kings of France were still
arguing about whether they had the right to appoint bishops and receive a cut of their
income." [3] He also writes, independent "research teams became common in the late
16th and early 17th centuries, when states often depended on independent scholars
and their expertise."
Several major current think tanks date to the 19th century. For instance, the Institute for
Defence and Security Studies (RUSI) was founded in 1831 in London, and the Fabian
Society in Britain dates from 1884. The oldest American think tank, the Carnegie
Endowment for International Peace, was founded in Washington, D.C. in 1910 by
philanthropist Andrew Carnegie. Carnegie charged trustees to use the fund to "hasten
the abolition of international war, the foulest blot upon our civilization." The Brookings
Institution was founded shortly thereafter in 1916 by Robert S. Brookings and was
conceived as a bipartisan "research center modeled on academic institutions and
focused on addressing the questions of the federal government."
After 1945, the number of policy institutes increased, with many small new ones forming
to express various issue and policy agendas. Until the 1940s, most think tanks were
known only by the name of the institution. During the Second World War, think tanks
were often referred to as "brain boxes" after the slang term for skull. The phrase "think
tank" in wartime American slang referred to rooms where strategists discussed war
planning. Later the term "think tank" was used to refer to organizations that offered
military advice, such as the RAND Corporation, founded in 1946 as an offshoot of
Douglas Aircraft and became an independent corporation in 1948.
For most of the 20th century, independent public policy institutes that performed
research and provided advice concerning public policy were found primarily in the
United States, with a much smaller number in Canada, the UK and Western Europe.
Although think tanks existed in Japan for some time, they generally lacked
independence, having close associations with government ministries or corporations.
There has been a veritable proliferation of "think tanks" around the world that began
during the 1980s as a result of globalization, the end of the Cold War, and the
emergence of transnational problems. Two-thirds of all the think tanks that exist today
were established after 1970 and more than half were established since 1980.
The effect of globalisation on the proliferation of think tanks is most evident in regions
such as Africa, Eastern Europe, Central Asia, and parts of Southeast Asia, where there
was a concerted effort by the international community to assist in the creation of
independent public policy research organizations. A recent survey performed by the
Foreign Policy Research Institute's Think Tanks and Civil Societies Program
underscores the significance of this effort and documents the fact that most of the think
tanks in these regions have been established during the last 10 years. Presently there
are more than 4,500 of these institutions around the world. Many of the more
Page 122 of 206

established think tanks, having been created during the Cold War, are focused on
international affairs, security studies, and foreign policy.
Types
Think tanks vary by ideological perspectives, sources of funding, topical emphasis and
prospective consumers.
Some think tanks, such as The Heritage Foundation, which promotes conservative
principles, and the Center for American Progress, a progressive organization, are more
partisan in purpose.
Others, including the Tellus Institute, which emphasizes social and environmental
topics, are more issue-oriented groups.
Page 123 of 206

Funding sources and the consumers intended also define the workings of think tanks.
Some receive direct government assistance, while others rely on private individual or
corporate donors. This will invariably affect the degree of academic freedom within each
policy institute and to whom or what the institution feels beholden. Funding may also
represent who or what the institution wants to influence; in the United States, for
example, "Some donors want to influence votes in Congress or shape public opinion,
others want to position themselves or the experts they fund for future government jobs,
while others want to push specific areas of research or education."
A new trend, resulting from globalization, is collaboration between policy institutes in
different countries. For instance, the Carnegie Endowment for International Peace
operates offices in Washington, D.C., Beijing, Beirut, Brussels and Moscow.
The Think Tanks and Civil Societies Program (TTCSP) at the University of
Pennsylvania, led by Dr. James McGann, annually rates policy institutes worldwide in a
number of categories and presents its findings in the "Global Go-To Think Tanks" rating
index. However, this method of the study and assessment of policy institutes has been
criticized by researchers such as Enrique Mendizabal and Goran Buldioski, Director of
the Think Tank Fund, assisted by the Open Society Institute.
Several authors have indicated a number of different methods of describing policy
institutes in a way that takes into account regional and national variations.
For example:
Independent civil society think tanks established as non-profit organisations—
ideologically identifiable or not;
Policy research institutes affiliated with a university;
Governmentally created or state sponsored think tanks;
Corporate created or business affiliated think tanks;
Political party think tanks and legacy or personal think tanks;
Global (or regional) think tanks (with some of the above).
Alternatively, one could use some of the following criteria:
Size and focus: e.g., large and diversified, large and specialized, small and
specialized;
Evolution of stage of development: e.g., first (small), second (small to large but
more complex projects), and third (larger and policy influence) stages;
Strategy, including: Funding sources (individuals, corporations, foundations,
donors/governments, endowments, sales/events). and business model
(independent research, contract work, advocacy); The balance between
research, consultancy, and advocacy; The source of their arguments: Ideology,
Page 124 of 206

values or interests; applied, empirical or synthesis research; or theoretical or
academic research (Stephen Yeo); The manner in which the research agenda is
developed—by senior members of the think tank or by individual researchers, or
by the think tank of their funders; Their influencing approaches and tactics (many
researchers but an interesting one comes from Abelson) and the time horizon for
their strategies: long term and short term mobilisation; Their various audiences of
the think tanks (audiences as consumers and public -this merits another blog;
soon) (again, many authors, but Zufeng provides a good framework for China);
and Affiliation, which refers to the issue of independence (or autonomy) but also
includes think tanks with formal and informal links to political parties, interest
groups and other political players.
Advocacy by Think Tanks
In some cases, corporate interests and political groups have found it useful to create
policy institutes, advocacy organizations, and think tanks. For example, The
Advancement of Sound Science Coalition was formed in the mid-1990s to dispute
research finding an association between second-hand smoke and cancer. According to
an internal memorandum from Philip Morris Companies referring to the United States
Environmental Protection Agency (EPA), "The credibility of the EPA is defeatable, but
not on the basis of ETS [environmental tobacco smoke] alone,... It must be part of a
larger mosaic that concentrates all the EPA's enemies against it at one time."
Page 125 of 206

According to the Fairness and Accuracy in Reporting, both left-wing and right-wing
policy institutes are often quoted and rarely identified as such. The result is that think
tank "experts" are sometimes depicted as neutral sources without any ideological
predispositions when, in fact, they represent a particular perspective. In the United
States, think tank publications on education are subjected to expert review by the
National Education Policy Center's "Think Twice" think tank review project.
A policy institute is often a "tank", in the intellectual sense: discussion only in a
sheltered group protected from outside influence isolates the participants, subjects them
to several cognitive biases (groupthink, confirmation bias) and fosters members' existing
beliefs. This results in surprisingly radical and even unfeasible ideas being published.
Many think tanks, however, claim to purposefully attempt to alleviate this problem by
selecting members from diverse backgrounds.
A 2014 New York Times report asserted that foreign governments buy influence at
many United States think tanks. According to the article: "More than a dozen prominent
Washington research groups have received tens of millions of dollars from foreign
governments in recent years while pushing United States government officials to adopt
policies that often reflect the donors’ priorities."
African Think Tanks
Ghana
Global Think Tanks
Ghana's first president, Dr. Kwame Nkrumah, set up various state-supported think tanks
in the 1960s. By the 1990s, a variety of policy research centers sprang up in Africa set
up by academics who sought to influence public policy in Ghana.
One such think tank was The Institute of Economic Affairs, Ghana, which was founded
in 1989 when the country was ruled by the Provisional National Defence Council. The
IEA undertakes and publishes research on a range of economic and governance issues
confronting Ghana and Sub-Saharan Africa. It has also been involved in bringing
political parties together to engage in dialogue. In particular it has organised
Presidential debates every election year since the Ghanaian presidential election, 1996.
Some of the active think tanks in Ghana include:
Regional Advocacy for Public Development, Ghana ( RAPiD)
Concerned Citizens Movement, Ghana (CCMG)
The Institute For Democratic Governance ( IDEG)
Africa Centre for Development & Integrity (CeDI-Africa)
IMANI Centre for Policy and Education
The Institute of Economic Affairs, Ghana (IEA)
Page 126 of 206

The Center for Policy Analysis (CEPA)
Institute of Statistical, Social and Economic Research (ISSER)
Centre for Democratic Development (CDD)
The Integrated Social Development Centre (ISODEC)
Civil Society Platform on Oil and Gas
Artwatch Ghana http://artwatchghana.org
The Institute for Health Policy and Research (IHPR)
Morocco
The Amadeus Institute is an independent Moroccan think tank, founded in 2008
and based in Rabat. It acts as a laboratory of ideas, a brainstorming platform,
and a creator of debates. It contributes to the Moroccan and Maghreban public
debate. It also acts as the Voice of the South to communicate its vision and
concerns at the global level. The Amadeus Institute has a double role: analysis
and creating debates. It operates as a laboratory of ideas and a unique creator of
debates. It is at the same time a centre of reflection, dialogue proposition and
consultancy, but also a platform of exchanges, meetings and North-South and
South-South cooperation.
Page 127 of 206

Mazagan Institute promotes the development of intercultural dialogue, bringing
people together through culture and the development of cultural activities based
on the diversity of disciplines, thematic approaches, stakeholders, forms,
audiences, and places of achievement in promoting youth participation in
projects related to urban culture and social development ... Awaken in them the
notion of citizenship and social integration ... a conception of culture for which the
Institute Mazagan engages and advocates.
According to Marianne Republic, AMAQUEN is one of leading think tanks in the
world. Indeed, AMAQUEN, founded in 2003, is one of the most influential
associations in the field of education through its publications (rapports) and
international scientific journal Quality in Education and international events
(CIMQUSEF).
Somalia
Heritage Institute for Policy Studies
Institute for Somali Studies (ISOS), affiliated with Mogadishu University
The Somali Institute of Security and Diplomacy (Research & Practical Training)
(SIRAD)
Puntland Development Research Center
Somaliland Academy for Peace and Development
South Africa
N.P. van Wyk Louw-sentrum (Afrikaans)
IDASA: Frederik Van Zyl Slabbert, and Alex Boraine.
FW de Klerk Foundation
South African Institute of International Affairs (SAIIA)
South African Institute of Race Relations
Institute for Justice and Reconciliation
Centre for Development and Enterprise
Helen Suzman Foundation
Free Market Foundation
sbp Business Environment Specialists
Good Governance Africa
Institute for Security Studies
AfriMAP
Electoral Institute for Sustainable Democracy in Africa
Mapungubwe Institute for Strategic Reflection
Tunisia
Most of the Tunisian think tanks have emerged after 2011. Taking advantage of the new
climate of free expression and academic freedom, academics and politicians have
attempted to set up research centers whose mission the development of public policies.
Page 128 of 206

The Applied Social Science Forum (ASSF) was established in 2011 with the intent of
analyzing social transformation and democratic change. As a civilian non-profit
organization the program has become a think tank that seeks to develop "citizenship
research" – that is research oriented towards policy formulation and public interest
service. Today the ASSF works towards a dual mission of "preparing future generations
of leaders", and "providing leadership in advancing policy-relevant and applied
knowledge about the most important challenges of social dignity, Reform of Education
system, Security Sector Reform, security, public health Reform and other critical
issues."
Founded in 1995, the Tunisian Institute for Strategic Studies' (ITES) mission is to carry
out research, studies, analyses, and forecasting regarding short- and longer-term
horizons for a wide range of issues related to various national and international
phenomena that may affect the process of development of Tunisian society. These
issues cover the political, economic, social, and cultural fields. Among other things, the
institute is a meeting place for exchange among those with different skills, experiences,
and technical capabilities and a structure for building understanding and consensus
among the intellectual elite on the important questions and serious challenges facing
the country.
The Ibn Khaldun Institute, an affiliate of the Tunisian Community Center, is a nonpartisan,
non-profit and secular Advocay type Think Tank. Its focus is on the socioeconomic
development of Tunisia. The Ibn Khaldun Institute aims to be a Talent Bank
as well as an online clearinghouse for information on activities taking place in the United
States, that aim to promote the development of Tunisia.
Created by the Tunisian Community Center in 2005, the think tank was named after Ibn
Khaldun, the renowned 14th Century Tunisian polymath and statesman, whose name
came to symbolize kinship and solidarity. It is composed of Tunisian and Tunisian
American professionals in all disciplines, dedicated to promoting business, as well as,
cultural and professional exchanges between the United States and Tunisia.
Page 129 of 206

Asian Think Tanks
Armenia
According to the Global Go Think Tank Report 2012, there are around 14 think tanks in
Armenia of which the largest part is located in Yerevan. The Economic Development
and Research Center (EDRC), International Center for Human Development (ICHD) are
among the most active and well known think tanks in the country.
Bangladesh
Bangladesh has a number of think tanks that are in the form governmental, nongovernmental
and corporate organizations.
The Institute for Policy, Advocacy, and Governance (IPAG)
Bangladesh Institute of Development Studies (BIDS)
Bangladesh Institute of Law and International Affairs (BILIA)
Bangladesh Institute of Peace and Security Studies (BIPSS)
Centre for Policy Dialogue (CPD)
International Growth Centre (IGC)
Making Our Economy Right (MOER)
China
In the People's Republic of China a number of think tanks are sponsored by
governmental agencies, like Development Research Center of the State Council, but
still retain sufficient non-official status to be able to propose and debate ideas more
freely. In January 2012, the first non-official think-tank in China, South Non-
Governmental Think-Tank, was established in Guangdong province. In 2009 the China
Center for International Economic Exchanges, described as "China's top think tank,"
was founded.
Hong Kong
In Hong Kong, those early think tanks established in the late 1980s and early 1990s
focused on the political development including first direct Legislative Council members
election in 1991 and the political framework of "One Country, Two Systems" manifested
in the Sino-British Joint Declaration. After the transfer of sovereignty to China in 1997,
more and more think tanks were established by various groups of intellectuals and
professionals. They have various missions and objectives including promoting civic
education; undertaking research on economic social and political policies; promoting
"public understanding of and participation in the political, economic, and social
development of the Hong Kong Special Administrative Region".
India
Page 130 of 206

India has a number of think tanks. Most are based in New Delhi, and a few are
government sponsored. A number of these work on foreign policy and security
issues [citation needed] . There are few think tanks like Observer Research Foundation, Indian
Council for Research on International Economic Relations (ICRIER) and Centre for Civil
Society who promote liberal social and economic ideas and others like the Rakshak
Foundation, who encourage students to do empirical research and gain first hand
experience in public policy issues.
In Mumbai, Strategic Foresight Group is a global think tank that works on issues such
as Water Diplomacy, Peace and Conflict and Foresight (futures studies). Think tanks
with a development focus are those like the National Centre for Cold-chain
Development ('NCCD') which serve to bring inclusive policy change by supporting the
Planning Commission and related government bodies with industry-specific inputs – in
this case set up at the behest of the government to direct cold chain development.Some
think tanks have a fixed set of focus areas and they work towards finding out policy
solutions to social problems in the respective areas.
Initiatives such as National Data Sharing and Accessibility Policy (NDSAP) ( to ensure
systemic and semantic consistency of data collection and data sharing), National e-
Governance Plan (to automate administrative processes) and National Knowledge
Network (NKN) (for data and resource sharing amongst education and research
institutions), if implemented properly, should help improve the quality of work done by
think tanks.
Iraq
There are over 50 recently emerged think tanks in Iraq, particularly in the Kurdistan
Region. Iraq's leading think tank is the Middle East Research Institute (MERI), based in
Erbil. MERI is an independent non-governmental policy research organisation,
Page 131 of 206

established in 2014 and publishes in English, Kurdish and Arabic. It was listed in the
global ranking by the USA's Lauder Institute of the University of Pennsylvania as 46th in
the Middle East. Other Iraqi think tanks, publish in Arabic, include the Shiite-focused Al-
Rafidain Center in Najaf and the Islamic Dawa Party sponsored Al-Bayan Center.
Israel
There are many think tank teams in Israel:
Shaharit - Creating Common Cause
Jerusalem Institute for Market Studies (JIMS)
Neaman Institute
Reut Institute
Israel Council on Foreign Relations
The Jerusalem Center for Public Affairs
Adva Center
Israel Democracy Institute
The Institute for National Security Studies
The Jerusalem Institute for Israel Studies
The Myers-JDC-Brookdale Institute
The Taub Center for Social Policy Studies in Israel
The Van Leer Jerusalem Institute
AIX Group-Joint Palestinian-Israeli-International Economic Working Group
Floersheimer Studies at the Hebrew University of Jerusalem
Harry S. Truman Research Institute for the Advancement of Peace, The Hebrew
University of Jerusalem
International Institute for Counter-Terrorism – IDC Herziliya
Israel Center for Third Sector Research, Ben Gurion University of the Negev
IPCRI – Israel/Palestine Center for Research and Information
The Milken Institute
Moshe Dayan Center for Middle Eastern and African Studies, Tel Aviv University
The Shalom Hartman Institute
The Begin-Sadat Center – Bar Ilan University
The Center for the Study of Philanthropy in Israel at the Hebrew University of
Jerusalem
The Institute for Advanced Studies at The Hebrew University of Jerusalem
The Jewish Arab Center (JAC), University of Haifa
The Jewish People Policy Institute (JPPI)
The Maurice Falk Institute for Economic Research in Israel, The Hebrew
University
The Shalem Center
Institute for National Security Studies, affiliated with Tel Aviv University.
Page 132 of 206

Japan
Japan has over 100 think tanks, most of which cover not only policy research but also
economy, technology and so on. Some are government related, but most of the think
tanks are sponsored by the private sector.
Kazakhstan
Institute of World Economics and Politics (IWEP) at the Foundation of the First
President of the Republic of Kazakhstan was created in 2003. IWEP activities
aimed at research problems of the world economy, international relations,
geopolitics, security, integration and Eurasia, as well as the study of the First
President of the Republic of Kazakhstan and its contribution to the establishment
and strengthening of Kazakhstan as an independent state, the development of
international cooperation and the promotion of peace and stability.
The Kazakhstan Institute for Strategic Studies under the President of the RK
(KazISS) was established by the Decree of the President of RK on 16 June 1993.
Since its foundation the main mission of the Kazakhstan Institute for Strategic
Studies under the President of the Republic of Kazakhstan, as a national think
tank, is to maintain analytical and research support for the President of
Kazakhstan.
Malaysia
Most Malaysian think tanks are government or political party related. They focus on
defense, politicsm and policies. However, in recent dates, think tanks that focus on
Page 133 of 206

international, trade and economics have also existed. Notable ones include the Institute
for Democracy and Economic Affairs (IDEAS), Institute for Pioneering of Education and
Economic Excellence (INSPIRE), Penang Institute (PI), Center of Public Policy Studies
(CPPS), and Khazanah Research Institute (KRI).
Pakistan
Pakistan's think tanks mainly revolve around social policy, internal politics, foreign
security issues, and regional geo-politics. Most of these are centered on the capital,
Islamabad. One notable think tank is the Sustainable Development Policy Institute
(SDPI), which focuses on policy advocacy and research particularly in the area of
environment and social development. Another notable policy research institute based in
Islamabad is the Institute of Social and Policy Sciences (I-SAPS) which works in the
fields of education, health, disaster risk reduction, governance, conflict and stabilization.
Philippines
Think tanks in the Philippines could be generally categorized in terms of their linkages
with the national government. Several were set up by the Philippine government for the
specific purpose of providing research input into the policy-making process.
Sri Lanka
Sri Lanka has a number of think tanks that are in the form governmental, nongovernmental
and corporate organizations.
LIRNEasia is a think-tank working across the Asia-Pacific on regulatory and policy
issues. Their main focus is the ICT sector, although they do work in other sectors, such
as agriculture and health, which can benefit from ICT.
Verité Research is an interdisciplinary think tank in Colombo.
The Lakshman Kadirgamar Institute of International Relations and Strategic Studies is a
policy-studies institute that is often referred to as a think tank.
Singapore
There are several think tanks in Singapore that advises the government on various
policies and as well as private ones for corporation within the region. Many of them are
hosted within the local public educational institutions.
Among them are the Singapore Institute of International Affairs (SIIA), Institute of
Southeast Asian Studies (ISEAS), S. Rajaratnam School of International Studies (64th),
Centre on Asia and Globalization, Asia Competitiveness Institute, The HEAD
Foundation.
Page 134 of 206

Uzbekistan
CED – Center for Economic Development (Центр Содействия Экономическому
Развитию) is a think-tank whose major tasks are: analytic support in economic
reforming and development in Uzbekistan; improving knowledge and skills of the
subjects of economic development; assistance in productive dialogue between
the government, civil society and private sectors on the economic development
matters.
Key projects: Preparation of the National human development report for Uzbekistan,
Sociological "portrait" of the Uzbek businessman, Preparation of an analytical report on
export procedures optimization in Uzbekistan, various industry and marketing
researches in Uzbekistan, Tajikistan, and Turkmenistan.
Australia
Most Australian think-tanks are based at universities – for example, the Melbourne
Institute – or are government-funded – for example, the Productivity Commission or the
CSIRO.
Private sources fund about 20 to 30 "independent" Australian think tanks. The bestknown
of these think tanks play a much more limited role in Australian public and
business policy-making than do their equivalents in the United States. However, in the
past decade the number of think tanks has increased substantially.
[citation needed]
Page 135 of 206

Prominent Australian conservative think tanks include the Centre for Independent
Studies, the Sydney Institute and the Institute of Public Affairs. Prominent leftist
Australian think tanks include the McKell Institute, Per Capita, the Australia Institute, the
Lowy Institute and the Centre for Policy Development. In recent years regionally-based
independent and non-partisan think tanks have emerged. Some, such as the Illawarra's
i-eat-drink-think, engage in discussion, research and advocacy within a broader civics
framework. Commercial think-tanks like the Gartner Group, Access Economics, the
Helmsman Institute, and others provide additional insight which complements not-forprofit
organisations such as CEDA, the Australian Strategic Policy Institute, and the
Australian Institute of Company Directors to provide more targeted policy in defence,
program governance, corporate governance and similar.
Listed in alphabetical order, think tanks based in Australia include:
Ainslie Institute
Air Power Australia
AltSolved Commonwealth Think Tank
Asia Education Foundation
Asia Society (AustralAsia)
Asialink
Australasian Institute of Business Productivity
Australia India Institute
The Australia Institute
Australian Fabian Society
Australian Institute of International Affairs
Australian Institute of Policy & Science
Australian Strategic Policy Institute
The Brisbane Institute
Centre for Independent Studies
Centre for Policy Development
Chifley Research Centre
Committee for Economic Development of Australia
Crowther Centre for Learning and Innovation
Development Policy Centre
Doctors Reform Society of Australia
Evatt Foundation
Grattan Institute
H. R. Nicholls Society
Infrastructure Partnerships Australia
Institute for Economics and Peace
Institute of Public Affairs
International Energy Centre
International Water Centre
Issues Deliberation Australia/America
Laboratory for Visionary Architecture
Lowy Institute for International Policy
Page 136 of 206

Mannkal Economic Education Foundation
The McKell Institute
The Melbourne Institute of Applied Economic and Social Research
Menzies Research Centre
National Civic Council
New England Futures Group
New South Wales Institute for Educational Research
Per Capita
Samuel Griffith Society
Strategic and Defence Studies Centre
Sydney Institute
Transport and logistics centre
United States Studies Centre
Western Australia Policy Forum
European Think Tanks
Belgium
Brussels hosts most of the European Institutions, hence a large number of international
think tanks are based there. Among them there are, Bruegel, the Centre for European
Policy Studies (CEPS), Centre for the New Europe (CNE), the European Centre of
International Political Economy (ECIPE), the European Policy Centre (EPC), the Friends
of Europe, the Global Governance Institute (GGI), Sport and Citizenship, and
ThinkYoung.
Page 137 of 206

Bulgaria
Bulgaria has a number of think tanks providing expertise and shaping policies, including
Institute of Modern Politics.
Czech Republic
The European Values Think-Tank
Denmark
CEPOS is a classical liberal/free-market conservative think-tank in Denmark.
Cevea is a centre-left think tank, mainly founded as an opposition to CEPOS.
Estonia
PRAXIS (et) is a socio-economic research think-tank.
Finland
Finland has several small think tanks that provide expertise in very specific fields.
Vasemmistofoorumi researches the future of leftism, OK Do is a socially-minded design
thinking organization, Demos Helsinki is a think tank that researches future society and
Culture Crisis Management is political artists' think tank.
Other similar think tanks include:
Åland Islands Peace Institute
European Centre of Excellence for Countering Hybrid Threats (Hybrid CoE)
Crisis Management Initiative (CMI)
Research Institute of the Finnish Economy (Etla)
Finnish Institute of International Affairs
In addition to specific independent think tanks, the largest political parties have their
own think tank organizations. This is mainly due to support granted by state for such
activity. The corporate world has focused their efforts to central representative
organization Confederation of Finnish Industries, which acts as think tank in addition to
negotiating salaries with workers unions. Furthermore, there is the Finnish Business
and Policy Forum (Elinkeinoelämän valtuuskunta, EVA). Agricultural and regional
interests, associated with The Central Union of Agricultural Producers and Forest
Owners (Maa- ja metsätaloustuottajain Keskusliitto, MTK) and the Centre Party, are
researched by Pellervo Economic Research (Pellervon taloustutkimus, PTT). The
Central Organisation of Finnish Trade Unions (Suomen Ammattiliittojen Keskusjärjestö,
SAK) and the Social Democratic Party are associated with the Labour Institute for
Economic Research (Palkansaajien tutkimuslaitos, PT). Each of these organizations
often release forecasts concerning the national economy.
Page 138 of 206

France
The French Institute of International Relations (IFRI) was founded in 1979 and is the
third oldest think tank of western Europe, after Chatham House (UK, 1920) and the
Stockholm International Peace Research Institute (Sweden, 1960). The primary goals of
IFRI are to develop applied research in the field of public policy related to international
issues, and foster interactive and constructive dialogue between researchers,
professionals, and opinion leaders. France also hosts the European Union Institute for
Security Studies (EUISS), a Paris-based agency of the European Union and think tank
researching security issues of relevance for the EU. There are also a number of probusiness
think tanks, notably the Paris-based Fondation Concorde. The foundation
focuses on increasing the competitiveness of French SME's and aims to revive
entrepreneurship in France.
On the left, the main think tanks in France are the Fondation Jean Jaures, which is
organizationally linked to the French Socialist Party, and Terra Nova. Terra Nova is an
independent left-leaning think tank, although it is nevertheless considered to be close to
the Socialists. It works on producing reports and analyses of current public policy issues
from a progressive point of view, and contributing to the intellectual renewal of social
democracy.
Only French Think Tank mentioned in the list "Think Tank to watch" of the 2014 2014
Global Go To Think Tank Index Report GenerationLibre is a French think-tank created
by Gaspard Koenig in 2013, independent from all political parties, which aims at
promoting freedoms in France, in terms of fondamental rights, economics and societal
issues. GenerationLibre is an interesting breed able to connect to the right on pro
business freedom and regulations issues but also to the left on issues such as "basic
income", gay marriage or marijuana legalization.
Page 139 of 206

Germany
In Germany all of the major parties are loosely associated with research foundations
that play some role in shaping policy, but generally from the more disinterested role of
providing research to support policymakers than explicitly proposing policy. These
include the Konrad-Adenauer-Stiftung (Christian Democratic Union-aligned), the
Friedrich-Ebert-Stiftung (Social Democratic Party-aligned), the Hanns-Seidel-Stiftung
(Christian Social Union-aligned), the Heinrich-Böll-Stiftung (aligned with the Greens),
Friedrich Naumann Foundation (Free Democratic Party-aligned) and the Rosa
Luxemburg Foundation (aligned with Die Linke). The German Institute for International
and Security Affairs is a prominent example of a German foreign policy think tank.
Atlantic Community think tank is an example of independent, non-partisan and nonprofit
organization set up as a joint project of Atlantische Initiative e.V. and Atlantic
Initiative United States The Institute for Media and Communication Policy is the leading
think tank in the realm of media. Transparency International is a think tank on the role of
corporate and political corruption in international development.
Greece
In Greece there are many think tanks, also called research organisations or institutes.
Ireland
The Economic and Social Research Institute (ESRI) is an independent research
institute in Dublin, Ireland. Its research focuses on Ireland's economic and social
development to inform policy-making and societal understanding.
The Institute of International and European Affairs (IIEA) is Ireland's leading think tank
on European and International affairs
The Iona Institute is a conservative, Catholic think tank.
Tasc (Think tank for Action on Social Change) is an Irish left wing think tank.
Italy
Bruno Leoni Institute
Fondazione Eni Enrico Mattei
Future Italy
ISPI – Italian Institute for International Political Studies
Istituto Affari Internazionali
Mediterranean Affairs
‘Trinità dei Monti’ think tank
Venezie Institute
I2I Platform
Sardegna Democratica
Page 140 of 206

Latvia
While think tanks are not widespread in Latvia, as opposed to single issue advocacy
organizations, there are several noticeable institutions in the Latvian think tank
landscape:
The oldest think tank in Latvia is Latvian Institute of International Affairs. LIIA is a
non governmental and non partisan foundation, established in 1992, their
research and advocacy mainly focuses on: Latvian foreign policy, Transatlantic
relations, European Union policies, including its neighborhood policy and Eastern
Partnership, and multilateral and bilateral relations with Russia.
Centre for Public policy PROVIDUS is a non governmental and non partisan
association, established in 2002. Providus focuses their work (both research and
advocacy) on topics especially relevant in transition and post-transition
environments and Latvia in particular: good governance; criminal justice policy;
tolerance and inclusive public policy and European policy.
There are several think tanks that are established and operate under the auspices of
Universities. Such as:
Centre for European and Transition Studies is a think tank working under the
auspices of the University of Latvia,- the largest public university in the country.
CETS was established in 2000.
or Defense research centre in 1992 under the auspices of the National Academy
of Defense.
Page 141 of 206

Netherlands
All major political parties in the Netherlands have state-sponsored research foundations
that play a role in shaping policy. The Dutch government also has its own think tank: the
Scientific Council for Government Policy.
Poland
There is a large pool of think-tanks in Poland on a wide variety of subjects. The oldest
state-sponsored think tank is The Western Institute in Poznań (Polish: Instytut
Zachodni, German West-Institut, French: L'Institut Occidental).The second oldest is the
Polish Institute of International Affairs (PISM) established in 1947. The other most
important state-sponsored think tank is the Centre for Eastern Studies (OSW), which
specializes in the countries neighboring Poland and in the Baltic Sea region, the
Balkans, Turkey, the Caucasus and Central Asia. Among the private think tanks the
most important are: the Center for Social and Economic Research (CASE), founded in
1991 and the oldest economic think tank in the country; and Institute for Structural
Research (IBS) on economic policy, The Casimir Pulaski Foundation on foreign policy,
demosEUROPA on EU affairs, the Institute of Public Affairs (ISP) on social policy, the
Center for International Affairs (CSM) and The Sobieski Institute.
Portugal
Founded in 1970, the SEDES is one of the oldest Portuguese civic associations and
think tanks. Contraditório think tank was founded in 2008. Contraditório is a non-profit,
independent and non-partisan think tank.
Romania
Romania's largest think tank is the Romanian Academic Society (SAR), which was
founded in 1996.
The Institute for Public Policy (IPP) is a think-tank established in 2001 with the aim to
support the development of democratic processes in Romania through in-depth
research, comprehensive debates and non-partisan public policy analysis. Its mission is
to contribute to a better process of public policy formulation in Romania. From its very
inception, the Institute adhered to high professional standards and to promote concrete,
objective and data-supported policy measures, with the aim to contribute to a
consolidation of the democratic system in Romania by promoting the idea of public
policy designed in accordance with global standards of scholarship. The IPP developed
and consolidated recognized expertise in the fields of reform of public administration
(reform of public services, modernization of the civil service body, fiscal
decentralization), political parties finance, analysis of electoral systems and processes,
health reform, public procurement and policies to combat corruption. This was achieved
by working with specialized personnel and by permanent collaboration with experts in
the aforementioned fields. Since 2004, the IPP is a member organization of the Policy
Page 142 of 206

Association for an Open Society (PASOS) network, together with other similar
organizations from 22 countries. The IPP's motto is "It's all about thinking".
Serbia
Serbia's best known think thank is the Foundation for the Advancement of Economics –
FREN, founded in 2005 by the Belgrade University's Faculty of Economics. Thanks to
the quality and relevance of its research, FREN has established itself as one of the
leading economic think tanks in Serbia. FREN's team comprises a network of over 30
associates who regularly and systematically monitor economic trends in Serbia, conduct
in-depth research and encourage and facilitate the exchange of information and
availability of economic data.
Slovakia
Besides the international think tanks present in the surrounding countries as well (with
Open Society Foundations being the most notable one) Slovakia has a host of its own
think tanks as well. Some of the think tanks in Slovakia focus on public policy issues,
such as Institute of Public Affairs (Inštitút pre verejné otázky or IVO in Slovak) or Central
European Labour Studies Institute (Stredoeurópsky inštitút pre výskum práce or CELSI
in Slovak). Others specialize on human rights issues such as minority protection, for
example Forum Minority Research Institute (Fórum Kisebbségkutató Intézet or Fórum
Intézet in Hungarian and Fórum inštitút pre výskum menšín or Fórum inštitút in Slovak).
Page 143 of 206

Since some of the Slovak think tanks are perceived to be associated with right-wing and
liberal parties of Slovakia (with the perception being particularly strong among Slovak
nationalists), findings and proposals made by these organizations are generally
resented or ignored by left-wing supporters and nationalists.
Spain
In Spain, think tanks are progressively raising their public profile. There are now at least
30 think tanks in the country. One of the most influential Spanish think tanks is the
Elcano Royal Institute, created in 2001 following the example of the Royal Institute of
International Affairs (Chatham House) in the UK, although it is closely linked to (and
receives funding from) the government in power. More independent but clearly to the
left of the political spectrum are the Centro de Investigaciones de Relaciones
Internacionales y Desarrollo (CIDOB) founded in 1973; and the Fundación para las
Relaciones Internacionales y el Diálogo Exterior (FRIDE) established in 1999 by Diego
Hidalgo and main driving force behind projects such as the Club de Madrid, a group of
democratic former heads of state and government, the Foreign Policy Spanish Edition
and DARA (international organization).
Former Prime Minister José Maria Aznar presides over the Fundación para el Analisis y
los Estudios Sociales (FAES), a policy institute that is associated with the conservative
Popular Party (PP). Also linked to the PP is the Grupo de Estudios Estratégicos
(GEES), which is known for its defense- and security-related research and analysis. For
its part, the Fundación Alternativas is independent but close to left-wing ideas. The
Socialist Partido Socialista Obrero Español (PSOE) created Fundación Ideas in 2009
and dissolved it in January 2014. Also in 2009, the centrist Union, Progress and
Democracy (UPyD) created Fundación Progreso y Democracia (FPyD).
More specialized think tanks has also emerged in Spain during the past 10 years, like
the Future Trends Forum from Bankinter Foundation, a unique think tank in Europe,
focused on detecting social, economic, scientific and technological trends and analyzing
their possible application and impact on current business models. There are also
regional think tanks, such as Institución Futuro, considered one of the most influential
think tanks in the World, according to the Global Go to Think Tank Index Report, by
James McGann. Lately, a new Thinktank has been founded on European Union, it is
called "Institute for European Advancement".
Sweden
The two biggest think tanks in Sweden is the right oriented Timbro and left oriented
Agora, now changed to Arena Idé. Others are Sektor3, SNS, FORES, Arbetarrörelsens
Tankesmedja (socialdemocratic oriented), Civitas (Christian democratic oriented),
Institute for Security and Development Policy, DNV (Den Nya Välfärden, no party
connection) and Cogito (green oriented).
Page 144 of 206

Switzerland
The first think tank of Switzerland is the Gottlieb Duttweiler Institute(GDI), envisioned by
the Migros-founder Gottlieb Duttweiler in 1946. It opened its doors in 1963 after the
death of Duttweiler. Other think tanks include:
Liberal Institute, founded in 1979.
Avenir Suisse, founded in 1999 by fifteen of the largest Swiss companies. It is
supported by over 130 companies to date.
DCAF, the Geneva Centre for the Democratic Control of Armed Forces, founded
in 2000 to research security sector governance and reform.
Denknetz, founded in 2004.
The Geneva Academy of International Humanitarian Law and Human Rights.
Universal Rights Group.
foraus, Swiss Forum on Foreign Policy, founded in 2009.
Ukraine
In Ukraine, there is the Centre of Policy and Legal Reform (CPLR). Additionally, the
Razumkov Centre is a non-governmental think tank founded in 1994. It carries out
research of public policy in the following spheres:
domestic policy;
state administration;
Page 145 of 206

economic policy;
energy;
land relations;
foreign policy;
social policy;
international and regional security;
national security and defense.
Razumkov Centre united experts in the fields of economy, energy, law, political
sciences, international relations, military security, land relations, sociology, history and
philosophy. The Centre has about 35 full-time employees, and over 100 persons work
on contractual basis. The Ukrainian-wide public opinion polls of Razumkov Centre
Sociological Service are carried out by over 300 interviewers.
Analytical materials of Razumkov Centre are:
recognized and used by different political forces;
recognized by scientific and expert community;
presented on the web sites of the Government, some ministries and
departments;
used as analytical and reference materials during the parliamentary hearings in
the Verkhovna Rada of Ukraine;
listed as recommended for the students of Ukrainian universities;
have high index of quoting in Ukrainian and foreign mass media and scientific
literature.
In 2004, on the International Economic Forum in Krynica (Poland) Razumkov Centre
was named the best non-governmental organisation of Eastern Europe.
The Razumkov Centre is listed among top-25 think tanks of the Central and Eastern
Europe.
The average Centre's yearly budget is approximately $600,000.
Institute for Social & Economic Studies is a non-governmental organization in Ukraine, a
think tank that aims to analyze public policy in various aspects of the life of the state.
The mission of the think tank: to be an intellectual bridge of trust between government,
business, and community. Such intellectual bridges primarily aimed at the formation and
restoration of trust among all stakeholders to achieve synergy and better results of joint
efforts in assessing and improving public policy in all spheres of social and economic
development – from the nomination of an idea to the analysis of the results of its
implementation.
Products and activities:
Drafts of normative legal acts;
Conclusions to draft laws and recommendations to authorities
Page 146 of 206

Studies in the field of public policy
Analytical publications
Expert comments, presentations, briefings
Organization of seminars, workshops and training for government officials,
people's deputies, representatives of local communities and businesses
United Kingdom
In Britain, think tanks play a similar role to the United States, attempting to shape policy,
and indeed there is some cooperation between British and American think tanks. For
example, the London-based think tank Chatham House and the Council on Foreign
Relations were both conceived at the Paris Peace Conference, 1919 and have
remained sister organisations.
The Bow Group, founded in 1951, is the oldest centre-right think tank and many of its
members have gone on to serve as Members of Parliament or Members of the
European Parliament. Past chairmen have included Conservative Party leader Michael
Howard, Margaret Thatcher's longest-serving Cabinet Minister Geoffrey Howe,
Chancellor of the Exchequer Norman Lamont and former British Telecom chairman
Christopher Bland.
CIVITAS, Demos, the Institute for Public Policy Research, Policy Exchange and Reform
are five of the most significant think-tanks of the United Kingdom.
Page 147 of 206

Transcontinental Countries (Asia-Europe)
Azerbaijan
According to research done by the University of Pennsylvania, there are a total of 12
think tanks in Azerbaijan.
The Center for Economic and Social Development, or CESD; in Azeri, Azerbaijan,
İqtisadi və Sosial İnkişaf Mərkəzi (İSİM) is an Azeri think tank, non-profit organization,
NGO based in Baku, Azerbaijan. The Center was established in 2005.
CESD focuses on policy advocacy and reform, and is involved with policy research and
capacity building. CESD employs leading researchers prominent in their fields and
enjoys a broad regional and international network. CESD has been set up to promote
research into domestic and regional economic and social issues, advocacy towards
reforms and capacity building for the purpose to positively impact the policy making and
improve the participation.
CESD ranked as one of the top think tanks in the world by the University of
Pennsylvania. According to the University of Pennsylvania rankings – a result of
surveys from 1500 scholars and peer review evaluation – the Center for Economic and
Social Development (CESD) is one of the top 25 think tanks in Central and Eastern
Europe, including CIS. CESD is the only think tank from the Caucasus and Central Asia
included in the top think tanks rankings.CESD is also ranked as one of the top 25
domestic economic policy thinks tanks in the world. Only CESD (ranked 19) and the
Center for Economic and Social Research (CASE), (Poland, ranked 21) were included
in the list from Central and Eastern Europe and CIS countries.
The Economic Research Center (ERC) is a policy-research oriented non-profit think
tank established in 1999 with a mission to facilitate sustainable economic development
and good governance in the new public management system of Azerbaijan. It seeks to
do this by building favorable interactions between the public, private and civil society
and working with different networks both in local (EITI NGO Coalition, National Budget
Group, Public Coalition Against Poverty, etc.) and international levels (PWYP, IBP,
ENTO, ALDA, PASOS, WTO NGO Network etc.).
Center for Strategic Studies under the President of Azerbaijan
Russia
According to the Foreign Policy Research Institute, Russia has 112 think tanks, while
Russian think tanks claimed four of the top ten spots in 2011's "Top Thirty Think Tanks
in Central and Eastern Europe".
Notable Russian think tanks include:
Page 148 of 206

Analytical Center for the Government of the Russian Federation
Carnegie Moscow Center
Institute of World Economy and International Relations
Moscow State Institute of International Relations
Center for Economic and Financial Research
Institute for US and Canadian Studies
Council on Foreign and Defense Policy (in Russian)
Independent Institute for Social Policy
Pskovian Laboratory of Urban Macroeconomics (PLUM)
Turkey
Turkish think tanks are relatively new. Many of them are sister organizations of a
political party, University or a company. University think tanks are not typical think tanks.
Most Turkish think tanks provide research and ideas, yet they play less important roles
in policy making when compared with American think tanks. There are at least 20 think
tanks in the country. There are number of Think Tank organizations both independent
and supported by government. Turksam, Tasam and the journal of Turkish weekly are
the leading information sources.
The oldest and most influential think tank organization in Turkey is ESAM (The Center
for Economic and Social Research – Ekonomik ve Sosyal Araştırmalar Merkezi) which
is established in 1969 and centrally based in Ankara. There are also branch offices of
ESAM in İstanbul, Bursa, Konya and other cities. ESAM has strong international
Page 149 of 206

elationships especially with Muslim countries and societies. Ideologically it performs
policies, produce ideas and manage projects in parallel to Milli Görüş and have also
influential effects on political parties and international strategies. The founder Leader of
Milli Görüş movement Prof. Dr. Necmettin ERBAKAN was very concerned with activities
and brainstorming events of ESAM. In The Republic of Turkey, 2 of the presidents, 4 of
the prime ministers, various ministers, many members of the parliament, a lot of mayors
and bureaucrats had been member of ESAM. Currently the General Chairman of ESAM
is a famous veteran politician Recai KUTAN (who is older Minister for two different
ministries, older main opposition party leader, and founder General Chairman of Saadet
Party).
Turkish Economic and Social Studies Foundation (TESEV) is another leading thinkthank.
Established in 1994, TESEV is an independent non-governmental think-tank,
analyzing social, political and economic policy issues facing Turkey. Some of the most
remarkable of TESEV's work have been on the issues of Islam and democracy,
combating corruption, state reform, and transparency and accountability. TESEV serve
as a bridge between academic research and policy-making process in Turkey. Its core
program areas are democratization, good governance, and foreign policy.
Education Reform Initiative (ERI) was launched within Sabancı University in 2003 with
the aim of improving education policy and decision-making through research, advocacy,
and training. ERI mobilizes a wide range of stakeholders in participatory education
policy processes in pursuit of its mission of "quality education for all."
Other influential Turkish think tanks are the International Strategic Research
Organisation (USAK), SETA, BİLGESAM, Academic Research Institute (AAE) etc.
Canada
North American Think Tanks
Canada has many think tanks (listed in alphabetical order). Each has specific
areas of interest with some overlaps
Asia Pacific Foundation of Canada
Atlantic Institute for Market Studies
Broadbent Institute
C.D Howe Institute
Caledon Institute of Social Policy
Canada 2020
Canada West Foundation
Canadian Centre for Policy Alternatives
Canadian Employment Research Forum
Canadian Global Affairs Institute
Canadian Institute for Advanced Research
Canadian International Council
Page 150 of 206

Canadian Labour and Business Centre
Canadian Tax Foundation
Cardus
Centre for International Governance Innovation
Centre for Trade Policy and Law
Clean Energy Canada
Conference Board of Canada
Council of Canadians
Digital Economy Forum
Franco-Canadian Research Centre
Fraser Institute
Frontier Centre for Public Policy
Canadian Council on Social Development
Institute for Public Economics
Institute for Quantum Computing
Institute for Research on Public Policy
Institute on Governance
International Institute for Sustainable Development
International Justice Institute
International Policy Forum
Institut de recherche et d'informations socio-économiques
Manning Foundation
Montreal Economic Institute
Mowat Centre for Policy Innovation
North-South Institute
Parkland Institute
Pembina Institute
Perimeter Institute for Theoretical Physics
Public Policy Forum
The Conference of Defence Associations
Western Centre for Economic Research
Note: The Canadian Policy Research Networks (CPRN) is a Canadian think-tank that
has disbanded.
United States
As the classification is most often used today, the oldest American think tank is the
Carnegie Endowment for International Peace, founded in 1910. The Institute for
Government Research, which later merged with two organizations to form the Brookings
Institution, was formed in 1916. Other early twentieth century organizations now
classified as think tanks include the Hoover Institution (1919), The Twentieth Century
Fund (1919, and now known as the Century Foundation), the National Bureau of
Economic Research (1920), the Council on Foreign Relations (1921), and the Social
Science Research Council (1923). The Great Depression and its aftermath spawned
several economic policy organizations, such as the National Planning Association
Page 151 of 206

(1934), the Tax Foundation (1937), and the Committee for Economic Development
(1943).
In collaboration with the Douglas Aircraft Company, the Air Force set up the RAND
Corporation in 1946 to develop weapons technology and strategic defense analysis.
More recently, progressive and liberal think tanks have been established, most notably
the Center for American Progress and the Center for Research on Educational Access
and Leadership (CREAL). The organization has close ties to former United States
President Barack Obama and other prominent Democrats. In 2002, a French
economist, Dr Gerard Pince, founded the Free World Academy well known for its
entrepreneurship program.
Think tanks help shape both foreign and domestic policy. They receive funding from
private donors, and members of private organizations. By 2013, the largest 21 think
tanks in the US spent more than $1 billion per year. Think tanks may feel more free to
propose and debate controversial ideas than people within government. The
progressive media watchgroup Fairness and Accuracy in Reporting (FAIR) has
identified the top 25 think tanks by media citations, noting that from 2006 to 2007 the
number of citations declined 17%. The FAIR report reveals the ideological breakdown of
the citations: 37% conservative, 47% centrist, and 16% liberal. Their data show that the
most-cited think tank was the Brookings Institution, followed by the Council on Foreign
Relations, the American Enterprise Institute, The Heritage Foundation, and the Center
for Strategic and International Studies.
Recently in response to scrutiny about think tanks appearing to have a "conflict of
interest" or lack transparency, executive vice president, Martin S. Indyk of Brookings
Institution – the "most prestigious think tank in the world" – admitted that they had
"decided to prohibit corporations or corporate-backed foundations from making
anonymous contributions." In August 2016, the New York Times published a series on
think tanks that blur the line. One of the cases the journalists cited was Brookings,
where scholars paid by a seemingly independent think tank "push donors' agendas
amplifying a culture of corporate influence in Washington." For example, in exchange for
hundreds of thousands of dollars the Brookings Institution provided the publicly-traded
company Lennar Corporation – one of the United States' largest home builders – with a
significant advantage in pursuing their $US8 billion revitalization project in Hunters
Point, San Francisco. In 2014 Lennar's then-regional vice president in charge of the San
Francisco revitalization, Kofi Bonner in 2014, was named as a Brookings senior fellow –
a position as 'trusted adviser' that carries some distinction. Bruce Katz, a Brookings vice
president, also offered to help Lennar Corporation "engage with national media to
develop stories that highlight Lennar's innovative approach."
Government
Government think tanks are also important in the United States, particularly in the
security and defense field. These include the Institute for National Strategic Studies,
Page 152 of 206

Institute for Homeland Security Studies, and the Center for Technology and National
Security Policy, at the National Defense University; the Center for Naval Warfare
Studies at the Naval War College and the Strategic Studies Institute at the U.S. Army
War College.
The government funds, wholly or in part, activities at approximately 30 Federally
Funded Research and Development Centers (FFRDCs). FFRDCs, are unique
independent nonprofit entities sponsored and funded by the United States government
to meet specific long-term technical needs that cannot be met by any other single
organization. FFRDCs typically assist government agencies with scientific research and
analysis, systems development, and systems acquisition. They bring together the
expertise and outlook of government, industry, and academia to solve complex
technical problems. These FFRDCs include the RAND Corporation, the MITRE
Corporation, the Institute for Defense Analyses, the Aerospace Corporation, the MIT
Lincoln Laboratory, and other organizations supporting various departments within the
United States Government.
Similar to the above quasi-governmental organizations are Federal Advisory
Committees. These groups, sometimes referred to as commissions, are a form of think
tank dedicated to advising the US Presidents or the Executive branch of government.
They typically focus on a specific issue and as such, might be considered similar to
special interest groups. However, unlike special interest groups these committees have
come under some oversight regulation and are required to make formal records
Page 153 of 206

available to the public. Approximately 1,000 these advisory committees are described in
the FACA searchable database.
Latin American Think Tanks
South American Think Tanks
Research done by Enrique Mendizabal [110] shows that Latin American think tanks play
various roles depending on their origins, historical development and relations to other
policy actors. In this study, Orazio Bellettini from Grupo FARO suggests that they: [111]
1. Seek political support for policies.
2. Legitimize policies – This has been clearer in Ecuador, Bolivia and Peru. New
governments in Ecuador and Peru have approached policy institutes for support
for already defined policies. In Bolivia, the government of Evo Morales has been
working with Non-Government Organizations (NGOs) and other research
institutes to do the same. However, in Chile, many think tanks during the 1990s
seemed to endorse and maintain the legitimacy of policies implemented during
the previous decade by the military dictatorship headed by Pinochet.
3. Spaces of debate – In this case think tanks serve as sounding boards for new
policies. In Chile, during the Pinochet dictatorship, many left wing intellectuals
and researchers found ‘asylum’ in think tanks. In Ecuador, think tanks are seen
as spaces where politicians can test the soundness of their policies and
government plans.
4. Financial channels for political parties or other interest groups – In Ecuador and
Bolivia, German foundations have been able to provide funds to think tanks that
work with certain political parties. This method has provided support to the
system as a whole rather than individual CSOs.
5. Expert cadres of policy-makers and politicians – In Peru after the end of the
Fujimori regime, and in Chile after the fall of Pinochet, think tank staff left to form
part of the new governments. In the United States, the role of major think tanks is
precisely that: host scholars for a few months or years and then lose them to
government employ.
How a policy institute addresses these largely depends on how they work, their ideology
vs. evidence credentials, and the context in which they operate including funding
opportunities, the degree and type of competition they have and their staff.
This functional method addresses the inherit challenge of defining a think tank. As
Simon James said in 1998, "Discussion of think tanks...has a tendency to get bogged
down in the vexed question of defining what we mean by ‘think tank’—an exercise that
often degenerates into futile semantics." It is better (as in the Network Functions
Page 154 of 206

Approach) to describe what the organisation should do. Then the shape of the
organisation should follow to allow this to happen. The following framework (based on
Stephen Yeo's description of think tanks’ mode of work) is described in Enrique
Mendizabal's blog "onthinktanks":
First, policy institutes may work in or base their funding on one or more of:
1. Independent Research: this would be work done with core or flexible funding
that allows the researchers the liberty to choose their research questions and
method. It may be long term and could emphasize ‘big ideas’ without direct policy
relevance. However, it could emphasize a major policy problem that requires a
thorough research and action investment.
2. Consultancy: this would be work done by commission with specific clients and
addressing one or two major questions. Consultancies often respond to an
existing agenda.
3. Influencing/ Advocacy: this would be work done by communications, capacity
development, networking, campaigns, lobbying, etc. It is likely to be based on
research based evidence emerging from independent research or consultancies.
Second, policy institutes may base their work or arguments on:
1. Ideology, values or interests
2. Applied, empirical or synthesis research
3. Theoretical or academic research
According to the National Institute for Research Advancement, a Japanese policy
institute, think tanks are "one of the main policy actors in democratic societies ...,
assuring a pluralistic, open and accountable process of policy analysis, research,
Page 155 of 206

decision-making and evaluation". A study in early 2009 found a total of 5,465 think tanks
worldwide. Of that number, 1,777 were based in the United States and approximately
350 in Washington DC alone.
Argentina
Argentina is home to 122 think tanks; many specializing in public policy and economics
issues, Argentina ranks fifth in the number of these institutions worldwide.
Brazil
Working on public policies, Brazil hosts, for example, Instituto Liberdade, a Universitybased
Center at Tecnopuc inside the Pontifícia Universidade Católica do Rio Grande do
Sul, located in the South Region of the country, in the city of Porto Alegre. Instituto
Liberdade is among the Top 40 think tanks in Latin America and the Caribbean,
according to the 2009 Global Go To Think Tanks Index a report from the University of
Pennsylvania's Think Tanks and Civil Societies Program (TTCSP).
Fundação Getulio Vargas (Getulio Vargas Foundation (FGV)) is a Brazilian higher
education institution. Its original goal was to train people for the country's public- and
private-sector management. Today it hosts faculties (Law, Business, Economics, Social
Sciences and Mathematics), libraries, and also research centers in Rio, São Paulo and
Brasilia. It is considered by Foreign Policy magazine to be a top-5 "policymaker thinktank"
worldwide.
The Igarapé Institute is a Brazilian think tank focusing on public security and policing.
Instituto Acende Brasil (www.acendebrasil.com) focuses on energy policies and is the
only Brazilian think tank dedicated to this economic sector. It produces research and
studies aimed at increasing the level of transparency and influencing the public policies
of the Brazilian energy sector.
Jamaica
The Planning Institute of Jamaica is an agency of the Office of the Prime Minister that is
"committed to leading the process of policy formulation on economic and social issues
and external co-operation management to achieve sustainable development."
Mexico
CIDE is one of the most important think tank institutes. The researching lines are the
"public policies", "public choice", "democracy", and "economy".
CIDAC – The Center of Research for Development (Centro de Investigación para el
Desarrollo, Asociación Civil) is a not-for-profit think tank that undertakes research and
proposes viable policy options for Mexico's economic and democratic development. The
organization seeks to promote open, pluralistic debate pursuing: the Rule of Law &
Page 156 of 206

Democracy, market economics, social development, and strengthening Mexico-United
States relations.
Think Tank Watch
In some countries, organizations have been established that monitor the activities of
think tanks. The best known is the Czech organization Think-Tank Watch.
Page 157 of 206

Page 158 of 206

VII. Collaborative Decision-Making
Group Decision-Making (also known as Collaborative Decision-
Making) is a situation faced when individuals collectively make a choice from the
alternatives before them. The decision is then no longer attributable to any single
individual who is a member of the group. This is because all the individuals and social
group processes such as social influence contribute to the outcome. The decisions
made by groups are often different from those made by individuals.
Group polarization is one clear example: groups tend to make decisions that are more
extreme than those of its individual members, in the direction of the individual
inclinations.
Page 159 of 206

There is much debate as to whether this difference results in decisions that are better or
worse. According to the idea of synergy, decisions made collectively tend to be more
effective than decisions made by a single individual. However, there are also examples
where the decisions made by a group are flawed, such as the Bay of Pigs invasion, the
incident on which the groupthink model of group decision-making is based.
Factors that impact other social group behaviours also affect group decisions. For
example, groups high in cohesion, in combination with other antecedent conditions (e.g.
ideological homogeneity and insulation from dissenting opinions) have been noted to
have a negative effect on group decision-making and hence on group effectiveness.
Moreover, when individuals make decisions as part of a group, there is a tendency to
exhibit a bias towards discussing shared information (i.e. shared information bias), as
opposed to unshared information.
In Psychology
The social identity approach suggests a more general approach to group decisionmaking
than the popular groupthink model, which is a narrow look at situations where
group and other decision-making is flawed. Social identity analysis suggests that the
changes which occur during collective decision-making is part of rational psychological
processes which build on the essence of the group in ways that are psychologically
efficient, grounded in the social reality experienced by members of the group and have
the potential to have a positive impact on society.
Consensus Decision-Making
Formal Systems
Tries to avoid "winners" and "losers". Consensus requires that a majority approve a
given course of action, but that the minority agree to go along with the course of action.
In other words, if the minority opposes the course of action, consensus requires that the
course of action be modified to remove objectionable features.
Voting-Based Methods
Range voting lets each member score one or more of the available options. The option
with the highest average is chosen. This method has experimentally been shown to
produce the lowest Bayesian regret among common voting methods, even when voters
are strategic.
Majority requires support from more than 50% of the members of the group. Thus, the
bar for action is lower than with unanimity and a group of "losers" is implicit to this rule.
Plurality, where the largest block in a group decides, even if it falls short of a majority.
Page 160 of 206

Delphi method this method is a structured communication technique for groups,
originally developed for collaborative forecasting but also used for policy-making.
Dotmocracy a facilitation method that relies on the use of forms called "dotmocracy
sheets" to allow large groups to brainstorm collectively and recognize agreement on an
unlimited number of ideas they have authored.
Decision-Making in Social Settings
Decision-making in groups is sometimes examined separately as process and outcome.
Process refers to the group interactions. Some relevant ideas include coalitions among
participants as well as influence and persuasion. The use of politics is often judged
negatively, but it is a useful way to approach problems when preferences among actors
are in conflict, when dependencies exist that cannot be avoided, when there are no
super-ordinate authorities, and when the technical or scientific merit of the options is
ambiguous.
In addition to the different processes involved in making decisions, group decision
support systems (GDSSs) may have different decision rules. A decision rule is the
GDSS protocol a group uses to choose among scenario planning alternatives.
Page 161 of 206

Gathering
Involves all participants acknowledging each other's needs and opinions and tends
towards a problem solving approach in which as many needs and opinions as possible
can be satisfied. It allows for multiple outcomes and does not require agreement from
some for others to act.
Sub-committee
Involves assigning responsibility for evaluation of a decision to a sub-set of a larger
group, which then comes back to the larger group with recommendations for action.
Using a sub-committee is more common in larger governance groups, such as a
legislature. Sometimes a sub-committee includes those individuals most affected by a
decision, although at other times it is useful for the larger group to have a subcommittee
that involves more neutral participants.
Participatory
Each participant has a say that is directly proportional to the degree that particular
decision would affect the individual. Those not affected by a decision would have no say
and those exclusively affected by a decision would have full say. Likewise, those most
affected would have the most say while those least affected would have the least say.
Plurality and dictatorship are less desirable as decision rules because they do not
require the involvement of the broader group to determine a choice. Thus, they do not
engender commitment to the course of action chosen. An absence of commitment from
individuals in the group can be problematic during the implementation phase of a
decision.
There are no perfect decision-making rules. Depending on how the rules are
implemented in practice and the situation, all of these can lead to situations where
either no decision is made, or to situations where decisions made are inconsistent with
one another over time.
Social Decision Schemes
Sometimes, groups may have established and clearly defined standards for making
decisions, such as bylaws and statutes. However, it is often the case that the decisionmaking
process is less formal, and might even be implicitly accepted. Social decision
schemes are the methods used by a group to combine individual responses to come up
with a single group decision. There are a number of these schemes, but the following
are the most common:
Delegation
An individual, subgroup or external party makes the decision on behalf of the group. For
instance, in an "authority scheme", the leader makes the decision or, in an oligarchy, a
coalition of leading figures makes the decision.
Page 162 of 206

Averaging
Each group member makes their own private and independent decision and all are later
"averaged" to produce a decision.
Plurality
Group members vote on their preferences, either privately or publicly. These votes are
then used to select a decision, either by simple majority, supermajority or
other more or less complicated voting system.
Unanimity
A consensus scheme whereby the group
discusses the issue until it reaches a
unanimous agreement. This
decision rule is what dictates the
decision-making
for most juries.
Random
The group leaves the
For example, picking a
10 or flipping a coin.
choice to chance.
number between 1 and
There are strengths
and weaknesses to each
of these social decision schemes. Delegation saves time
and is a good method for less
important decisions, but ignored
members might react negatively. Averaging responses will cancel out
extreme opinions, but the final decision might disappoint many members. Plurality is the
most consistent scheme when superior decisions are being made, and it involves the
least amount of effort. Voting, however, may lead to members feeling alienated when
they lose a close vote, or to internal politics, or to conformity to other opinions.
Consensus schemes involve members more deeply, and tend to lead to high levels of
commitment. But, it might be difficult for the group to reach such decisions.
Normative Model of Decision-Making
Groups have many advantages and disadvantages when making decisions. Groups, by
definition, are composed of two or more people, and for this reason naturally have
access to more information and have a greater capacity to process this information.
However, they also present a number of liabilities to decision-making, such as requiring
more time to make choices and by consequence rushing to a low-quality agreement in
order to be timely. Some issues are also so simple that a group decision-making
process leads to too many cooks in the kitchen: for such trivial issues, having a group
Page 163 of 206

make the decision is overkill and can lead to failure. Because groups offer both
advantages and disadvantages in making decisions, Victor Vroom developed a
normative model of decision-making that suggests different decision-making methods
should be selected depending on the situation. In this model, Vroom identified five
different decision-making processes.
Decide
The leader of the group uses other group members as sources of information, but
makes the final decision independently and does not explain to group members why
s/he required that information.
Consult (Individual)
The leader talks to each group member alone and never consults a group meeting. S/he
then makes the final decision in light of the information obtained in this manner.
Consult (Group)
The group and the leader meet and s/he consults the entire group at once, asking for
opinions and information, then comes to a decision.
Facilitate
The leader takes on a cooperative holistic approach, collaborating with the group as a
whole as they work toward a unified and consensual decision. The leader is nondirective
and never imposes a particular solution on the group. In this case, the final
decision is one made by the group, not by the leader.
Delegate
The leader takes a backseat approach, passing the problem over to the group. The
leader is supportive, but allows the group to come to a decision without their direct
collaboration.
Decision Support Systems
The idea of using computerized support systems is discussed by James Reason under
the heading of intelligent decision support systems in his work on the topic of human
error. James Reason notes that events subsequent to The Three Mile accident have not
inspired great confidence in the efficacy of some of these methods. In the Davis-Besse
accident, for example, both independent safety parameter display systems were out of
action before and during the event.
Decision-making software is essential for autonomous robots and for different forms of
active decision support for industrial operators, designers and managers.
Page 164 of 206

Due to the large number of considerations involved in many decisions, computer-based
decision support systems (DSS) have been developed to assist decision-makers in
considering the implications of various courses of thinking. They can help reduce the
risk of human errors. DSSs which try to realize some human-cognitive decision-making
functions are called Intelligent Decision Support Systems (IDSS). On the other hand, an
active and intelligent DSS is an important tool for the design of complex engineering
systems and the management of large technological and business projects.
Group Discussion Pitfalls
Groups have greater informational and motivational resources, and therefore have the
potential to outperform individuals. However they do not always reach this potential.
Groups often lack proper communication skills. On the sender side this means that
group members may lack the skills needed to express themselves clearly.
On the receiver side this means that miscommunication can result from information
processing limitations and faulty listening habits of human beings. In cases where an
individual controls the group it may prevent others from contributing meaningfully.
It is also the case that groups sometimes use discussion to avoid rather than make a
decision. Avoidance tactics include the following:
Page 165 of 206

Procrastination
Replacing high-priority tasks with tasks of lower priority. The group postpones the
decision rather than studying the alternatives and discussing their relative merits.
Bolstering
The group may quickly or arbitrarily formulate a decision without thinking things through
to completion. They then bolster their decision by exaggerating the favorable
consequences of the decision and minimizing the importance of unfavorable
consequences.
Denying Responsibility
The group delegates the decision to a subcommittee or diffuses accountability
throughout the entire group, thereby avoiding responsibility.
Muddling Through
The group muddles through the issue by considering only a very narrow range of
alternatives that differ to only a small degree from the existing choice.
"Satisficing"
A combination of the words "satisfy" and "suffice". Members accept a low-risk, easy
solution instead of searching for the best solution.
Trivialization
The group will avoid dealing with larger issues by focusing on minor issues.
Two fundamental "laws" that groups all too often obey:
Parkinson’s Law
"A task will expand to fill the time available for its completion."
Law of Triviality
"The amount of time a group spends discussing an issue will be in inverse proportion to
the consequentiality of the issue."
(For example, a committee discusses an expenditure of $20 million for 3 minutes and
one for $500 for 15 minutes.)
Failure to Share Information
Page 166 of 206

Research using the hidden profiles task shows that lack of information sharing is a
common problem in group decision making. This happens when certain members of the
group have information that is not known by all of the members in the group. If the
members were to all combine all of their information, they would be more likely to make
an optimal decision. But if people do not share all of their information, the group may
make a sub-optimal decision.
Stasser and Titus have shown that partial sharing of information can lead to a wrong
decision. And Lu and Yuan found that groups were eight times more likely to correctly
answer a problem when all of the group members had all of the information rather than
when some information was only known by select group members.
Cognitive Limitations and Subsequent Error
Individuals in a group decision-making setting are often functioning under substantial
cognitive demands. As a result, cognitive and motivational biases can often affect group
decision-making adversely. According to Forsyth, there are three categories of potential
biases that a group can fall victim to when engaging in decision-making:
"Sins of Commission"
The misuse, abuse and/or inappropriate use of information, including:
Page 167 of 206

Belief Perseverance
A group utilises information in their decision-making that has already been
deemed inaccurate.
Sunk Cost Bias
A group remains committed to a given plan primarily due to the investment
already made in that plan, regardless of how inefficient and/or ineffective it may
have become.
Extra-Evidentiary Bias
A group choosing to use some information despite having been told it should be
ignored.
Hindsight Bias
Group members falsely over-estimate the accuracy of and/or the relevance of
their past knowledge of a given outcome.
"Sins of Omission"
Overlooking useful information. This can include:
Base Rate Bias
Group members ignore applicable information they have concerning basic
trends/tendencies.
Fundamental Attribution Error
Group members base their decisions on inaccurate appraisals of individuals'
behavior—namely, overestimating internal factors (e.g., personality) and
underestimating external or contextual factors. (Note: This phenomenon is
reliably observed in individualist cultures, not in collectivist cultures.)
"Sins of imprecision"
Relying too heavily on heuristics that over-simplify complex decisions. This can
include:
Availability Heuristic
Group members rely on information that is readily available.
Page 168 of 206

Conjunctive Bias
When groups are not aware that the probability of a given event occurring is the
least upper bound on the probability of that event and any other given event
occurring together; thus if the probability of the second event is less than one, the
occurrence of the pair will always be less likely than the first event alone.
Representativeness Heuristic
Group members rely too heavily on decision-making factors that seem
meaningful but are, in fact, more or less misleading.
______
Collective Problem Solving
Problem solving is applied on many different levels − from the individual to the
civilizational. Collective problem solving refers to problem solving performed collectively.
Social issues and global issues can typically only be solved collectively.
It
has been noted that the complexity of contemporary problems has exceeded the
cognitive capacity of any individual and requires different but complementary expertise
and collective problem solving ability.
Page 169 of 206

Collective intelligence is shared or group intelligence that emerges from the
collaboration, collective efforts, and competition of many individuals.
In a 1962 research report, Douglas Engelbart linked collective intelligence to
organizational effectiveness, and predicted that pro-actively 'augmenting human
intellect' would yield a multiplier effect in group problem solving: "Three people working
together in this augmented mode [would] seem to be more than three times as effective
in solving a complex problem as is one augmented person working alone".
Henry Jenkins, a key theorist of new media and media convergence draws on the
theory that collective intelligence can be attributed to media convergence and
participatory culture. He criticizes contemporary education for failing to incorporate
online trends of collective problem solving into the classroom, stating "whereas a
collective intelligence community encourages ownership of work as a group, schools
grade individuals". Jenkins argues that interaction within a knowledge community builds
vital skills for young people, and teamwork through collective intelligence communities
contribute to the development of such skills.
Collective impact is the commitment of a group of actors from different sectors to a
common agenda for solving a specific social problem, using a structured form of
collaboration.
After World War II the UN, the Bretton Woods organization and the WTO were created
and collective problem solving on the international level crystallized since the 1980s
around these 3 types of organizations. As these global institutions remain state-like or
state-centric it has been called unsurprising that these continue state-like or statecentric
approaches to collective problem-solving rather than alternative ones.
Crowdsourcing is a process of accumulating the ideas, thoughts or information from
many independent participants, with aim to find the best solution for a given challenge.
Modern information technologies allow for massive number of subjects to be involved as
well as systems of managing these suggestions that provide good results. With the
Internet a new capacity for collective, including planetary-scale, problem solving was
created.
Page 170 of 206

VIII. References
1. https://en.wikipedia.org/wiki/Mass_collaboration
2. https://en.wikipedia.org/wiki/Collaborative_innovation_network
3. https://en.wikipedia.org/wiki/Collective_intelligence
4. https://en.wikipedia.org/wiki/Digital_collaboration
5. https://en.wikipedia.org/wiki/Cloud_collaboration
6. https://en.wikipedia.org/wiki/Open_collaboration
7. https://en.wikipedia.org/wiki/Open-source_model
8. https://en.wikipedia.org/wiki/Crowdmapping
9. https://en.wikipedia.org/wiki/Crowdsensing
10. https://en.wikipedia.org/wiki/Big_data
11. https://en.wikipedia.org/wiki/Data_analysis
12. https://en.wikipedia.org/wiki/Think_tank
13. https://en.wikipedia.org/wiki/Group_decision-making
14. https://en.wikipedia.org/wiki/Problem_solving#Collective_problem_solving
15. https://www.ippr.org/files/publications/pdf/mass-collaboration_July2014.pdf
16. http://pages.cs.wisc.edu/~anhai/papers/mc-survey.pdf
17.http://www.transitsocialinnovation.eu/content/original/Book%20covers/Local%20PDFs/119%
20SF%20Tjornbo%20Potential%20of%20mass%20collaboration%20for%20SI.pdf
Page 171 of 206

Notes
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
Page 172 of 206

Notes
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
Page 173 of 206

Page 174 of 206

Attachment A
Mass Collaboration - Discussion Paper
Page 175 of 206

DISCUSSION PAPER
MASS
COLLABORATION
HOW WE CAN TRANSFORM THE
IMPACT OF PUBLIC FUNDING
Matthew Pike
July 2014
© IPPR 2014
Institute for Public Policy Research

CONTENTS
Summary.............................................................................................................3
Five steps to support mass collaboration................................................................ 3
What central government should do....................................................................... 4
An invitation to action............................................................................................. 6
1. From mass production to mass collaboration................................................7
The cycle of failure: a system that manufactures need............................................. 7
The context for change........................................................................................... 8
Cost, capacity and complexity as drivers of public funding...................................... 9
From ‘command and control’ to bottom-up mass collaboration............................ 12
Models of asset-based development in action...................................................... 13
The collective impact movement........................................................................... 14
Taking collaboration to a mass scale..................................................................... 14
2. The price of everything and the value of nothing.........................................15
Getting the basics right: seven rules of thumb for effective funding........................ 15
Understanding the flaws in ‘payment by results’.................................................... 18
Understanding the price of everything but the (added) value of nothing................. 19
Putting social value centre-stage........................................................................... 20
What shared intelligence makes possible.............................................................. 25
Towards a new Shared Value Act.......................................................................... 28
3. Collective impact...........................................................................................30
The complex, contested nature of social systems................................................. 30
The relevance of quality management thinking and practice.................................. 31
The growth of a new ‘collective impact’ movement............................................... 31
Taking an ecological view...................................................................................... 38
Funders as stewards of system change................................................................ 39
4. Ensuring money follows value: A new model of social finance....................41
Making localisation real......................................................................................... 41
Four models for localisation.................................................................................. 42
Towards a new model of social finance................................................................. 43
Conclusion........................................................................................................... 46
References........................................................................................................47
Appendix: Open Outcomes map, v0.4..............................................................50
1
IPPR | Mass collaboration: How we can transform the impact of public funding

ABOUT THE AUTHOR
Matthew Pike is an experienced social entrepreneur who has been involved in creating more
than 50 new organisations and major programmes, including Unltd, the Social Investment
Business and Big Society Capital. He has also advised most domestic UK government
departments on issues ranging from police reform to personalisation of public services.
ACKNOWLEDGMENTS
At IPPR, I would like to express my thanks to Nick Pearce for giving me the opportunity to
write this paper, Rick Muir for a highly productive sharing of ideas around the relational state
and Kayte Lawton for expert editorial support and advice. I would also like to acknowledge
the support of the Lankelly Chase Foundation and the rich contribution that Julian Corner and
Alice Evans have made to our shared exploration of how we can redesign local systems so
that they work for people with more complex needs.
The work of John Seddon and Vanguard has chimed with my own experience and helped
begin to build a business case for a different way of working. Hugh Biddell, director of public
sector and charities for Royal Bank of Scotland and Dr Russ Bubley of ‘I for Change’ have
offered wise advice on how we can develop new models of social finance that offer a genuine
sharing of risk and return between the state and other sectors. John Tizard has shared his
deep experience of cross-sector partnership working.
Many others have responded to earlier drafts of this paper and / or shared thoughts and
relevant practice. I express my gratitude to them all: Jason Lowther, Birmingham City
Council; Adrian Smith and John Kerridge, Lambeth Council; Charles Uzzell and Fran Hughes,
Torbay Council; Steve Wyler, Locality; Cliff Prior Unltd; Simon Johnson, Advice UK; Tim
Wilson, City Bridge Trust; Alex Massey, ACEVO; Steve James, Avenues Group; Gemma
Hope, Careers Development Group; Rosie Ferguson, London Youth; Joe Irvin, NAVCA; Oliver
Henman, NCVO; Jo Hay, NSPCC; Ralph Michell, Cabinet Office; Robert Pollack, Public
Service Transformation Network; Dominic Williamson, Revolving Doors; Peter Holbrook ,
Social Enterprise UK; Tanya English, St Mungo's; Martin Bright ,The Creative Society; James
Rees, Third Sector Research Centre; Malik Gul, Wandsworth Community Empowerment
Network; Henry Kippin, Collaborate; and Paul Perkins of the Winch. To the many other
people I am certain to have forgotten, my apologies.
ABOUT IPPR
IPPR, the Institute for Public Policy Research, is the UK’s leading progressive thinktank. We
are an independent charitable organisation with more than 40 staff members, paid interns
and visiting fellows. Our main office is in London, with IPPR North, IPPR’s dedicated thinktank
for the North of England, operating out of offices in Newcastle and Manchester.
The purpose of our work is to assist all those who want to create a society where every
citizen lives a decent and fulfilled life, in reciprocal relationships with the people they care
about. We believe that a society of this sort cannot be legislated for or guaranteed by the
state. And it certainly won’t be achieved by markets alone. It requires people to act together
and take responsibility for themselves and each other.
IPPR
4th Floor
14 Buckingham Street
London WC2N 6DF
T: +44 (0)20 7470 6100
E: info@ippr.org
www.ippr.org
Registered charity no. 800065
This paper was first published in July 2014. © 2014
The contents and opinions expressed in this paper are those of the authors only.
2
IPPR | Mass collaboration: How we can transform the impact of public funding

SUMMARY
A moment for transformation
Over the coming five years we have a rare window of opportunity to transform how
government works with others to maximise the life chances of its citizens.
In places across the UK we can see the makings of a new approach to public
services that is characterised by:
• Honesty: that none of us have all the answers to the complex social problems
that now face us – we must work together to pioneer new solutions.
• Confidence: that we already have, between us, the resources and freedoms
required for deep reform – the challenge is one of building more effective
working relationships.
• A sense that a top-down, mass-production model of change has run its
course, and a new model of bottom-up, mass collaboration has come of age.
The rise of mass collaboration can be seen in many countries around the world
that have experienced the same fiscal shock and loss of old certainties over the
past five years. In the United States alone there are more than 500 major ‘collective
impact‘ programmes across whole cities or states, each of which involves all
sectors in tackling a major social challenge. In Africa we see efforts to redesign
market systems to better support the ‘bottom billion’. Here in the UK we have seen
waves of bottom-up innovation over the past two decades, the products of which
are only now entering the mainstream, as local agencies learn the new skills of
collaborative working and new online platforms – from app stores to massive online
learning – become an ever more important part of our lives. Together, these forces
and resources are powering new forms of collaboration that transcend traditional
bounds of time and place.
This paper sets out five common sense steps for building mass collaboration in
towns and cities across the UK. It is an approach that places shared, experiential
learning centre-stage, not egos or preconceived ideas.
Five steps to support mass collaboration
1. Invest in shared institutions that build social capital and engender
supportive working relationships across sectors and hierarchies, such
as teams of supporters around individuals, community anchor organisations,
children’s centres, extended schools and more. Above all, invest in new
‘backbone organisations’ that can mobilise and organise whole-system change
across localities.
2. Understand what help people need in order to help themselves and
discover the existing strengths within people and communities, through an
immersive programme of listening and learning.
3. Harness the new power of ‘big social data’ to turn public funding into a
real-time process of action learning, understanding as much as possible
about activities, outcomes and costs in an area to help design new systems
that give people the help they need in a much smarter way.
4. Provide funding, investment and support to test, grow and scale up what
works better in a local context and cut what isn’t needed or is less effective.
5. Work progressively to use new insight and evidence to help redesign the wider
systems, rules and regulations that hamper local achievement.
3
IPPR | Mass collaboration: How we can transform the impact of public funding

The exciting news is that by focusing on giving people the help they need the first
time around, we can unlock major cost savings. Some of these savings can in
turn help to fund those kinds of early intervention that common sense tells us will
safeguard the future. Projections of cost savings of £16 billion have been made for
more integrated neighbourhood services alone. Reinvesting some savings in deeper
efforts at reform and prevention will deliver much greater savings over time.
Figure 1
Flip the script: reducing costs and improving outcomes through timely support
What central government should do
For the potential of mass collaboration to be realised, the bad habits of the topdown,
mass-production model need to be unlearned by central government. Above
all, the current experiments with ‘payment by results’ for funding public service
providers should be scrapped. There is rising public concern that payment by
results as currently trialled can be highly destructive, with narrow, top-down metrics;
overly large, complex and costly contracts; the temptation to take on easier cases
and further marginalise those who are harder to help; and the ever-present risk of
‘gaming’, over-reporting and fraud.
Payment by success
The alternative model of 'payment by success' proposed here is much tougher
on non-performance than payment by results purports to be. By harnessing the
potential of real-time ‘big social data’, all providers will be brought to account
for the results that matter to service users. Each year, the worst-performing 10
per cent of organisations will be subject to formal review and be at risk of funds
being stepped down or withdrawn, where the facts justify it. In this way, ineffective
services will be cut and more effective services will flourish over time, but with
none of the costs of complex contracting or working capital that, in practice, make
payment by results a blunt tool for promoting the privatisation of public service.
The benefit of this model for providers is that regardless of their sector the majority
can expect longer contracts and deeper collaborative relationships. Those that
deliver more and work in closer partnership with others will access more resources.
They will, however, be accountable as never before.
4
IPPR | Mass collaboration: How we can transform the impact of public funding

Looking to the future, we need a new faith in the benefits of devolving and
spreading power, maximising the incentives and support for collaboration and
using new shared intelligence to achieve the gains in performance that have eluded
different governments to date.
There are five ways in particular in which central government can maximise the
benefits of a new model of bottom-up, mass collaboration.
1. Local by results: devolve power to localities
Central government needs to learn how to step back and empower localities to
work in new ways, in return for the promise of superior results. Local areas require
an unambiguous ‘licence to innovate’.
In return for greatly expanded and more flexible powers to bend and change existing
policies and support systems, localities would work to demonstrate superior results
over time – productivity gains of at least 10 per cent (10 per cent better outcomes for
every pound spent) should be possible over the coming five years.
2. Local on demand: introduce local pooled funding arrangements
Funding should be local by demand, in a much more focused variation on the
‘community budget’. Local agencies should be able to pool and control central and
local funding as the business case demands, with a shared public purse linked to
shared value derived from investment in specific needs or groups of people.
3. Local finance on demand: permit greater use of capital
Local areas also require access to longer-term, flexible capital that is capable of
financing the depth of system change efforts required. Borrowing powers for local
authorities should be relaxed so they can raise new capital against the value of their
assets and the cashflow certainty offered by a five-year agreed budget from central
government.
New models of social investment are required that share risk and reward between
local agencies and outside investors and thus are able to capitalise a much wider
range of local change efforts. While such models would be slightly more expensive
than public borrowing (between 1 and 1.5 per cent per annum more than Public
Works Loan Board rates), they would create new opportunities for people and
organisations to invest in the development of their own localities through not-forprofit
structures.
4. Legislate for a new Shared Value Act
Government should legislate for a new Shared Value Act that would mandate a
new model of public purchasing across the field of public spending. The Act would
set out expectations of the performance of local areas, in terms of the overall
productivity of funding they receive. It would not mandate narrow performance
targets but would expect an account of the impact achieved across the entirety of
local spend, against a range of performance measures agreed on the local level.
Central and local agency purchasing behaviour would apply a common code of
behaviour across all providers, regardless of sector or prior relationships. Tenders
would be smaller. Arbitrary entry requirements for tenders would be forbidden.
Those with greatest expertise would set the tender requirements and service
users would be involved in making key decisions. Providers would expect to
make full financial disclosure. Providers and funders would be expected to work in
partnership as appropriate. An even playing field for all capable providers would be
the guiding principle.
5. Invest in collaboration
The UK is not alone. Across the world, many other societies are faced with similar,
mounting, complex social problems. The most successful among them are rising
5
IPPR | Mass collaboration: How we can transform the impact of public funding

to the challenge through new, cross-sector programmes that place service users
centre-stage. An increasing number of these programmes are achieving very
impressive results – the UK should follow a similar path.
The future lies in bold collaboration: redesigning how whole systems work for us,
from the ground up, in towns and cities across the country. This – not an endless
diet of austerity – is the root solution to the problems that face us. The catch is that
for this to happen on anything like the scale required, central government needs to
devolve power as never before and learn the delicate art of leading by standing back.
For these reasons I call, above all, for the creation of new investment funds
that prioritise collaboration, offering capital that is sufficiently flexible, patient
and adventurous to support the period of transition away from the top-down
approaches of the past and towards the highly collaborative models of the future.
Figure 2
Cultivating healthy services: what do we cut, what do we grow?
An invitation to action
In tandem with writing this paper, I have also created a package of free resources
to help develop mass collaboration within the UK. A new, free platform to collect,
analyse and share social impact data can be found at www.resultsmark.org. It is
available for funders now and will be offered on free public release in the autumn of
2014. Resultsmark offers access to new open source data standards, codeveloped
by a wide range of agencies.
6
IPPR | Mass collaboration: How we can transform the impact of public funding

1.
FROM MASS PRODUCTION TO MASS
COLLABORATION
In the wake of a painful recession and mounting social problems, government needs to
learn to use the funding at its disposal in a far more effective and focused way.
It would be preferable to have more public funding, of course, but the great danger
in the current debate is that in obsessing over the level of funding and the traumatic
consequences of funding cuts we neglect the opportunity to transform services for
the better over the coming years, and reduce costs by many billions of pounds in
the process.
The cycle of failure: a system that manufactures need
What the current process of fiscal retrenchment has revealed in stark relief is that
government all too often funds the wrong things in the wrong ways.
For all the many billions spent on public services and welfare, far too often people
do not get the help they need the first time around. At best, this means that people
cycle back through the system, creating a financial burden that could have been
avoided with better design. At worst, it leads to a whole new set of needs that are
progressively more severe, more complex and therefore harder and more costly to
solve.
Here are some examples, drawn from what could be a much longer list:
• The quality of attachment between a baby and its mother is strongly predictive
of future health or social dysfunction (Moullin et al 2014), yet we fail to offer help
to struggling mothers in the first few critical months after birth – to play, bond
and nurture.
• An experienced teacher can easily identify children with social and emotional
problems early in primary school, yet many such children fall through the net
only to surface again later with far more severe needs.
• Short-term support for young people, such as mentoring programmes, often
has a destructive effect over the medium term, as young people come to feel
the system has no real interest in their welfare– yet we still fund short-term fixes,
not sustained apprenticeships.
• Poor literacy and brain injury are strongly predictive of likely repeat offenders
yet we fail to target prevention. We also know that a home, learning, work and
good social relationships help to break the cycle of reoffending, yet prisoners
are commonly left at the prison door with no properly coordinated support
programme in place.
• Support programmes for families with more complex needs spend money
on navigators of the system, rather than changing the system itself; funding
encourages solving one or two problems chosen by the funder, rather than the
comprehensive, tailored package that is required for a family to make progress.
• Despite substantial budgetary power, older people are still offered highly
traditional options for at-home and residential care, leaving a wider set of
practical and social needs unaddressed.
7
IPPR | Mass collaboration: How we can transform the impact of public funding

The consequence of failures like these is that more and more funding is directed
away from simple needs and earlier intervention and towards higher and more
severe needs, which in turn become much harder and more costly to address.
Figure 1.1
Flip the script: reducing costs and improving outcomes through timely support
There is now an overwhelming imperative to reverse this cycle of repeated failure.
It is not a question of laying blame: no one person or organisation ever set out to
design a system that works to manufacture need rather than address it, just as no
citizen ever set out to be ever more helpless and dependent on that system. But
it is an issue of being very clear about the roots of the problems that now face us.
The core models of public funding require urgent reform. Our top-down approach to
reform no longer works. The good news, described in some depth in this paper, is
that we have all the knowledge and resources at our disposal to pursue a different,
far more effective approach.
The context for change
Reform of funding is the key to achieving a wider transformation of impact and
effectiveness, because it represents one of the most important ways in which
government achieves its stated aims. Government was once as much the provider
as the funder of key public services. But the balance has by now shifted heavily,
with government assuming a role at arm’s length from delivery in many areas of
public services. The extent of the shift from provider to purchaser is uneven from
one service to the next, and the level and nature of involvement by private or social
sector providers can vary widely. But the overall trend is clear: government is now,
above all, a funder of public value, so it is imperative that it learns how to fund in the
most effective way possible.
The sums involved are massive. The public sector as a whole now spends £238
billion each year buying services from a complex ecology of public, private and
social sector enterprises (BIS 2013). The spoils of this procurement bonanza have,
however, gone largely to a small number of large companies. While it is true that
the social sector’s income from the state has grown from £6 billion to £11 billion
over the past decade, this growth should be considered in light of the latest figures
showing that 25 per cent of the procurement spend has been awarded to just 40
companies (Social Enterprise UK 2012).
8
IPPR | Mass collaboration: How we can transform the impact of public funding

The funding environment has also grown much more complex. Traditional sector
boundaries have been blurred by the emergence of new legal forms (such
as community interest companies) and wider development of cross-sector
partnerships. Government also makes these purchases in ever more complex ways:
bulk purchase, brokerage of personal support packages, social impact bonds,
and different forms of procurement – from the highly centralised to the local, spot
purchases, as well as rapidly expanding use of results-based contracts.
Cost, capacity and complexity as drivers of public funding
This overall trend towards more outsourcing and greater complexity of funding
arrangements speaks to three imperatives at the heart of government policy.
First and most obviously there is the fact of shrinking budgets and the prospect of
prolonged austerity. By outsourcing delivery, it is hoped that providers will compete
to offer services at a better price and with a lower risk to the state.
Second, there is a growing consensus that the services of the future need to be
smarter, different and more productive, and this in turn requires access to a set of
capacities that the state alone does not possess.
Third, and more implicit, there is a growing sense within government that it does
not have the answers to the big challenges of 21st-century Britain, especially when
seen against the backdrop of reduced funding, rising demographic pressures
and a society that has grown far more diverse and disconnected. The challenges
are just too complex. Seen in this light, ‘procurement’ can extend beyond simple
outsourcing of delivery and amount to the outsourcing of policy itself, as a way
of finding new ideas as well as new means to address the circumstances we find
ourselves in. To date, perhaps the most spectacular example of this is the awarding
to outsourcing company Capita of the contract to run large swathes of Barnet
Council’s services for the next 10 years (see Patel and Sackman 2013).
The breakdown of command and control
The imperatives now shaping public funding speak all too clearly of a growing
conviction that the apparatus of state funding that was designed to address
the great social challenges of the 20th century has become – except in a small
minority of cases – unfit for purpose. After both the first and the second world
wars, national solidarity lent itself to the pursuit of simple shared goals on a national
basis. National insurance, the NHS and universal education were all great prizes of
a Fordist, top-down, mass-production model of public service delivery. Under this
old model, goals and detailed plans were established at the centre and only then (in
more recent parlance) ‘rolled out’ across the country.
This Fordist model is still, in the overwhelming majority of cases, the one that
determines how government acts. In this respect there is an uncanny continuity
of thought and practice between the Blair administration and the current Coalition
government. It is perhaps a case of old habits of command and control being
reinforced by the requirements of the 24/7 news cycle, which demands politicians
are seen to be ‘doing something’. The focus on payment by (narrowly defined)
results across big national contracts that replaced the much-criticised target-setting
regime of the Blair years bears a strong family resemblance to the ideas of ‘new
public management’ that were tested, some would say to destruction, some years
ago.
So there is a tension at the heart of policy, an acknowledgement that cost, capacity
and complexity should lead us to more radical innovation in the way that funding
works alongside an instinctive adherence to old command and control modes of
thought and action. The result is that government constantly funds the wrong things
9
IPPR | Mass collaboration: How we can transform the impact of public funding

in the wrong ways, with chronic levels of inefficiency building up in the system as a
whole.
Distinguishing between simple and complex problems
Part of the reason for this continued dependence on command and control models
is that there are significant cases where the approach, at least in principle, can work
quite well. This is especially true where there is a simple and commonly shared
goal, and where there is strong existing capacity to deliver the product or service
required. Commodities can be purchased in a top-down manner precisely because
they are commodities – ubiquitous, homogenous, with a range of well-established
providers. An excellent recent example is cloud computing services, which can
be bought by government on a pay-as-you-go, unitised, scalable basis, that will
undoubtedly offer excellent value for taxpayers’ money (and disrupt oligopolic
providers in the process).
Nevertheless in the growing majority of cases, a ‘commodity’ purchasing model
is likely to prove ineffective or positively harmful, as can be seen from the image
below.
Table 1.1
Managing complexity: what’s needed to tackle the problem?
Type A: Simple goal
1: High capacity Command control, eg central
purchasing of commodities
2: Low capacity Capacity building; funding of
innovation
Type B: Multiple or contested
goals
Coordinated delivery across
sectors
Collaborative, bottom-up
strategies for improvement
A common failing in central government policy is to mistake a type-B problem for a
type-A problem. For example, getting more people into work might look at first like
a type-A problem: ‘Provide the right incentives, leverage private sector capacity –
how hard can it be?’ It might be tempting to believe that the difficult cases which
characterise this model can still be further outsourced to the charities and social
enterprises that specialise in supporting such people. However, in practice, work
placement is firmly a type-B problem, given the multiple needs of many long-term
jobseekers, their sustained disengagement from the labour market and the lack of
appropriate job opportunities. For this reason, it calls for both tight cross-sector
collaboration and whole-system innovation of a kind that has not been much in
evidence in the delivery of welfare-to-work programmes to date.
The government’s Work Programme is a classic example of a type-A solution
(prime contractors paid on a narrow results-based tariff) used to address a
type-B problem, where a complex set of support services require highly skilled
codevelopment and coordination. There are many other similar cases. Support
for ex-offenders, which is the government’s current candidate for large resultsbased
contracts, is even more clearly a type-B problem, with a host of attendant
issues ranging from addiction to work and housing that can never be delivered in a
traditional top down way.
Complexity as a defining feature of many public service challenges
The issue that all governments will face in the future is that there are fewer and
fewer type-A problems that lend themselves to a command-and-control response.
For instance, the challenges within adult social care are formidably complex,
compounded by falling budgets and the legendary difficulties of integrating health
and social care successfully at the level of the individual. The set of unhealthy
lifestyles and behaviours that drive the long-term costs of the NHS are firmly in the
10
IPPR | Mass collaboration: How we can transform the impact of public funding

type-B category. In our schools, education secretaries can seek to apply type-A
solutions to the tier of students who are already performing well, but the challenge
presented by the long tail of under-performing students – some 40 per cent of the
whole – is a classic type-B problem. Here there is a dense, interconnected set
of issues related to local job opportunities, parental commitment to their child’s
learning, self-image, learning styles, social networks, poor quality vocational
training, perverse incentives for schools and much more. These challenges will
never be solved from within the confines of Whitehall.
The awkward truth about government funding is not only that it fails to achieve the
good it sets out to achieve but that all too often it actively causes harm, adding to
the very cost and complexity that it seeks to control or address. Government action
is contributing to new levels not just of complexity but of whole-system risk.
This failure flows from a continued inability to invest in prevention rather than cure,
to help solve people’s problems and build their capacity to help themselves in the
moments of transition and crisis that we come up against as children, adolescents,
adults, families and older citizens.
John Seddon, in a resonant phrase, has termed this phenomenon ‘failure demand’,
a repeated failure to get things right for people the first time around (Seddon
2003). It is very hard to estimate the costs of failure demand – just as it is hard to
make a full account of the financial benefits of prevention, given the complexity
and timescales involved. Nevertheless, a recent report which reviewed tens of
thousands of case files for services across the UK arrived at the startling conclusion
that up to 80 per cent of the needs that presented resulted from previous failed
efforts at support (Locality and Vanguard Consulting 2014). In a similar exercise
carried out by Advice UK with its members, 56 per cent of demand requests
were classed as failure demand – wasteful activity that if eliminated with superior
service and system design would result in major cost savings (AdviceUK 2013).
The Locality/Vanguard report estimated a potential saving from better integrated
neighbourhood-level services of some £16 billion – and this is before one considers
the savings potential across wider public services, or the benefits of investment in
early intervention.
The stark truth is that the default command-and-control model of government
simply does not work for an increasing range of problems that are of greatest
importance to British society. What is required is a radical shift in perspective, from
‘us and them’ to ‘we’; a commitment to developing new kinds of joint operation,
across sectors and hierarchies and localities; and a focus on finding solutions
together. But this in turn requires a seachange in how we understand the challenges
placed before us and how we view and use the wider resources at our disposal.
The fiscal crisis is paralysing a more creative view of our combined
resources
From the perspective of combined interests and capacities across public services,
the way in which the fiscal crisis has dominated and almost paralysed contemporary
political debate seems a major misplacement of emphasis.
Yes, government funding is tight, and for that matter personal borrowing is also at
an all-time high. Nevertheless the UK still enjoys unprecedented levels of wealth
and in particular there are sources of private investment – above all from pension
funds and insurance policies that UK citizens pay into – that offer a massive source
of potential regenerative capital in years to come. The issue is less about the
availability of money as such than it is about aligning personal, institutional and
national interests so that the combined funds we do have can be used to release
the resources otherwise locked up in unnecessary costs and waste throughout the
system.
11
IPPR | Mass collaboration: How we can transform the impact of public funding

From the perspective of a potential partnership across sectors, then, it is
abundantly clear that the capacity of hundreds of thousands of organisations
is being squandered. Smaller voluntary and community groups and local small
enterprises, for example, continue to lose out on funding opportunities, despite the
fact that pound-for-pound they so often offer superior value for money (Pike 2004).
A simplistic approach to outsourcing will rarely be the best way to harness these
disparate capacities. For progress to be made on complex challenges, the
outstanding capabilities of different organisations, often from different sectors, will
need to be brought together in tailored combinations, perhaps unique to a particular
set of circumstances. This in turn requires a whole new level of skill in facilitation,
coordination and performance management – a skill set that is very different from
that found in many procurement teams in Whitehall and local government.
From ‘command and control’ to bottom-up mass collaboration
To tackle the complex problems that universal support services are less and less
capable of solving on their own, we need to build partnerships with a complex set
of capacities. This should not be characterised as the simple swing of a pendulum
from ‘centralised’ to ‘localised’, although mass-scale devolution is very much to be
desired. Rather, it amounts to a new amalgam of local and national resources within
a local context, both bottom-up and ‘side to side’.
The police, with others, could rebuild positive social norms, bring back guardian
figures in public spaces, develop social capital, grow a sense of collective efficacy,
reduce opportunities for crime and increase opportunities for positive, diversionary
activities that could get disaffected youth onto new paths.
The health service, with others, could focus on the root causes of ill health, build a
sense of control in the workplace, clamp down on low pay, support healthy lifestyles,
open up amenities of different kinds, and promote healthy living messages.
The welfare system, with others, could support people out of crisis while the
education service, with others, builds up the cultural value that society could place
on skill, ideas, knowledge and learning itself – making learning what it should be,
truly lifelong.
This is not to ignore that there have been serious, effective and at times sustained
efforts across all these areas of partnership opportunity over recent years. But for
the most part these efforts have remained on the margins, often deeply antithetical
to government’s normal ways of doing business. There have been ever-changing
acronyms, new initiatives and new structures, but much less deep embedding of
new, coproductive ways of working.
The root issue here is not one of expertise or even funding, although both matter
enormously. The real issues are honesty and power. Is the state able to admit its
inability to solve the great, mounting social challenges we face on its own and to
decide instead to devolve power to a range of actors capable of discovering the
answers together. Is it able to move with the times and devolve power downwards
and outwards so that the challenges of today become a genuine joint operation?
Is it willing to forge real partnerships, for which the acid tests are where power sits,
how it is used, who makes decisions, and how all are held to account? Instead, all
too often, we have fake partnerships where risk is transferred but the substantive
power to shape different, better outcomes is not.
We need to move from a model of command and control to a new model of
government as a genuine joint operation. This requires a new deepening of
democracy, not just with a radical shift of power outwards and downwards – to
localities, neighbourhoods, citizens, frontline workers and provider organisations –
12
IPPR | Mass collaboration: How we can transform the impact of public funding

ut also through an opening up of new deliberative spaces that enable people of
different backgrounds to come together and work through a new set of answers to
the ever more challenging problems that confront us.
What is required, in short, is a vision of government swapping its command-andcontrol
mode for a new role as equal partner of and investor in society’s capacities
for change and development.
Models of asset-based development in action
In exploring alternatives to traditional command-and-control models of action,
government can draw confidence from the waves of social innovation over the past
two decades that have developed at the margins and since grown to such a level
of maturity that they offer a compelling new way for politicians to connect with the
public on different terms.
The independent living movement, led by people with learning disabilities, has
successfully made a personalised approach to service delivery the growing
orthodoxy of adult social care, even if the challenge of delivering the approach
with real fidelity – and integrity – is ongoing. At the heart of this process is the
space and possibility created for an individual to begin to codesign new support
arrangements in partnership with others. Hallmarks of this radical approach include
an expectation of respect for the individual, for their strengths, their right to direct
how they wish support to be delivered and, indeed, their right to get things wrong
and learn from the experience. This space for respect and agency is ultimately even
more important than any consideration of how money might be placed under an
individual’s control, whether as a personal budget, a professionally managed fund or
any other alternative arrangement.
A very similar set of values and concerns has shaped the development of assetbased
community development in the UK over the past two decades. This includes
work with grassroots social entrepreneurs, community centres, settlements,
development trusts, community enterprises, local co-ops, social firms and multipurpose
housing associations, as well as more traditional community development.
What all these have in common is a focus on the strengths of communities in all
their forms: skills, social ties, land, buildings and public spaces, and stocks and
flows of money, as well as layers of more intangible cultural identity and social
history. A concern for building agency or a ‘can do’ spirit is central, building a set
of social networks that connect people and ideas to relevant resources, as well as
developing skills and promoting positive social norms. All of this wide experience
long pre-dates more recent interest in ‘coproduction’.
As with the personalisation of public services, the point where asset-based
development becomes a real partnership is with a spreading out and sharing of
power, a new kind of partnership with the state, and a grown-up, more equitable
sharing of risk and accountability. This is the case with participatory budgets run
through community panels, in the transfer of land and buildings to ‘community hub’
organisations, and in the colocation of different public services with private enterprises.
Experiments with government-supported time currencies, peer-support networks and
mutual purchasing of care, energy and other services and resources are also important
in supporting the reemergence in new forms of the old cooperative ethos. 1
Internationally, interest in forms of asset-based welfare has followed a similar track,
with a focus on assisting the poorest to build financial assets, knowledge and
skills that equip them to withstand setbacks more easily, as well as to plan with
greater confidence and ambition for their future. This approach has taken off in
countries where the state has made the process real and simple, not just by funding
1 Lambeth Council is being especially creative in this field.
13
IPPR | Mass collaboration: How we can transform the impact of public funding

financial education but also by matching people’s savings on a pound-for-pound
basis – a model that makes intuitive sense for the cash-strapped working poor (see
Schreiner et al 2005).
The development of microfinance – subsidised and championed by state and
philanthropic actors in its early years – bears a similar message. Support for
alternatives to doorstep lending in the UK – including community development
finance initiatives (CDFIs) and credit unions, grassroots microbusinesses, social
entrepreneurship and social investment – have all had a similar partnership with
government and an ability to use money to build a complex, mutually supporting set
of strengths and assets. Similar themes can be seen, for example, in Hernando de
Soto’s work on property rights in the developing world (2002) and Amartya Sen’s
highly influential account of the role of disparate capabilities in building human
freedom and agency (1999).
The collective impact movement
Over the past five years, these disparate movements have shifted into what we can
now view as a decisive new phase of maturity. Many hundreds of different ‘collective
impact’ programmes across the world demonstrate a major alternative to the old
top-down model. In the US alone there are more than 500 programmes working
across whole cities or states, engaging with all sectors and working together in a
concerted, long-term way to change how systems work for people (see Kania and
Kramer 2011). Many of the more mature programmes are achieving 10 per cent
productivity gains and more (Bridgespan Group 2012). As we will see in chapter 3,
early pilot programmes in the UK are also showing great potential, with examples
of cost-efficiency gains of as much as 30 per cent (see for example Stoke-on-Trent
City Council 2013).
Just at the point that mass collaborative models are entering the mainstream in the
arena of public services, they have – with the advent of Apple’s app store, eBay,
Facebook and dozens of other online platforms – become central to 21st-century
capitalism. It requires a very modest leap of the imagination to see such platforms
transforming the capacity of local public services to solve people’s problems in
far smarter ways: charities making new services and apps available online for
independent living; ex-gang members building apps, selling music or finding
partners; social peer support among the most vulnerable. The potential for this
new social technology to help redefine our idea of ‘public service’ – as something
inherently fluid and context specific – is immense.
Taking collaboration to a mass scale
Faced with this formidable potential, we can begin to see beyond the current era of
austerity to a new phase of social as well as economic recovery. In what follows, I
seek to demonstrate how a transformation of the impact and effectiveness of public
funding is within our grasp if we can abandon old ways of working and embrace a
new era of mass collaboration.
The following chapters set out how this can be achieved:
• Chapter 2 is concerned with the mechanics of funding. It sets out a wide range
of ways (ideally to be applied in combination) that can make all public funding
work to achieve better results.
• Chapter 3 homes in on the opportunities to tackle complex social problems on
a local, whole-system basis.
• Chapter 4 reviews how we can pool the funding we have more effectively, how
we can best leverage it through use of social finance, and how we can move
towards a new norm of investing in the productive capacities of people and
communities.
14
IPPR | Mass collaboration: How we can transform the impact of public funding

2.
THE PRICE OF EVERYTHING AND THE
VALUE OF NOTHING
If we aim to achieve a step-change in the productivity of public funding then the key,
as noted in the chapter 1, is to learn how to build pioneering collaborations across the
sectors capable of redesigning how whole systems work for people.
But first, we need to get some of the basics in place. This chapter starts by setting
out seven basic rules of public funding which, if applied on an appropriate scale,
would deliver major productivity gains in and of themselves. Second, we review the
flaws in the payment-by-results model as it is currently practised, before moving on
to review the potential for a bolder approach that puts social value at the centre of
each and every funding decision.
Getting the basics right: seven rules of thumb for effective
funding
1. Identify the right scale of funding – the minimum viable unit
The most glaring failure in current funding practice concerns the scale of funding.
Central government has a growing tendency to seek economies of scale by
bundling ever more contracts together. However, all too often this works to create
a diseconomy of scale at a local level, with costly and damaging fragmentation of
service delivery (see Locality and Vanguard Consulting 2014).
The consequence of pursuing economies of scale at a national level is that
eventually the contracts become so large that they are deemed appropriate for bids
only by large, well-resourced private companies. Smaller organisations can then
only access these contracts through subcontract relationships with lead or ‘prime’
providers. This places these smaller organisations at a serious disadvantage, even
when they are responsible for the large majority of the delivery of the contract. Even
for larger, better-resourced providers, the cost of bidding for the larger contracts
has escalated: bidding costs of more than £1 million are not uncommon, given the
winner-takes-all nature of the contracts on offer. 2 Of course, this is a cost that is
recouped from government contracts at some point in the future.
In no other sector would this approach be deemed appropriate. Indeed, the wider
trend is in the opposite direction. There is a growing preference either to make
spot purchases for specific items through online markets that support price and
value comparison, or to identify specific problems that require a tailored response.
As a first rule of thumb, therefore, funding should always be broken down to the
minimum viable unit of contract.
2. Identify the requirements that are crucial to success
A major second failing – and one which goes hand in hand with the tendency to
put out overly large tenders – is an inability to get the specification of requirements
right. The greater the variety of products and services that are bundled together in a
contract, the greater the chances of buying the wrong thing. As a contract expands,
the level of detail required in the specification extends far beyond the capabilities of
any single purchasing team.
2 Cited by outsourcing companies at an IPPR seminar on collaboration, London, September 2013.
15
IPPR | Mass collaboration: How we can transform the impact of public funding

In practice the problem is much worse than this: in place of detailed requirements
relevant to a specific problem we commonly see a whole set of general
requirements, many of which are of no direct relevance whatsoever to the contract
in question but which effectively exclude a wide range of organisations from
applying. Public funding needs to be far more focused on which capabilities among
prospective providers are essential or desirable for success to be achieved and
failure avoided. Too often funding programmes hide behind generic requirements,
such as turnover, financial reserves and quality marks.
The second rule of thumb is, therefore, that requirements should be specific and
relevant to the tender in question. The process should be similar to that an employer
would follow in setting out specific essential and desirable attributes in a job
description or vacancy advert. An ability to deliver services in accessible local venues,
for example, or to offer a minimum length of contact time with clients, may be far
more relevant than a general ISO standard or minimum level of turnover or reserves.
3. Ensure decisions are made by those with the greatest insight
The chances of getting the specification of requirements right is made still more
remote by the fact that all too often the people making purchasing decisions are
the least well qualified to take on this role. Government delegates decision-making
to a procurement function staffed by people who cannot possibly have expertise,
experience or passion for every issue they will be asked to address. It is therefore
not surprising that in the effort to achieve objectivity and make the process easier
to administer, the process becomes one of ticking boxes and, overwhelmingly,
making decisions on the basis of price, rather than undertaking a more searching
assessment of needs and value.
Commissioners and providers are as one in saying that procurement practice needs
radical reform. The solution is for procurement to become purely a support service,
issuing tenders and managing payments on behalf of others, with decision-making
authority delegated to panels of commissioners and end-user representatives.
So the third rule is that funding decisions should be made by those with greatest
insight, above all with the strong involvement of the ultimate service user.
4. Put providers’ track record at the heart of bid appraisal
Even if these failures with respect to scale, requirements and expertise were to
be corrected, an effective process is still going to be undermined by a general
failure in government funding programmes to take proper account of a would-be
provider’s track record. It is absurd that so much government contracting actively
prevents consideration of an organisation’s previous success or failure, by awarding
so few (if any) ‘points’ to track record in the scoring of tender bids. If you wanted
an extension to your home, you would be quite interested in how often and how
well your prospective builder had done this kind of work before. Indeed, the notion
of not reviewing track record would be utterly alien in any field but public funding.
In the world of finance, for example, large amounts of time and money are spent
conducting assessment and due diligence. The failure to make track record central
to bid appraisal enables better-resourced firms to win contracts in fields where they
have little or no expertise, while firms with long track records of high achievement
are unable to capitalise fully on this inherent strength.
The fourth rule, therefore, is that funding decisions should focus on understanding
the probability of a provider delivering superior results, which can only be assessed
by making track record a central consideration.
5. Make financial information public as open data
Once funding decisions have been made, it is too often the case that government
colludes in keeping the terms of the transaction confidential, an approach that runs
directly counter to government’s own interests in promoting open competition and
16
IPPR | Mass collaboration: How we can transform the impact of public funding

sharing access to important information across the many arms of central and local
government. In the construction industry, by contrast, open-book accountability is
the norm, whereby client and contractor share financial information about costs.
Through any number of online platforms, from eBay through to price comparison
sites, we are used to being able to compare the cost and value for money of
different offers. Furthermore, we now have the ability to take public funding data
and release it as open data for reuse for a wide variety of purposes, such as
benchmarking cost and performance or understanding trends – something that is
clearly in the public interest.
Rule five is thus that all funding decisions and key financial metrics associated with
them should be made publicly available as open data.
6. Invest in expert supplier management
Once contracts are in operation, procurement’s job is only partly done. Much
of the value of the contract will now lie in the quality of its management, and
government as a general rule is poor at supplier management, accepting changes
to contracts that boost providers’ profit margins, as well as poor targeting of
services and poor performance far too often. This failure, just as much as the
manifest failures in procurement outlined above, is to blame for a very long roll-call
of failed programmes, many of them of massive size and an increasing number
subject to fraud investigations. Rule six is thus to invest in boosting the capacity of
government for supply chain management.
7. Promote diverse, open markets tilted towards social value
Even in the purchasing of relatively simple commodities, government procurement
has delivered a double whammy of poor value for taxpayers’ money and market
domination by a small number of monopolistic providers. Any market that is
dominated by a small number of providers is far more likely to deliver poorer results
and higher cost over time.
An instructive example of the dangers of this approach is provided by the field
of children in care. Over the past two decades, the government has opened up
this provision in England in a bid to promote market competition. In practice the
opposite has happened: private entrants have been able to undercut charities
and other not for profit provision on the basis of price alone. Then, once the
competition had been pushed out of the market, a process began whereby control
of was consolidated by a much smaller number of providers, part-owned by a
handful of private equity investors such as Sovereign and 3i (Social Enterprise UK
2012). Now, only 11 per cent of children’s home places are run by charities, while
England’s biggest providers are owned by private equity firms and continue to
expand. The long-term result has been that, because of high costs and very poor
social outcomes, protecting children in care is one of the deepest social and fiscal
challenges facing any part of government.
The business model for almost all these larger private contractors is the same: to
bid aggressively low to win a contract and then to increase margins over the course
of its lifecycle. This inflation applies above all to crucial components that were not
included in the contract specification at the outset. These providers know that
government supplier management is weak and therefore that levels of scrutiny and
accountability will be, at best, light-touch.
For all these reasons, rule seven of public funding is that it should always seek to
encourage an open, diverse market, with a range of providers from a range of sectors.
One way of achieving this would be for government to create an Amazon or
eBay for public value, giving funders access to a single platform that enables
them to click and buy from a range of preapproved providers, as long as detailed
information is offered to support the kind of fine-grained comparison that is needed.
17
IPPR | Mass collaboration: How we can transform the impact of public funding

The government’s recent experiments with the ‘G-cloud’ marketplace, which offers
small companies the opportunity to bid for cloud computing services on an equal
footing to larger competitors, provide one example of what is possible.3
The key to the success of these and other approaches to creating an open and
diverse marketplace – and one that delivers the best results for government and
society – is that they don’t just make it easier for a wider range of organisations to
bid for contracts. They also change the focus from an almost exclusive focus on
price to a concern with price and social value. The challenge of how to place social
value centre-stage in all funding decisions is what we turn to next.
Understanding the flaws in ‘payment by results’
At the heart of the current vogue in government for payment by results (PBR) there
is undoubtedly a good idea trying to get out – namely that government should be
focused not on specifying and paying for activities but on maximising the impact
that its money helps to generate. The more that government can pay for success in
the form of transformed lives and avoid paying for failure, the stronger the attraction
of this approach. Unfortunately in practice things are nothing like as simple.
PBR mechanisms abide by none of the seven rules for effective funding set out
above. Contracts are commonly too large and the entry requirements too onerous.
Decisions are made by the wrong people on the wrong basis. There is nowhere near
enough focus on the likelihood of different providers achieving the targeted outcomes.
PBR mismanages the market
The immediate result of PBR in its current form is to shut out many capable but less
well-resourced organisations from the market and to shoehorn many others into
invidious subcontract arrangements with the larger private companies that have the
necessary capacity to write strong bids and meet tender requirements but rather
less expertise in supporting people with challenging needs. Even before we reach
the starting line, therefore, PBR has removed from contention many of the providers
who might be best equipped to deliver the superior results that government wants
to achieve.
PBR increases inefficiency and risk
PBR is also not the adroit means of shifting risk onto the provider that it purports to
be. Consider the Work Programme – in practice, the government had to abandon
plans to pay providers on a purely PBR basis as none of the would-be providers
was able to raise the finance to cover the full level of working capital required and
the degree of risk involved. Instead, ‘attachment’ fees were introduced – these
are payments for activity which may result in no benefit whatsoever. Given the low
success rate to date, this represents a large sunk cost. There is also the increased
complexity and cost of the tendering process to be paid for – by both parties – long
before any results materialise.
PBR creates narrow and misguided incentives
PBR contracts consistently underestimate the complexity of the issues being
addressed, pinning a type-A solution to a type-B problem, as described in the
previous chapter. Even the altogether more promising Troubled Families programme
pays out on a simplistic tariff basis that is at odds with the hugely complex range of
issues faced – from debt to addiction to mental health and much more.
Narrow metrics work consistently to warp decisions about the priorities for effort
and resources. They introduce strong incentives for providers to favour or ‘cream’
those clients who are easiest to help and to ignore or ‘park’ more difficult cases.
3 But with the key difference that funders do not need to run a second procurement process once they
have identified potential bidders via the marketplace.
18
IPPR | Mass collaboration: How we can transform the impact of public funding

The more successful they appear to be the more likely it is that they are generating
‘soft results’, with clients pushed into an outcome, a job for example, in the full
knowledge that they are likely to be recycled quickly through the system. 4 In the
course of consulting on this paper, several third sector organisations identified this
higher ‘churn’ rate as a direct result of running PBR contracts.
More insidiously still, PBR creates incentives for organisations to exaggerate
their results. One such case came about with the introduction of penalties on
Accidents and Emergency waiting times within the NHS. Faced with a target to
achieve maximum waiting times of three hours, there was an uncanny increase in
the number of hospital admissions for patients waiting for just under three hours,
each of which resulted in a payment of several hundred pounds from increasingly
cash-strapped health trusts. The lesson is that overstretched workers will make
the data fit with the result. When this happens on a large scale, as it can do when
big corporates win big contracts, this constitutes fraudulent activity. Over the past
year we have seen three leading providers investigated for fraudulent reporting
(see BBC News 2013a, 2013b) and problems with the senior leadership of G4S
(see Armstrong 2013), and it is likely that there are many other cases that have
not yet come to light. What is at issue here, in the end, is not the ethics of any one
organisation (worrying as these are), but rather the corrosive effects of the system
within which people and organisations are obliged to operate.
PBR perpetuates a top-down ethos
PBR represents an effort to apply a top-down approach to inherently complex
problems. Therefore it goes fundamentally against the grain of the vision outlined
in chapter 1 of this paper, which calls for a far more collaborative approach to the
complex issues that we see in most if not all of the cases where PBR has been
applied to date. Progress with worklessness, addiction, homelessness, or children
in care requires far more than a focus on one aspect of one individual need. The
difficulty with PBR here is twofold: not only is it clear that we cannot make progress
on these issues without the efforts of multiple actors, but if we start to reward the
efforts of individual providers on the basis of ‘their’ results then we are, in practice,
rewarding them for the efforts of other providers, who may well at the same time be
facing withdrawal of their funding.
PBR is a classic instance of an appealing idea in theory going badly wrong in
practice. This does not mean that we should give up on the idea of placing social
value centre-stage in a new model of public funding. Indeed, I will argue that we
need to ensure each and every funding decision is focused on the results that are
achieved – we just need to go about it in a different way.
Understanding the price of everything but the (added) value of
nothing
If PBR has achieved one thing, it has helped to place a far greater focus on being
clear about the results that are desirable and far more robust in collecting data
about change and impact over time.
Looking back over recent decades we can see that, due to a lack of robust data
about social impact, government agencies have in effect been flying blind, with no
clear idea of the relative effectiveness and value for money of different activities,
services, products and infrastructure they have supported. Government knows (with
honourable exceptions) the price of everything but the added value of nothing. Even
more seriously, government has also often lacked critical data to help safeguard and
improve the outcomes of individuals, families and other groups.
4 From an unpublished 2014 report on payment by results by Revolving Doors.
19
IPPR | Mass collaboration: How we can transform the impact of public funding

The advent of PBR has helped to draw attention to the weakness of the feedback
loops that inform government about the consequences of its actions and funding
decisions. Indeed, this surely offers one powerful explanation as to why there
are quite so many striking cases of poor use of public funds, from high levels of
recidivism amongst ex-offenders to the mounting costs of complex needs across
the funding system. Shared intelligence is akin to a central nervous system; without
it, the capacity required to respond in a timely, effective way simply does not exist.
This lack of rigour in reporting outcomes is a systemic one. Very few provider
organisations are able to provide a robust account of the outcomes that they have
helped to create. Larger, better-resourced organisations tend to be hampered
by the use of unwieldy data systems, several of which often run in parallel, that
were developed for related but different purposes of client management. Smaller
organisations find themselves in an even weaker position, and this lack of capacity
hugely limits the ability of the social sector and many small and micro enterprises
to make the case for a greater share of the public funding pie. Social investors are
similarly very uneven in how they account for the social impact that is generated
by their ‘social’ investments, as they are limited by the ability of their investees to
account for the difference they make.
Putting social value centre-stage
A shared language for social value
In order to move beyond the problems associated with payment by results we need
to move beyond a preoccupation with single metrics to develop a much richer and
more nuanced language to talk about change and value.
Abstract as it may sound, developing a new shared language for talking about
social value is a primary requirement for effective collaboration. Without a common
language organisations will continue to talk past each other and overly simplistic
models of PBR will remain the norm. Without shared standards and tools we will fail
to collect information in a reliable, robust and comparable way. We will also never be
in a position to harness the enormous potential offered by information technology to
draw insights from the massive datasets that are growing exponentially in all areas
of our lives. Big data will remain noise. This is why building a shared language for
social value is of foundational importance.
A new ‘open outcomes’ framework
The only way we are likely to develop a shared language is through an open process
of sharing and peer review. Top-down prescription of outcome measures and other
standards is only likely to lead to yet another reincarnation of top-down target-setting.
Some initial suggestions for this new framework are set out below, drawn from
the work of the open outcomes reference group which was set up in tandem with
writing this report. The framework will be developed further through open peer
review involving many leading national and local organisations. 5
1. who we work with – the beneficiaries
2. their needs – the demand
3. their existing strengths
4. the work we do with beneficiaries
5. the organisational capabilities required for success
6. the resources required to undertake activity
7. investment required to increase impact – the impact investment
8. the wider outcomes developed
9. the economic impacts upon the government and wider society.
5 An early version of a set of recommended outcomes and metrics is set out in the appendix to this paper.
20
IPPR | Mass collaboration: How we can transform the impact of public funding

A menu of outcome metrics and data sources
Attempting this exercise 10 years ago might have presented insurmountable
challenges. Today, however, it is far more realistic. There are powerful tools for sharing
materials that have been developed by academics and practitioners, and there is a
rich range of excellent practice to draw upon. The NHS, for instance, is reviewing the
use of patient-reported outcome tools. The Inspiring Impact Alliance is promoting
results-based ways of working. The Office for National Statistics has been developing
new models of reporting on wellbeing. Big Society Capital is promoting social impact
reporting in the field of social investment. The Dartington Social Research Unit has
developed a set of good practices in the field of children’s development.
There are many hundreds of surveys and other data collection tools that are free to
use, short and reliable. The problem is that they have never been brought together,
made easily accessible and subject to ongoing review from the perspective of
different types of user – funder, investor, citizen, provider or researcher.
Much has already been written about the immense opportunities that flow from the
release of open data, especially government data. Open data is quite distinct from
the reuse of sensitive personal data, held by government, where there is obvious
potential to gain useful insights – for example, tracking school attendance as an
early indicator of the impact of programmes supporting children and young people –
but also serious concerns about potential abuse of that data and about the erosion
of people’s rights to data protection and privacy.
The combination of opportunities to use personal data in new ways and public
unease about data misuse is likely to transform the ways in which personal data
is held and used in the future, with the rise of a new generation of personal data
stores. Under this model – already seen with Mydex, Patients Know Best, Cosy
Cloud and many other innovative startups – ownership and control of data lies with
the individual.
This means that the individual can act as the point of integration for an extraordinary
new array of data:
• Physiological data, for example from wearable technologies of different kinds
that can track metrics such as heart-rate, paces walked, blood glucose levels
and sleep patterns.
• Self-reported data, including survey responses, personal profiles and ‘likes and
dislikes’.
• Remote sensor and third-party app data, such as energy usage, credit scores,
pollution levels and weather patterns.
• Social, locational and time series data, including Google calendars, social media
‘updates’ and mobile phone GPS information.
In the future, ‘slices’ of this data will be shared by individuals with different trusted
supporters as they wish and as the legitimate needs of supporters dictate, offering
vastly greater insights to the different support agencies involved. GPs, for example,
could track for dangerously low or high scores for different physiological metrics,
such as glucose or testosterone, or provide new incentives for successful weight
loss. The individual and their personal data store will have begun to act as the new
nexus of data and therefore of public services, at the heart of a web extending
outwards to include a personal network of supporters that work for them.
A new big data mutual
The opportunity to use new forms of data and new models of ownership and
access to revolutionise public services may sound as if it is some years off, but
it needn’t be. I have worked with a group of organisations, including Lambeth
Council, the Royal Bank of Scotland and many others, to develop a new kind of
21
IPPR | Mass collaboration: How we can transform the impact of public funding

‘big social data’ platform called ResultsMark. 6 ResultsMark offers free tools to all
providers to both collect, analyse and share data, and free tools for individuals to
own, aggregate and share their data as they wish.
Through 2014, the ResultsMark system will be made available to a growing number
of local public agencies as well as bodies working to support the third sector across
the UK. Along with the development of open outcomes standards, the availability of
free data tools removes the crucial obstacles to making impact reporting the norm
across the entirety of public sector commissioning.
A new funding scorecard
New shared standards and data systems give funders a whole new set of options
to define and optimise the kinds of results that they want to achieve. Government
can take a truly strategic view of social value and build questions about potential
and actual value into every decision that is made, from central government and core
funding programmes right through to the business case for supporting an individual
citizen. Such a move has been theorised by experts on public value, such as Mark
Moore and John Bennington (see Moore and Bennington 2011), but we have
until now lacked the means to connect the big picture with detailed practice in an
effective way.
A useful way of beginning to operationalise a view of social value is by adopting a
balanced scorecard like the example below, which is designed to take account of
four interconnected perspectives on overall value.
Figure 2.1
A balanced scorecard of social value
Needs
All public funding should start with a focus on the needs or demand of the
beneficiaries. It is not enough to have general summaries of the needs that people
have (as was common in past forms of joint strategic needs assessments). What is
required is granular, real-time data on people’s needs, their number and intensity.
We then need to understand how quickly and how effectively these needs are
being met, including by using satisfaction ratings. It is useful to see how effective
individual organisations are at solving people’s problems, how many needs are
referred to other organisations, and how organisations work in tandem (or not) in
addressing need. Above all we need to probe the patterns within this demand to
understand what segment of it results from recent systems failure and also what
segment results from historic systems failure – this is the most powerful insight
required to inform redesign of systems in a collaborative way, as will be outlined in
the next chapter.
6 See https://www.resultsmark.org/
22
IPPR | Mass collaboration: How we can transform the impact of public funding

Strengths
The point of timely help is, where possible, to help people to help themselves. It
has become hard in recent years to find any provider that does at least not pay
lip-service to the principle of codesign and coproduction of support services
between user and provider. But a real test of such support is whether people feel
better, more confident and more in control of their lives. Measures of self-esteem,
confidence in one’s own skills and efficacy, grit, zest, perseverance or self-reported
wellbeing are often dismissed by some funders as ‘soft’, but they offer real value
in their own right as measures of capacity for self-reliance, especially over a longer
timeframe. Measures of resilience (an interconnected set of factors that help
people to deal with adversity) and general self-efficacy (the ability to cope with
the different shocks and challenges that life puts in our way) are strongly predictive
of future recovery, wellbeing and – from a government perspective – reduced
dependency on the state.
A public funding regime that places its primary emphasis on solving people’s
problems and recognising – and building on – their strengths as quickly and
effectively as possible will look very different from the rigid, uniform services of the
past. They will be highly fluid and responsive, and give proper weight to measures
of progress that are far less susceptible to misrepresentation by different support
agencies for their own self-interested reasons.
Outcomes
In the third quadrant we have a range of potential outcome measures, each with a
range of potential metrics and data sources. These outcomes depend, logically, on
both the needs of the people being served and the strategic focus and capabilities
of the organisation in question. As we saw with payment by results, it can be very
destructive for a funder to insist on specific outcome measures to the exclusion
of others. Conversely, it can be very positive for funders to work with providers to
understand and find ways to enhance the outcomes in question. It is a process of
action-learning (reflective practice) and improvement from the frontline upwards, not
prescription from the top downwards.
The conventional wisdom among impact measurement experts for planning
outcomes is to use a simple results chain or logic model.
Figure 2.2
A simple results chain
This approach offers some benefits as a conceptual model. However, given the
increasing complexity of social problems, a more collaborative and experimental
model such as that set out below speaks more directly to the experience of frontline
organisations. This ‘collective impact’ way of thinking and acting is central to the
methods of collaborative system change outlined in the next chapter. The progress
metrics used can draw on all four of the elements in the scorecard above, with a
particular focus on needs and strengths, as they offer early, real-time data to help
track the degree of positive or negative value of different actions.
23
IPPR | Mass collaboration: How we can transform the impact of public funding

Figure 2.3
A framework for shared impact
Economics
Finally we turn to the economics of impact, which covers the extent (from a
government perspective) to which cost in the system has changed over time,
through reduced flow of new needs and greater cost-effectiveness in addressing
current need, as well as new sources of economic value, such as new tax revenues
and greater productivity (reduced marginal cost to serve for each citizen).
The first thing to note in this model is that economic return is in quadrant four,
not quadrant one, which is significant. Change efforts that start by targeting a
proportional level of cost reduction (normally a heroic target) almost invariably have
no basis in understanding of the system. Government cannot simply wish unmet
needs, low self-reliance or poor outcomes away and jump straight to saving money.
The process can only work the other way: support more needs earlier and better,
build on more strengths faster, codevelop more outcomes further, and then – only
at the end – by these means deliver the wider economic gains that our current
fiscal position so urgently requires.
A second point to emphasise is that economic impacts are not the same thing as
‘outcomes’, which concern real, measurable (qualitative or quantitative) differences
for the individual or those around them. While some outcomes like ‘school readiness’
read across into economic benefits (in this case, estimated at £1,000 per child 7 ),
others do not, even though they may ultimately contribute hugely to wider economic
returns, such as through children’s commitment to and behaviour within schools.
Social return on investment
There are now a range of methods available to help public funders understand the
financial value of the activities which they have funded, ranging from social return
on investment (SROI) through to related forms of cost-benefit analysis. These
approaches share two obvious risks if used without appropriate expertise and
judgment. First, by conferring superior financial values on certain outcomes they
can lead to precisely the kinds of simplistic, top-down PBR models that we have
already rejected. Second, they can lose all credibility in the act of trying to calculate
the economic value of different programmes. Valuations are highly dependent
upon assumptions made about attribution, deadweight, counterfactual scenarios,
calculations of net present value and so on; depending on the values selected,
the value assessed can vary widely. In practice, given the increasingly complex
7 See New Economy’s ‘Unit Cost Database’: http://neweconomymanchester.com/stories/832-unit_cost_
database
24
IPPR | Mass collaboration: How we can transform the impact of public funding

nature of social systems, it is very challenging – outside the conditions of a robust,
properly controlled trial – to justify the financial valuations often given to different
programmes and organisations.
There are, however, good arguments for using a stripped down version of costbenefit
analysis in the form of a ‘ready reckoner’ to help funders keep track of the
cost burdens upon their systems. This is precisely what a central government team
has just done, with support from New Economy, in creating an unheralded but
seminal piece of work that indicates the financial values associated with different
metrics and which, crucially, shows how these values apply across different local
and central agencies. 8
The key advantage of this approach is that, for the first time, local agencies
can begin to understand and demonstrate the business case for collaboration,
supporting collaboration across different funders to pool budgets and collaboration
in the field across multiple support organisations to work together to support
people’s needs better and more quickly, to build their strengths, maximise their
outcomes and by these means deliver the desired economic return.
What shared intelligence makes possible
So far in this chapter we have discussed the need for an alternative to payment by
results and outlined how to put in place strong foundations that can help to place
social value at the heart of each and every funding decision.
We turn now to the opportunity this presents to transform the impact of public
funding over the years to come.
The value of building reporting capacity
• Boosting productivity: The first point to make is that simply by giving
prominence to social value, funders will help to increase the productivity of
public resources. Funders can perform a vital role in asking their funding
recipients to tell their story – to be clear about what they do, how they do it
and the difference this makes. The positive impact this creates can be further
enhanced by offering free systems and support for impact reporting, such as
ResultsMark.
• Saving money: Many organisations spend up to 10 per cent of their budgets on
collecting the data they need and then reporting on progress to different funders
and supporters, who often have different reporting requirements, each of which
may require a specially tailored report. Once they have been produced and
handed over, there are then the costs to funders in making sense of these reports
and the data behind them. There is, therefore, a major saving of 5–10 per cent
to be made by cutting the costs of data collection with free tools, increasing the
sharing of data between clients and trusted supporters, and so on.
• Leveling the provider playing field: Offering free and open tools for impact
reporting will also help to level out the playing field on which providers from
all sectors operate. Many social sector organisations will be able to make a
stronger case for support, but the biggest gains will be for smaller organisations
who are currently the greatest casualty of government funding cuts. Consider
a project such as a boxing club that offers an invaluable lifeline to young
people who might otherwise be sucked into local gang activities. Using
their smartphone, the project leader is able to keep track of each person’s
development – how they are building up their strengths, solving problems,
building up their own support networks – and how they are beginning to
contribute to wider outcomes that are of great value for the government.
They are able to track how the incidence of crime among all the people they
8 See https://www.gov.uk/social-impact-bonds
25
IPPR | Mass collaboration: How we can transform the impact of public funding

work with changes over time. They can say how many are taking up further
education or apprenticeships, or starting up new businesses. In return, they
are able to get modest funding to maintain the building, train their team of
volunteers and offer new opportunities to develop the skills and expand the
horizons of the people they are working with.
• Mobilising hidden strengths: Equipped with information flowing in real-time
from hundreds of organisations, like the boxing club above, local funders can
begin to build up a map of the strengths and the assets of local communities.
Generally, funders know only about the programmes they fund, and as funding
gets cut so the number and range of programmes off the ‘funder radar screen’
grows exponentially. By using shared reporting to reveal hidden strengths, new
opportunities can be opened up, such as networks of volunteers, underused
facilities or a wider range of specialisms.
Payment for success
Building capacity for reporting on shared impact can produce benefits in and of
itself, but the major opportunity is to promote a mainstream alternative to payment
by results – an approach we might call ‘payment for success’.
The approach has three main components, which are discussed in further detail
below:
1. targeting resources according to need
2. cutting the least effective programmes
3. reinvesting in the most successful and evidence-based programmes.
Targeting resources according to need
With many payment-by-results contracts, just as with many traditional contracts
and service-level agreements, many providers choose to target people who are
considered easier to help. Indeed, in many local authorities there is often a wide
mismatch of resource to need in areas such as children’s and adult services. Those
who shout loudest are supported first; those who are easier to reach go to the
front of the queue. A key feature of a new model of payment for success therefore
involves controlling the types of citizen being supported, drawing on the best realtime
data about the range and level of needs and risks being presented.
The reform of the SureStart programme provides good evidence for the value of
this approach. Early in its life, the better-educated and more assertive section of the
population gained a disproportionate level of access to the support available. The
success of SureStart in investing in outreach and retargeting its efforts on those
with greatest need shows what can be achieved (Ofsted 2012). By reprioritising
support on priority need, a major waste of resource is avoided.
Cutting the least effective programmes
The rhetoric of payment by results is that it only funds success, not failure, but this
is not the case in practice. Payment-by-results contracts are more costly to set up,
they depend on payments being advanced by the funder to help ease cashflow
pressures, and they also need to pay for the increased risk and cost of working
capital to the provider. There is a good chance that the funder ends up making
payments that do not translate into ‘results’.
An alternative approach to trying only to fund success is instead to stop funding
programmes that deliver poorer results, or reach the wrong audience, or which
are unacceptably costly. This can be done by ranking programmes in order of the
added value achieved for an outcome metric and identifying the outliers lying within,
say, the bottom 10 per cent.
Of course, funders should follow good practice before they reduce or cut an
organisation’s funding on grounds of poor results. First, they should make the
26
IPPR | Mass collaboration: How we can transform the impact of public funding

process for decision-making clear and robust and embed it in funding contracts.
Second, they should look for mitigating factors through a deeper review of the
organisation’s data. Is there something unusual about the audience in question?
Is new data likely to be available shortly? Is the programme new? Is it able to
demonstrate progress on lead indicators? Is there an improvement plan in place
and does it show any early results? Is evidence-based practice being used to drive
improvement? Nevertheless, the confidence to cut less effective practice (backed
up by a clear audit trail) will probably do more than anything else to drive superior
use of public funding in the future.
Reinvesting in the most successful and evidence-based programmes
Just as funders can look to cut what is less effective, they can also look to
reallocate additional funding to the highest-performing providers at the other end
of the spectrum – that is, to the top 10 or 20 per cent of performers over time.
These are the candidates for potentially increased support, again subject to a more
intensive exploration of their data. Preference should be given to organisations that
have managed to build up a robust evidence-base on the programmes they offer.
Reducing variation in performance at the provider level
Most public funding is characterised by exceptionally wide variation in terms of the
outcomes achieved by each programme or organisation for each pound spent.
It is a picture that is wholly consistent with the syndrome identified at the start of
this paper of a crisis in social productivity. Productivity will only increase when the
level of variation in performance shrinks and when the mean level of performance
increases. An approach that each year (say, over a three-to-five-year period) aims to
cut the worst-performing 10 per cent and reinvest in the best-performing 10–20 per
cent will be capable of achieving major shifts in performance and productivity.
Working to understand and reduce variation at the individual level
Conventional marketing approaches place individuals into different segments – or
‘personae’ in more recent jargon. Government agencies often adopt an even less
sophisticated approach, placing individuals into a small number of risk bands based
on perceived relative risks and needs. This is a gross simplification. At first glance,
any individual might at first seem to be round about the mean, but for each metric
that we might choose and are able to measure they may well have outlying scores
that vary widely above or below the mean. With access to intelligence about the
different areas of relative risk and strength, it is possible for the first time to respond
to people as people, rather than what are essentially sterotypes, and therefore
support people in a far more timely and tailored way.
Investing in shared learning and evidence-based programmes and practice
A further way of using shared intelligence to drive service improvement is by turning
it into ever more reliable evidence about what works better.
One very welcome development in the past two or three years has been the
attention paid to evidence-based practice. Graham Allen’s taskforce on early
intervention played an important part in making the case for government investment
in what works. 9 Nesta has also had a major role with ESRC in building an emerging
network of What Works Centres. This work has placed a clear focus on building
good standards of evidence for assessing the effectiveness of different programmes
and helped to give prominence to the use of randomised control trials in assessing
the impact of different models of support. The result has been an increase in interest
in evidence-based programmes such as Triple P or Multi systemic family therapy,
as ‘gold standard’ interventions that have been shown to be replicable in other
contexts, with the overall level of outcomes achieved staying consistently high. 10
9 See http://www.alliance4usefulevidence.org/
10 For examples, see http://www.eif.org.uk/ or http://educationendowmentfoundation.org.uk/
27
IPPR | Mass collaboration: How we can transform the impact of public funding

There are also less well-evidenced programmes that still offer great scope for
trialling in other contexts, perhaps initially on a more modest scale. The importance
of this work would be hard to overstate, especially as more programmes gain
access to the resources to evaluate and test scalability and replicability.
What is even more significant is the scope this approach offers for a new kind
of evidence-based practice across all branches of service delivery. While it is
excellent to locate and replicate tried and tested programmes, even more effort
should be given to developing the types of granular methods and approaches
that are replicable in almost all contexts. A good example of this is the discovery
that the quality of teacher feedback to students on their work is one of the best
ways of improving levels of student attainment (Education Endowment Foundation
2014). 11 This simple finding really does have the scope, if applied with rigour and
consistency, to deliver notable improvement in levels of achievement.
Building cultures of learning and improvement
The true benefit of the access to shared intelligence via new technologies and a
new shared language is that it creates the opportunity for delivery organisations
to learn and improve. Unfortunately for most organisations this is not the natural
starting point (although it is a hallmark of the very best). Most organisations
require a nudge, from their funders and their peers, before they begin to make the
emotional and cultural as much as the practical commitment to work in a different,
results-focused way. In this, funders can make all the difference: making reporting
tools available at no cost, mandating that all organisations report on their impact,
cutting the costs of reporting, making ranked performance tables available, sharing
pooled data about what works better. In general, funders are in a great position to
champion an evidence-based approach.
Towards a new Shared Value Act
In this chapter we have reviewed how government needs to go about funding in a
very different way in the future. We’ve defined seven basic rules that government
needs to follow. We’ve explored the pitfalls in the current fashion for payment by
results, and how its core proposition can be applied in a different and better way,
exploiting the exceptional potential of information technology – married with shared,
open reporting standards. These approaches, used in combination, offer a route to
major productivity gains in public funding over a period of three to five years.
A key way of moving this new practice into the mainstream would be to legislate to
place shared value at the heart of all funding decisions, by creating a new Shared
Value Act.
It is true that the current government has already moved to create the Social Value
Act, 12 but this is widely viewed by both funders and providers as lacking real teeth
– relevant only at the pretender stage of public purchasing. If we are serious about
promoting strong, diverse, open markets that are tilted towards social value then
we should enact a robust Shared Value Act that provides an obligation – not just
permission – to take social value into account in making funding decisions. This
does not mean that the lowest bid would always be rejected: smaller charities and
SMEs can often offer exceptional price and superior outcomes, because they carry
a lower overhead. What it would do is ensure proper, rigorous scrutiny of what was
being received for the price on offer.
A new Shared Value Act would apply across all public spending decisions: not just
to service provision, but to the purchase of products and spending on physical
11 For more, see Education Endowment Foundation 2014, which offers a synthesis of the evidence, costs
and benefits of different interventions to boost school attainment.
12 See http://www.legislation.gov.uk/ukpga/2012/3/enacted
28
IPPR | Mass collaboration: How we can transform the impact of public funding

assets as well. The new act would not only require public value to feature in funding
decisions but also in ongoing review processes, requiring a culture of open-book
accountability for both how money has been spent and what results have been
generated. All providers would be subject to the act, regardless of sector, or
whether they are in-house or outsourced.
The act would also require commitment both on the part of those who fund and
those who receive public funding to collaborate to maximise public value. (Although,
as noted in chapter 3 of this paper, additional funding and support should also be
made available in recognition of the increased costs of working with others, that is,
beyond simple actions such as joint referral of clients to relevant support.)
Finally, the act should also set out more general guidance for funders building on
the kinds of good practice outlined above. It should set out what providers can
expect to give and what they can expect in return. Providers of all kinds can expect
heightened accountability for what they do, how they do it and the difference they
make. If their performance is parlous, they can expect their funding to be cut, but
in the majority of cases they should expect the opportunity to learn and improve.
Smaller for-profit and not-for-profit providers should expect an increased share of
funding and the benefit of longer-term contracts, albeit ones that can be terminated
on the grounds of underperformance.
What providers of all kinds should also experience is a major new expectation on
the part of funders to work together to rise up to the complex challenges presented
in 21st-century Britain. This is the theme which we explore in the next chapter.
29
IPPR | Mass collaboration: How we can transform the impact of public funding

3.
COLLECTIVE IMPACT
The need for a systems-based approach
All too often in public policy we reach for a new initiative only to see it bounce off
the problems it seeks to address. Surface language may change, but the more
fundamental ways in which things work remain unaltered. Incumbent culture,
intractable systems and nested sets of social problems trump new possibilities.
If we want to achieve a step-change in performance then we have to raise our level
of ambition and expand our frame of reference, to get to grips with system change.
Many funders have discovered through bitter experience that while they may identify
individual programmes that can often produce substantial benefits for a specific
group of people, the impact this represents in the wider locality is often no more than
a pin-prick. Some funders have responded to the frustration of wrestling with the
system by funding a new class of navigators of the system – ‘brokers’, ‘advisers’ and
so on. But this tends to add still more cost and complexity and fails to address why
the system works in such a complicated and dysfunctional way in the first place. We
need a whole new model for bringing about change at a whole-system level.
We should say at the outset of this chapter that system change is not an easy thing
to achieve, especially in a complex, changing social context. It requires a whole new
level of insight and range of capability than was involved in creating these systems
in the first place. It calls for new models of leadership, new types of support
organisations, and a willingness to commit to the hard grind of patient, long-term
reform. But it can be done and it does deliver results that individualised change
efforts will never be capable of.
The complex, contested nature of social systems
Over the past decade we have seen a major swell of academic interest in the
challenges posed by complex systems (see for example Beinhocker 2006, Geyer
and Rihani 2010, Bourgon et al 2010). The key insight this new work offers is that
complex systems do not reveal all of their secrets easily. They are most certainly
not susceptible to remote policy or research activity but instead require immersive
exploration of a kind that is highly collaborative, non-ideological, reflexive and
diverse in respect of both its participants and its sources of evidence, as suggested
by Michael Hallsworth (Hallsworth 2012).
Human systems are not just complex but hotly contested as well. Social systems
do not lie under one person’s control; there are notable power imbalances, many
different egos and interests are in play, and there is sometimes fundamental
disagreement about how they should work and which outcomes are most
important. There is wide variation not just in outcomes but also in terms of inputs:
humans enter into a system in anything but a uniform condition.
Social systems are not the same as ecological systems or production systems, even
if they do show some clear family resemblance. Peter Checkland’s ‘soft systems
methodology’ emphasised the nature of social systems as social constructs,
offering tools for developing shared meaning and purpose (see Checkland 1989).
Jay Forrester (1971) offered ways to map the emergent properties of complex
systems. Peter Senge (1990) focused on deep reflective learning, drawing
30
IPPR | Mass collaboration: How we can transform the impact of public funding

on a range of systems insights as a praxis for whole organisations or whole
system improvement. Danny Burns (2007), along with the key progenitors of the
personalisation movement, has placed central emphasis on the importance of
unequal power relationships within systems and thus the imperative to manage and
dislodge these. 13
This growing respect for the complexity of social systems should prompt us to
return to the seminal work of perhaps the most important systems thinker and
practitioner of all, W Edwards Deming.
The relevance of quality management thinking and practice
After the second world war, the statistician Deming moved to Japan to advise
companies on how they could bounce back from catastrophic defeat with a new
approach focused on quality and remorseless improvement. Deming did not take
with him a set of templates for how the car industry could leap forward. Rather, he
brought a broader set of principles about how one can attend to systems and, in
particular, how to use quite simple statistical techniques to understand variation in
results and work to reduce them. At the core of his approach and philosophy is a
focus on gathering profound knowledge about systems: how they work, what they
deliver, what the customer wants and the level of variation in the system, as well as
about how people think and operate and how they view the world around them.
What followed in Deming’s wake has been an industry of methods, many of them
proprietary and requiring large amounts of expensive consultancy: TQM, Six Sigma,
Lean, Kanban, Agile, Kaizen and so on. These techniques developed largely out
of a manufacturing context and were coloured deeply by the very special context
of a production line or software coding process. The result has been that the
methods have rarely been applied with equal success in other contexts. It has not
helped that programmes that are necessarily long-term have instead been seized
upon faddishly and applied in short-term, top-down ways. A common failing has
been to underestimate the distinctively different context of social systems. There is
nevertheless a wealth of practical value in the tools of system change – as opposed
to the more elaborate theories and claims constructed around them – that will
surely serve us well, so long as we select with discretion, avoid swallowing any one
methodology whole, and above all adhere to Deming’s core requirement to gain as
much profound knowledge as possible about the system in question.
The growth of a new ‘collective impact’ movement
The key though in recent years has been the growth of what can now be seen as a
global collective impact movement. In international development and environmental
stewardship programmes, in North America and in many other developed countries
across the world, sectors are coming together with a new level of ambition to
change how whole systems work for people and planet. The results in many cases
are highly impressive. 14
In Milwaukee, for example, a cross-sector partnership was formed to tackle a social
epidemic of teen pregnancies. Joint actions such as widescale training for teachers
and a sustained public information campaign codesigned by teenagers have
resulted in a 30 per cent fall in teenage pregnancies.
In East Lake, Atlanta, a new community foundation worked with residents to
develop new plans for mixed tenure housing and support a series of partnershipbased
ventures addressing challenges such as supporting early childhood
education, developing new charter schools, supporting healthy living and new forms
13 See also various papers by Simon Duffy for the Centre for Welfare Reform.
14 For background on all US collective impact case studies, see Bridgespan Group 2012.
31
IPPR | Mass collaboration: How we can transform the impact of public funding

of exercise, and much more. Violent crime has fallen by 95 per cent and welfare
dependency by 59 per cent while educational attainment has been boosted across
the entire school.
In 2004, less than half of Chicago’s youth were graduating from its public schools.
Four out of five students were in receipt of free school meals. In response, a crosssector
partnership led by Chicago Public Schools has worked to open up new
graduation pathways and put in place other support systems. Within the first three
years, graduation rates have increased by 3 per cent (some 13,000 students) and
the programme is on track to deliver a 10 per cent gain.
Parramore in Orlando has increased reading levels by 15 per cent and doubled
maths scores in just three years, while juvenile crime has fallen by 81 per cent.
Herkimer County has focused on young people at risk, reducing the number of
foster placements from 138 to 64 and the number of institutional care places by 55
per cent, with a recidivism rate of just 8 per cent. The City of Memphis put together
a 15-point plan to tackle the US’s second-worst violent crime rate. In response,
violent crime fell by 27 per cent within one year and property crime by 32 per cent.
Within four years the murder rate was at its lowest point for 30 years.
The more comprehensive and long-term the venture, the more impressive the
results are likely to be. An excellent example of this is the Strive partnership that
was developed in 2006 in Cincinnati and northern Kentucky in response to growing
alarm at low educational attainment. As the state president Dr O’Dell Owens put it
the area was ‘programme rich’ but ‘system poor’. Systematic research and a wide
process of deliberation led to the development of wide-ranging plans to support
student progression ‘from cradle to career’. The results of the Strive partnership
continue to be impressive, so much so that the model has already been replicated
in close to 100 other areas. Substantial progress has been made on 40 of 54
indicators, with gains of 10 per cent or more in areas such as school readiness,
school test scores, graduation rates and college enrolment. 15
There are now more than 500 programmes addressing the challenge of whole
system change in the US and the approach is now being taken up in many other
countries across the world. Canada continues to be a pioneer. International
development is adopting some of the core approaches. In the UK, Vanguard
Consulting has worked with councils such as Stoke on Trent as well as third sector
organisations such as Advice UK to experiment with models of whole-system
change. While these approaches are not yet on the scale of what we have seen in
the US, they certainly demonstrate the potential of a whole-systems model to deliver
better outcomes for people at a lower cost. Stoke has prototyped a ‘Rebalance
Me’ programme that aims to integrate support from across a range of disparate
agencies that have historically failed to collaborate fully – including the local authority,
police, the fire service, GPs and housing – using new locality team working. Initial
assessment indicates savings of £14,857 per person per annum, with 5,482
individuals potentially benefiting from the programme across the city as a whole.
What helps to make all these disparate forms of collaborative activity quite so
powerful? In the words of John Kania and Mark Kramer:
‘The power of collective impact lies in the heightened vigilance that comes
from multiple organisations looking for resources and innovations through
the same lense, the rapid learning that comes from continuous feedback
loops, and the immediacy of action that comes from a unified and
simultaneous response from all participants.’
Kania and Kramer 2013
15 See http://www.strivetogether.org/
32
IPPR | Mass collaboration: How we can transform the impact of public funding

Charles Sabel and Jonathan Zeitlin (2011) refer to a new model of ‘experimentalist
governance’ developing in many countries in response to a new level of
appreciation of the challenges presented by complex systems.
‘One significant response is the emergence of a novel, “experimentalist”
form of governance that establishes deliberately provisional frameworks
for action and elaborates and revises these in light of recursive review of
efforts to implement them in various context.’
Sabel and Zeitlin 2012
This ethos of deliberation and adaptive leadership is highly characteristic of how all
these programmes operate to good effect, frequently modifying approaches in the
light of experience. This focus on data and deliberation is one of the core features
that make collaboratives notably different from a plethora of partnership models that
have often proved less than fully effective in the past. There are 10 factors in all that
make models of ‘collective impact’, or ‘collaboratives’, stand out.
1. A shared vision and agenda for action
In all successful collaborations, people work together to a single action plan. Here,
achieving focus is of the essence – homing in on a very clear problem and a target
audience that all participants agree is of critical importance. It is an easy mistake to
make, to take on too broad a canvas and soon become lost in the scale and intricacy
of interconnected systems and problems. The art of effective system change is to
break the problem down and zoom in to a manageable level, while at the same time
keeping in view the question of how strategic issues cut across one other.
In practical terms, this means zeroing in on one distinct segment of clients, such as
a group of people or a theme across a geographic area – say, the needs of children
on the edge of the care system – and considering only this segment in real depth.
Figure 3.1
Changing a system, need by need
33
IPPR | Mass collaboration: How we can transform the impact of public funding

Achieving focus should not, however, mean jumping to conclusions about the
best actions to take. In the best programmes there is an ethos of starting not
with answers or programme ideas or even efforts at theories of change but rather
with burning questions – what do we need to discover or test out that is likely to
prove critical in making things better? The commitment to build shared learning
from the outset contributes to a sense of shared purpose, shared knowledge and
perspective and, in time, a sense of shared ownership of the improvements as they
begin to materialise.
The collaborative exploration of how systems work leads to a new shared
understanding of how and why they work the way they do and how they can
be changed and improved. There are many different tools and approaches for
achieving this. In general, the best start in a highly participatory way, creating largescale
drawings of systems and then delegating more detailed process mapping and
review work to smaller teams. Each group should be empowered not only to map
their part of the system in a precise way but also to work to redesign it.
In a similar way, an extremely effective tool (drawn from the field of agile
development) is to translate analysis of points of failure for the end-user into short
stories about what the system should be capable of delivering: ‘As a Mum with a
family on the edge of the care system I need help with financial advice.’
These two techniques, ‘systems pictures’ and ‘stories’, specify necessary
improvements from the service user’s perspective, and as a result can help to
‘ground’ the work of the collaborative, so there is wide understanding of what
change is required and why.
2. Shared data on needs and impact
All successful collaborations are data-rich and build directly on the set of good
practices outlined in the previous chapter. Indeed, shared data on needs and
outcomes is the lifeblood of every successful collaborative, helping participants
to build baseline data to assess progress; understand people’s needs, goals,
outcomes and ‘lived experience’; offer a feedback loop to test new ideas quickly
and support potential redesign and reprioritisation of effort; help assess the
probability of different outcomes being achieved; and offer evidence to funders and
investors that targeted results of different kinds have been achieved.
Shared data of course is only a starting point – the next step is to translate that as
much as possible into insight. Using classic quality management techniques, we
need to map the flow of people through the system. How much support is being
offered to whom and where? Where are people stuck in the system? How many
of them recycle through the system? In the case of people with mental health
problems, for instance, we might wish to adopt several timeframes for analysis,
charting daily, weekly or monthly cycles through the systems, as well as longer-term
service-user journeys, in order to examine where and how, with what ‘volume’ and
cost, people come into contact with the system. Here, pictures as well as real-time
data visualisations can be very helpful in identifying bottlenecks, critical failure points
and waste of resource and effort.
We also need an overview of variation in performance across a range of dimensions,
including metrics, providers and programmes. We can then home in on the outliers
on the graph, and probe for the reasons and the potential solutions – building
profound and useful knowledge.
As we saw in chapter 1, what these kinds of analysis commonly reveal is that much
of the demand on public services – commonly 50 per cent or more – flows from
failed efforts to help people properly the first time around. This does not just result in
chronic levels of inefficiency, it manufactures ever greater cost and complexity, as well
as increasing alienation from the very systems that are designed to support people.
34
IPPR | Mass collaboration: How we can transform the impact of public funding

Theorists of customer relationship marketing talk about ‘moments of truth’, points
of customer experience that confirm positive or negative views of a service that are
subsequently very hard to shift (see Beaujean et al 2006). However, in this respect
public services are not exactly like customer relationships. If I am stuck in an
endless cycle of conversations with a call-centre, at least I can change my energy
supplier or bank. With public services, on the other hand, where there are no or few
competitors, the result is alienation, cynicism, loss of engagement and ultimately
erosion of legitimacy.
3. A central role for citizens and communities
There have been many partnership programmes in the past, but one thing that has
marred all but the very best is the way in which they have tended to serve their
own interests and become preoccupied with their own challenges and internal
structures. All successful system change requires a shift in power to the subjects
and primary agents of the system in question – its users, citizens and participant
communities. As a matter of routine, system change builds in user panels or forums
to help ensure that the voice of citizens takes centre-stage and grounds each and
every effort at system improvement. Users are the primary source of information
about a failing system: they define what user stories need to be prioritised and they
are the ultimate judge of whether reform efforts are working or not.
4. Long-term, cross-sector engagement
Collaboratives are long-term ventures in their essence, because system change is
long-term work. Time horizons of one, or three or 10 years are required. Progress
can be achieved within a few months, as critical service-user stories and pieces of
process change are prioritised. Notable improvements are possible within three years,
but full consolidation, maintenance and refinement of a new way of working across a
large population can take as long as a decade. Some programmes have ended much
sooner than that only to see their achievements slip into reverse over time.
5. Alignment of resources and activities across many organisations
System change requires lots of different organisations from all sectors to work together
in an expertly coordinated way. This does not mean that all actions need to be taken
with other organisations; ‘paralysis by meeting’ does not have to be the norm for
collaborative projects. The key as we saw in chapter 1 is to be adept at ‘sorting’ the
issue in question: is it simple or complex? Is there existing capacity or not?
Addressing some user stories will require a grant and technical support to one
or two organisations. Many problems can be addressed by achieving better
coordination and alignment of action across organisations, or by making internal
improvements within organisations. In this way, the time and resource-intensive
work of collaborative open innovation can be focused on the truly complex, most
mission-critical issues.
Effective system change requires access to substantial resources, which in turn
places a premium on an asset-based approach, making the best use of the
resources already at hand. This approach has several virtues. First, and most
importantly, it helps to engender a ‘can-do spirit’: it is often possible to make
significant strides in improving services with no additional funding of any kind.
This has obvious appeal at a time of severe funding cuts. Second, an assetbased
approach goes hand in hand with social entrepreneurial approaches that
think ‘resources outwards’ and ‘needs inwards’ at the same time – what might
this unique combination of resource and capacity help us to do differently that our
target audience really needs? An effective system change model therefore needs
to find ways of opening resources up to new uses and to act as a magnet for
entrepreneurial talent. Finally, a truly strategic asset-based approach to systems
change uses the opportunity to combine resources and capabilities as a means
of dealing with and mitigating risk. The more dysfunctional a system is, the more
35
IPPR | Mass collaboration: How we can transform the impact of public funding

complex and intractable the needs being served tend to be, and the more that
risk is soaked up with the system as a whole. An asset-based approach to system
change therefore needs to work in a collaborative way to reduce the chances of
system failure. Often the key is a blending of expertise as much as money. Social
entrepreneurs need accountants; improvement efforts need project management
discipline; complex supply chains need state-of-the-art service integration. The set of
capacities that produced a system’s problems in the first place will never be sufficient
to solve them – the more ambitious we are for change, the greater our determination
should be to bring unlikely capacities together on a greater scale than before.
6. Support from a coordinating backbone organisation
At the heart of every successful collaborative is a ‘backbone organisation’: an
organisation that is dedicated to the task of ensuring effective and broad-based
collaboration in order to achieve shared goals. Backbone organisations can
come from any sector and be national or local, but most commonly they are
not-for-profit. Many national charities can be effective brokers and champions of
local collaboration, but strong, well-respected local organisations can also often
make excellent backbones. It is critical that they command wide respect, are
not hampered by conflicts of interest, and are able to be at once a critical friend,
a tough project manager and an astute problem-solver. The lead workers for
backbone organisations need strong leadership skills. Ideally they will also be good
programme managers, although this role is often taken on by others. Funders play
a critical role in ensuring that backbone organisations have the long-term support
they need to be effective, long-term change agents.
Figure 3.2
Cascading levels of collaboration
Source: Kramer and Kania 2013. Republished with permission from the Stanford Social Innovation Review.
Backbone organisations play several essential roles within the life of a successful
collaborative programme:
• guiding vision and strategy
• supporting aligned activities
• establishing shared measurement practice
36
IPPR | Mass collaboration: How we can transform the impact of public funding

• building public will
• advancing policy
• mobilising funding.
In the field of international development where the term ‘partnership broker’ is
sometimes preferred, typical behaviours associated with the lead worker include: 16
• business manager – ensuring the work remains results-focused
• record keeper – providing accurate, clear and appropriate communications
• teacher – raising awareness and building capacity
• healer – restoring health and wellbeing to dysfunctional relationships
• parent – nurturing the partnership to maturity
• police officer – ensuring that partners are transparent and accountable.
7. A role for advocacy plus innovation plus programme delivery in
combination
Collaborative system change is necessarily made of many strands. Often it does
not conform to the boxes that funders like to put things in. It isn’t focused on one
distinct theme or issue: it isn’t just about programmes; it isn’t just about advocacy.
Most notably of all, it does not conform to one or other theoretical model of ‘social
innovation’ but straddles all kinds of modes, from incremental improvement and
social entrepreneurship to disruptive service redesign. It is messy by nature.
Furthermore, these different modes of intervention can be seen to work together
in a synergistic way. Advocacy, for example, is especially important not only in
maintaining a focus on the voice and experience of the end user but also in building
public goodwill towards the changes in question and supporting action and
behaviour change among the people and communities involved.
System change methods are multistranded in order to impact on different aspects
of the system at the same time. This can be seen, for example, in the Build Initiative,
a collaboration of national funders in the US that supports state efforts to create
comprehensive early childhood systems, which has developed a five-part framework
to help define systems-building and its outcomes: 17
1. Context: changing the political environment that surrounds the system and
affects its success
2. Components: establishing high-performing and high-quality programmes and
services
3. Connections: creating strong and effective linkage across the system
4. Infrastructure: developing the supports the system needs to function effectively
and with quality
5. Scale: ensuring the system is comprehensive and works for all children.
8. Continuous communication
For people to work together in a well-coordinated way, on complex problems, for
any length of time, good communication is critical; conversely, poor communication
is frequently associated with less successful programmes. Programmes must
communicate their plan outwards beyond the limited set of actors involved in
its creation. Success, especially early on, must be disseminated widely and
expressed in terms that everyone can understand. Often, service users are the
best ambassadors, able to convey the difference and importance of programme
improvements far more vividly than any organisational leader, who will tend to talk
about the process and the structural challenges involved. Repeated, excellent
16 For more, see Tennyson 2005.
17 See http://www.buildinitiative.org/Home.aspx
37
IPPR | Mass collaboration: How we can transform the impact of public funding

communication helps to build the sense of common purpose across a collaborative,
it helps to build the base of those involved in its work, and most critically of all it
helps to build a sense of how everyone within a system, service users as much as
anyone else, need to change how they behave.
Of course, without all the other critical factors listed above, good communication
is merely a PR exercise, more likely to build cynicism about the programme than
goodwill towards it. But as one of many key ingredients it plays a crucial role in
sustaining work throughout the long timespans required.
9. Effective leadership, governance and operational management
It is critical that actions are taken up by teams that have permission to develop, test
and put out for review a new solution to a problem. In many cases, these actions
can be taken back into individual organisations by dedicated teams that have been
given the mandate to bring about the improvements required. There is no need
for everyone to spend long hours in meetings. Within individual organisations,
improvement can focus on building ‘repertoire’ – a requisite variety of actions that
people are competent to take in response to the top-priority problems that have
been identified.
This process can also extend to testing out possible new actions that the
organisation might support, in a form of rapid prototyping, or it might escalate to
more radical redesign of internal services. The ultimate goal for one organisation to
pass the baton to the next organisation as ‘cleanly’ as possible, with the presenting
problem dealt with or reduced as far as possible.
Cross-organisational collaboration is focused first on how organisations refer
between themselves or support individual clients jointly. This work may evolve into
looking at how organisations might work together to produce better solutions to
system failures. It can in turn progress towards a more radical redesign of major
parts of the system, although more ambitious reform will normally take longer, cost
more and present higher risk – hence the norm of a more incremental approach to
improvement.
10. Long-term funding
In every single case study of effective collaboration (that is, in the ‘collaboratives’
that have produced major gains in performance), funders, especially independent
trusts and foundations, can be seen to have played a decisive role. The critical help
they can offer includes:
• convening meetings of different people and ideas
• creating incentives and permission for collaboration – without setting the
agenda
• providing funding to test out ideas and build capacity
• investing in backbone organisations that help to organise change
• helping lever in resources of all kinds from all sectors
• helping to ensure that people and communities are placed centre-stage
throughout the process.
Taking an ecological view
The requirement for system change can be an intimidating prospect, especially
when described in the abstract. However, we can draw confidence from the range
of approaches that are likely to characterise the new mainstream of the future.
‘Community anchors’ that offer an increasingly rich set of support services and
colocated organisations. Libraries that offer a frontline for do-it-yourself citizen
self-help. Cross-disciplinary, integrated teams working together at a neighbourhood
level, perhaps with a relationship manage for each and every home. GP surgeries
38
IPPR | Mass collaboration: How we can transform the impact of public funding

offering a ‘wide open door’ to a range of health and wider social support services.
We can be confident that these kinds of approach will be a big part of future
provision for the simple reason that they are already springing up in different
localities across the country.
We can also draw reassurance, insight and inspiration from the way that systems
work in nature to support powerful processes of regeneration. As Frances Westley
has noted in respect of her pioneering work in Canada and elsewhere on systems
innovation, in moving to change and improve systems it is very helpful to maintain
an ecological view of the systems in question (Westley et al 2013). Systems that are
dysfunctional are systems that have been thrown out of balance, with less effective
feedback loops, reduced diversity and greater vulnerability to sudden internal or
external changes.
Improvement efforts must therefore work to build in levels of appropriate variety,
capability, coherence and resilience across the system as a whole.
• By variety, we mean that, given the variety inherent in any given need or goal
due to the necessarily different lives of individual users, there is an appropriate
range of responses available – what we have called repertoire. For example,
positive physical activities for young people are an excellent way of diverting
them from crime and can also offer first steps into new developmental
pathways, but there needs to be a sufficient range of choice of activity, opening
times and venues capable of serving as much of that need as possible – or
other ways of working around the problem (such as free travel cards).
• By capability, we mean that the basic processes, skills and knowledge are
in place to address need effectively the first time around. A lot of systemsimprovement
work focuses on capacity building – via shared learning – so as to
pass the baton to the next provider in the system with as few residual problems
as possible.
• By coherence, we mean that service-user experiences are designed to be
intuitive, sensible and fit together as well as possible: ‘I’ve been helped with
this, now I can go here or here to do this.’
• By resilience, we mean that the system is capable of standing up to excess
demands placed on it, whether by peaks in use or dips in capacity, such as
when staffing levels are low during evenings and weekends.
All systems, whether in nature or in society, move through a natural cycle: they
collapse and then a new phase of regeneration begins. This is the point at which the
UK finds itself now. As ever, the point of greatest stress is where innovation takes
root. In time, the systems that govern our lives will move towards a new stasis, at
which point the role of government will change once more. But in the meantime
we have at least a decade of readjustment to navigate, and for now government is
facing a burning imperative to work in a different way.
Funders as stewards of system change
What then is the role of funders within a paradigm of systems change? Funders
themselves cannot lead system change in the traditional sense. Steering groups
can oversee it and backbone organisations can organise it – very often with
the secondment and full involvement of talented people from the public sector.
Nonetheless, the real work is done at the coalface, in small multidisciplinary teams
who have been given the space and support to do something new.
The role of funders in this context is to provide the ground on which learning and
innovation can take place, while keeping a tight focus on the results that are being
achieved. Above all, they need to learn the art of leading by standing back.
39
IPPR | Mass collaboration: How we can transform the impact of public funding

Power
All systems are bound by complex, unequal power relationships and entrenched
interests. Effective system change is extremely unlikely to happen without these
existing power relationships being disturbed. This is true of any system, but is
doubly the case within the world of public services, where the ability of the service
user to exercise choice, voice and exit is still outweighed by where and how
resources are deployed and accounted for in the system. Hence the emphasis in
chapter 1 of this paper on an explicit transfer of decision-making as a precondition
for successful change.
In any successful process of system change, service users are given the means of
saying what work needs to be done and whether that work has achieved superior
results. This role can be done in a number of different ways, from participatory
budgeting and personal budgets through to feedback via fluid user forums at
one end of spectrum and direct involvement in formal governance at the other.
Processes that call themselves ‘system change’ or ‘service integration’ but in
practice spend months or years talking among themselves before eventually
opening the process out to people and providers from all sectors will not turn public
services around.
Leadership
The truth about systems is that they are more powerful and far more durable than
any individual, even the most powerful politician or business leader. A new level of
humility would serve us well.
New models of adaptive, servant leadership will be critical. There is no place for
certainty. None of us knows all the answers to the problems we face as a society.
We have to work together, as John Dewey once said, to discover the truth.
A new model of leadership is required that involves stepping back. Leadership and
management in the future will surely feel closer to a process of action learning:
frontline staff will lead the process of listening and learning and then adapting the
model, shifting the system little by little.
At the same time, leaders also need to learn how to step forwards into a new set
of often ambiguous collaborative arrangements, in pursuit of shared value and
shared interest. Leaders will have to operate far beyond their official organisational
boundaries, acting more like social entrepreneurs or ambassadors. This work does
not happen without a compass. The acid test, both moral and practical, is simply
this: we should do whatever it takes to give people the help they need and the
outcomes they have a right to expect. The role of funders is to ensure that egos
and interests do not win out over hard facts and practical action.
40
IPPR | Mass collaboration: How we can transform the impact of public funding

4.
ENSURING MONEY FOLLOWS VALUE:
A NEW MODEL OF SOCIAL FINANCE
The agendas for reform outlined in so far in this paper amount to a sea-change in how
government seeks to ensure public money follows maximum added value.
We have described in some detail a process of transformation that starts by
listening to people, understanding what help they need to help themselves,
recognising their strengths, applying real rigour to understanding what works better
and what works less well, and doing more of the former and less of the latter –
incrementally and remorselessly. We have seen how a focus on help and value leads
naturally to a process of bottom-up, highly collaborative system change. At this
point the process unavoidably comes into conflict with the local systems of funding
and control that still rely upon a highly traditional model of top-down control by
central government.
This is not to say that current funding structures present an insurmountable barrier
to the approaches outlined in previous chapters. One of the good aspects of the
Coalition government’s laissez faire stance is that it has opened up plenty of latitude
for local innovation. Nevertheless, the status quo acts as a massive dampener on
local possibilities.
This final chapter is therefore concerned with structural reform of public funding and
finance. It argues above all for a policy of local funding by default – a major move in
power away from the centre towards localities across the UK.
Making localisation real
It is an open secret that government is not at all good at coordination. The need
for government to ‘join-up’ different funding pots and services has been a mantra
– indeed a cliché – of central policy from the time of the genesis of the Social
Exclusion Unit. In practice, however, we have just as often rowed in the opposite
direction.
The effect of uncoordinated and yet overly constrained government funding has
worked to kill off collaborative improvement efforts in five crucial ways:
1. savings fall into different years
2. savings fall across different budgets of different agencies
3. it is hard to get agreement
4. it is even harder to get agreements over the medium term
5. different funders have different reporting arrangements and compliance
requirements.
A good case study is provided by investment in early intervention. When seen
across the range of funding pots that are impacted upon by prudent investment in
early years prevention of future harm, the business case appears incontrovertibly
strong. The problem is that the nature and timescale of the investment required
means that all five of these disruptive factors come into play: the full payback
comes in the medium term (and beyond) and cuts across many budgets. In
particular, without some participation from the Department for Work and Pensions
and the criminal justice system, the payback from savings tends not to cover the
41
IPPR | Mass collaboration: How we can transform the impact of public funding

upfront costs involved. As we will see, this situation serves to frustrate many efforts
to bring third-party finance to bear on social issues – such that the government has
created, for the time being, a fund that serves as a proxy for the wider government
interest that is served by investments such as those in early intervention. But this is
a short-term fix, not a root solution.
Community budgets have been a recent effort to solve this problem, building on
the experience of ‘Total Place’ under the previous administration. This process
has certainly dramatised the opportunities for better joint-working. But its practical
results have, thusfar, been somewhat disappointing. Given the depth of spending
cuts required, it was perhaps inevitable that early attention would fall upon
opportunities to share services and cut costs by working together, but the results to
date have been meagre, viewed against the scale of the overall challenge.
The key weakness with Total Place and community budgets is that they have
approached the problem from the wrong end, starting with money rather than value.
As we have seen, a properly grounded process of service transformation starts
with need and moves towards value, then collaboration and then shared funding. It
cannot work the other way around. Need should dictate function, function should
dictate form, and form should dictate the funding mechanisms required. Attempting
to do the process otherwise all too easily leads to a situation in which where
agencies talk with each other in exclusion of outside voices, structural change is
placed above system change, great job insecurity is created, and years that could
have been spent on frontline reform are squandered.
These frustrations with community budgets are not an argument for holding back on
the pace of devolution of power. Instead, they support an increase in the pace and
depth of change, based on confidence that funders are able to pursue reform in the
ways outlined in this report – not as some form of blueprint but as a guiding set of
heuristics for bringing about effective reform.
Four models for localisation
Local by results: devolve power to localities
Based on an understanding that local government will work in progressive ways to
achieve superior results, central government needs to learn how to step back and
offer a much greater range of discretionary powers. A key innovation will be the power
to set five-year budgets, such that local agencies can invest with greater confidence
for the longer term and escape from the cycle of poor value, short-term procurement.
Such a move should not be understood simply as the swinging of a pendulum from
‘centralised’ to ‘localised’. Not only would localities be expected to work in new
ways but above all they would be expected to show progress in achieving superior
results over time. By results, we mean accountability across all the aspects of the
balanced scorecard set out in chapter 2 and categorically not a return to the narrow,
top-down targets that have plagued previous efforts at improvement of services.
Local on demand: introduce local pooled funding arrangements
Funding should be local by demand, as required by the needs, functions and forms
that emerge from a process of bottom-up system change. Local agencies should be
able to pool and control central and local funding as the business case demands,
with a shared public purse linked to shared value derived from investment in specific
needs or groups of people.
Possible examples of this include:
• Merging health and adult social care funding, for example to support prevention
and reablement for older people.
• Pooling elements of ‘human capital’ funding, such as education, employment
and potentially elements of welfare payments, as well as clawback of savings
42
IPPR | Mass collaboration: How we can transform the impact of public funding

to the Treasury, to boost the chances of those who are not in employment,
education or training (known as ‘NEETs’).
• Pooling funding from social services, the NHS, police, the criminal justice
system and central government to provide proper reward for investment in
early intervention.
Local finance on demand: permit greater use of capital
Borrowing powers for local authorities should be relaxed so they can raise new
capital against the value of their assets and the cashflow certainty offered by a
five-year agreed budget from central government.
By these means, new hybrid forms of social investment can fulfil their potential,
with coinvestment and joint risk-taking between local public funders on the one
hand and cross-sector partners and external investors on the other. There is
a massive untapped potential for local people and organisations to reinvest in
the development of their own localities through not-for-profit structures, for a
4–5 per cent return on investment.
Central government would expect localities to develop cross-sector plans for
improvement To support this move, there should be a new financial settlement
between central and local government, with the creation of five-year capped
budgets, offering scope for localities to plan for both investment and cost saving
over a reasonable timescale.
Local by demand
Use of shared public purses would be still more powerful when set over a five-year
spending review period. In particular, it will make it considerably easier for local
authorities to exploit new and existing sources of investment, designed to achieve
cost savings over the medium term. Of course, this extended predictability and
flexibility of budgets will require new kinds of checks and balances to be put in
place. However, if the accountability framework can be got right, linking it to robust
measurement of all four quadrants of a public value model, then the simple act of
setting five-year budgets can add to rather than undermine fiscal discipline.
Would this approach create a major diseconomy of scale? There is no evidence
to support this concern: stripping out layers of funding bureaucracy will cut
cost; pooling budgets should create cost-efficiency gains; dealing with clients
as a whole set of needs and strengths is both better and more cost-effective.
Furthermore, central government or networks of local agencies can also
create shared infrastructure for accepting and reviewing tenders, as is already
widespread in practice.
Towards a new model of social finance
Integrated, five-year budgets would provide a basis for unleashing major, bottomup,
highly collaborative efforts for reform. The final missing element is a method for
channelling resource to the right people or places at the right time, in the right form,
for the right activities and results.
The rise in importance of social investment is something that was pioneered
by the last government and continued by the current government – a piece of
bipartisanship that has led to the UK now being acknowledged as a world leader
in this field. Social investment in its narrow sense involves the use of finance –
essentially any form of funding that is repaid – to help organisations with a clear
social mission to grow stronger, generate more revenue, deliver stronger impact,
and so be able to pay the investment back.
There are now many funds offering different types of products for different types of
organisations at different points in their lifecycle. Many of the most pioneering are
based in the UK. The overall volumes of current investment are modest – heading
43
IPPR | Mass collaboration: How we can transform the impact of public funding

towards £5 billion in the UK and $20 billion globally – but the potential funds
available are massive and the projected growth rates, if somewhat conjectural, are
impressive, heading towards $1 trillion by 2020 (Saltuk et al 2013). What started as
a marginal activity has now entered the mainstream and, in doing so, has further
blurred the boundaries between sectors and opened up the potential for new kinds
of cross-sector collaboration.
Introducing social impact bonds
The point of disruption has come with a major financial and social innovation, the
social impact bond, the work of UK-based Social Finance. Social impact bonds
(SIBs) differ in structural terms from conventional lending. Normal provision of
working capital takes the form of a line of credit, often from a high street bank
that covers the cashflow pressures of working on a results basis. In a SIB, the
transaction is not with a provider but between funders, investors and a managing
agent. The private investment is designed to achieve greater results and cost
savings than the locality would be able to achieve on its own.
In revenue terms, the investor pays one or more providers on a regular basis, not
on a results basis, so their cashflow is not placed under undue pressure. SIBs thus
recognise the inability of many good providers to work on at-risk basis (and the
potential for great harm were they to try to do so). Investors get paid as the targeted
results are delivered; in the UK version, these returns are capped, so there is an
upper limit to the returns that investors can expect.
Somewhat confusingly, a SIB is not a bond at all but a form of equity that is
considerably more expensive than conventional lending. This is because the
investor is exposed to the risk of potentially significant loss that is therefore
‘compensated’ by returns of up to around 17 per cent (Centre for Social Impact
Bonds 2013). 18 It is this capital structure that has been most widely criticised, both
by commissioners, who are comparing this headline rate to the lower costs of
prudential borrowing by local authorities, and by other investors, who also see more
scope for use of lower-cost lending rather than equity investment.
Despite considerable effort, the take-up of SIBs has thusfar been relatively limited,
although many more are in different stages of development – 53 in total at the
time of writing. There are several reasons for this relatively slow development. The
upfront investment of time and money is considerable. The structure is complex and
needs to be tailored for each transaction. There is controversy around the level of
return on offer to external investors and concern that the model acts as a form of
privatisation, turning government into a purchaser of results rather than a partner in
delivery. Finally, there is the problem that SIBs are not as flexible as they need to be
to address the range of needs faced at a local level.
Where the value lies for public funders
Despite these valid criticisms, there are nevertheless some inherent benefits to
the approach that need to be protected in any future version of SIBs. The biggest
payback is arguably not the capital available but the extra degree of commercial
rigour that is brought to bear on the funding process. The scrutiny and due
diligence offered by seasoned investors and their partners invariably offers an extra
degree of rigour in checking the aspirations set out in a business case. The act of
borrowing and needing to repay capital focuses the mind in a way that grants from
central government seldom do. The result is that a SIB-like review process will often
create a business case that is so robust that it could be funded out of public funds
in its entirety – subject to these funds being available.
On top of this additional rigour is the extra appetite and potential that SIBs create
for judicious risk-taking. It may well be that government can access alternative
18 See https://www.gov.uk/social-impact-bonds
44
IPPR | Mass collaboration: How we can transform the impact of public funding

sources of funding and finance, but it is much less likely to be able to access
flexible working capital that is consistent with the particular demands of system
transformation.
The requirements for a new model
A ‘next generation’ successor to SIBs will need to be capable of responding to
some clear requirements.
• ‘Patient capital’ that looks for a return over a sufficiently long period – typically
four to five years.
• Flexible draw-down, not a one-off investment.
• A ‘fair rate of interest’ in line with government borrowing – perhaps 1–1.5 per
cent above public borrowing (currently 2.86 per cent on a four-year term).
• Capable of investing in a portfolio of activities, not just one intervention.
• Capable of offering small grants to smaller organisations in return for a
contribution to shared impact.
• An overall not-for-profit structure, to build confidence in the approach.
From risk-shifting to risk-sharing
The core problem with SIBs, like the private finance initiative, is that they purport
to shift risk away from the state to the private sector but in reality the transfer of
financial and performance risk leaves the state with a substantial contingent liability.
Risk may be transferred to the private sector, but if the investor is doing their job
properly, this risk will be priced into the cost of capital that the commissioner will end
up paying for. If disproportionate risk is nevertheless transferred, by force of market
power, that will very seldom result in a ‘free lunch’ for the local purchaser: there is
still a wider cost to the state through reduced confidence, reduced capacity, lower
competition or higher prices, as well as the increased perceived risk on the part of
investors and thus a higher cost of capital in the medium term. Whether performance
is good or bad, government will be left dealing with the consequences in the end.
The alternative lies in models of risk-sharing between state, social and private
sectors. Under this model, the state acts as an anchor, providing a guarantee for
a substantial portion of the capital deployed, such that that the finance required
on top can be structured not as expensive equity but rather as a lower-cost, more
flexible form of debt.
Improving the financial structure of SIBs
A new model of social finance will combine guarantees from local public sector organisations,
potential credit enhancement (additional guarantees and perhaps insurance)
from organisations like Big Society Capital and the European Investment Fund, and
debt that earns a return on a performance basis, if a range of results are achieved.
A notable feature of this model is that because the state effectively guarantees
that investors will not lose their capital, a larger set of investors will be encouraged
to invest their capital in programmes of this kind. Indeed, the model could prove
especially attractive to local institutions and individuals that want to put something
back into the local community by reinvesting in local redevelopment programmes.
With sufficient scale and track record, it could also be structured so as to be
accessible to smaller investors through an ISA-like structure.
Just as the model cushions risk for external investors, it also helps to manage risk
for the government, creating the ideal requirements for local system transformation
as outlined above, especially by increasing the rigorousness of the process and
encouraging a culture of well-informed risk-taking and innovation.
The model will be especially potent as local agencies are able to move from the
single-metric outcomes that have characterised most SIB contracts to date, with
a range of funders signing up to joint finance and funding agreements – essentially
45
IPPR | Mass collaboration: How we can transform the impact of public funding

offering ‘pay as you save’ terms to local public agencies: as costs are saved,
as demand falls, the funder pays the investor back, with the aim of generating a
surplus that can be reinvested in further improvements.
The elegance of the model from a public sector risk-management point of view
is that it reaps the benefit of all the investment in strong foundations of impact
reporting outlined in chapter 2. Armed with real-time data, as well as access to the
wider evidence base, investment can be structured so that guaranteed government
repayment is pegged to the worst-case scenario. Put another way, government will
only pay if impact and savings are achieved that are demonstrably beyond what it
has achieved to date on its own.
Towards scale and maturity
The pay as you save investment funds of the future will look a lot more like
traditional prudential borrowing, but with a risk-sharing component that is used to
drive added-value through joint working and shared capacities for improvement.
We need to develop properly scaled funds with a value in the region of hundreds
of millions of pounds. In many cases, complex and costly special purpose vehicle
structures will be avoided, with different local backbone organisations able to act as
a conduit for funds, backed up by clear funding and finance agreements with local
public agencies. Next generation social impact funds are thus ideal mechanisms to
drive the kinds of system-wide improvements outlined in chapter 3.
Asset-backed transactions
It is important to note that scaled funds that invest on a pay-as-you-save basis are
only one way of scaling a new shared-risk approach to local reinvestment. We are
already seeing experiments with new models of shared risk-taking that address
some of the more shameful aspects of the old-style private finance initiative. Many
local authorities are in an ideal position to bring landholdings and planning powers
to the table, to work in partnership with private or social developers, and to act
as a part guarantor to pension funds and other institutional and local investors to
coinvest in ambitious programmes of asset development and wider place-shaping
on a not-for-profit basis. A similar process could extend to partnerships with
banks and specialist investment intermediaries, with a view to recycling capital into
community development finance programmes for unbanked and exploited families
as well as undercapitalised local enterprises.
The key in all these cases is a new spirit of cross-sector partnership and an
equitable sharing of risk and reward that offers no place for excess profiteering.
Conclusion
What this final chapter illustrates is that the UK’s real challenge is not a poverty of
resources but rather a poverty of ambition and imagination. The trick lies in learning
how best to recombine and better coordinate these existing resources.
With imagination, the systems of funding and finance can be redesigned so as to
maximise the potential of deep, collaborative learning and improvement. For this to
happen, we need above all to have the courage to hold new kinds of conversations
and try new joint actions – the more diverse the range of those involved the better.
New, grounded conversations will spark new actions. New actions will change
relationships. These new relationships will in turn change culture. And this new
culture – more respectful and resourceful – will, in the end, trump even the most
engrained systems that govern all our lives.
46
IPPR | Mass collaboration: How we can transform the impact of public funding

REFERENCES
AdviceUK (2013) It’s the System, Stupid! Radically Rethinking Advice, London.
http://www.baringfoundation.org.uk/ITSS.pdf
Armstrong A (2013) ‘G4S UK chief executive Richard Morris resigns’, Telegraph,
24 October 2013. http://www.telegraph.co.uk/finance/newsbysector/
supportservices/10403542/G4S-UK-chief-executive-Richard-Morris-resigns.html
BBC News (2013a) ‘Nine charged over Action 4 Employment fraud probe’, BBC
News website, 26 September 2013. http://www.bbc.co.uk/news/uk-englandberkshire-24291161
BBC News (2013b) ‘G4S boss “sorry” for tagging overcharging’, BBC News
website, 20 November 2013. http://www.bbc.co.uk/news/uk-25023715
Beaujean M, Davidson J and Madge S (2006) ‘The ‘moment of truth’ in customer
service’, McKinsey Quarterly. http://www.mckinsey.com/insights/organization/
the_moment_of_truth_in_customer_service
Beinhocker E (2006) The Origin Of Wealth: Evolution, Complexity, and the Radical
Remaking of Economics, Cambridge, MA: Harvard Business School Press
Bourgon J, Sassine M, Milley P and Comeau J (2010) A New Synthesis of Public
Administration: Compilation of Literature Scans, Ottawa: Canada School of
Public Service
Bridgespan Group (2012) Needle-Moving Collective Impact Guide: The Next
Generation of Community Participation, http://www.bridgespan.org/
Publications-and-Tools/Revitalizing-Communities/Community-Collaboratives/
Guide-The-Next-Generation-of-Community-Participati.aspx
Burns D (2007) Systemic Action Research: A strategy for whole system change,
Bristol: Policy Press
Checkland P (1989) ‘Soft systems methodology’, Human Systems Management,
8: 273–289
De Soto H (2002) The Other Path, New York: Basic Books
Department for Business, Innovation and Skills [BIS] (2013) ‘Using Industrial
Strategy to help the UK economy and business compete and grow’, policy
statement, 11 September 2013. https://www.gov.uk/government/policies/usingindustrial-strategy-to-help-the-uk-economy-and-business-compete-and-grow/
supporting-pages/publishing-government-contracts-to-provide-confidence-tobusiness-investment
Education Endowment Foundation (2014) ‘Teaching and Learning Toolkit’, 2014
update. http://educationendowmentfoundation.org.uk/toolkit/
Forrester J (1971) ‘Counterintuitive behavior of social systems’, Technology Review,
73(3): 52–68
Geyer R and Rihani S (2010) Complexity and Public Policy: A New Approach to 21st
Century Politics, Policy and Society, Oxford and New York: Routledge
47
IPPR | Mass collaboration: How we can transform the impact of public funding

Hallsworth M (2012) ‘How complexity economics can improve government:
rethinking policy actors, institutions and structures’ in Dolphin T and Nash D
(eds) Complex new world: Translating new economic thinking into public policy,
London: IPPR. http://www.ippr.org/publications/complex-new-world-translatingnew-economic-thinking-into-public-policy
Kania J and Kramer M (2011) ‘Collective impact’, Stanford Social Innovation
Review, Winter 2011. http://www.ssireview.org/articles/entry/collective_impact
Kania J and Kramer M (2013) ‘Embracing Emergence: How Collective Impact
Addresses Complexity’, Stanford Social Innovation Review, January 2013.
http://www.ssireview.org/blog/entry/embracing_emergence_how_collective_
impact_addresses_complexity
Locality and Vanguard Consulting (2014) Saving money by doing the right thing:
Why ‘local by default’ must replace ‘diseconomies of scale’, Buckingham.
http://locality.org.uk/wp-content/uploads/Locality-Report-Diseconomies-webversion.pdf
Moore M and Bennington J (2011) Public Value: Theory and Practice, Palgrave
Macmillan
Moullin S, Waldfogel J and Washbrook E (2014) Baby Bonds: Parenting, attachment
and a secure base for children, London: Sutton Trust. http://www.suttontrust.
com/our-work/research/item/baby-bonds/
Ofsted (2012) The report of Her Majesty’s Chief Inspector of Education, Children’s
Services and Skills: Early years, London. http://www.ofsted.gov.uk/resources/
report-of-her-majestys-chief-inspector-of-education-childrens-services-andskills-early-years
Patel R and Sackman S (2013) ‘The One Barnet case heralds local government’s
disappearing act’, New Statesman blog, 1 May 2013. http://www.
newstatesman.com/politics/2013/05/one-barnet-case-heralds-localgovernments-disappearing-act
Pike M (2004) Can Do Citizens, London: Scarman Trust
Sabel C and Zeitlin J (2011) ‘Experimentalism in Transnational Governance:
Emergent Pathways and Diffusion Mechanisms’, working paper no 3, Coventry:
GR:EEN, Centre for the Study of Globalisation and Regionalisation, University of
Warwick
Sabel C and Zeitlin J (2012) ‘Experimentalist Governance’ in Levi-Faur D, The
Oxford Handbook of Governance, Oxford: Oxford University Press
Saltuk Y, Bouri A, Mudaliar A and Pease M (2013) Perspectives on Progress, New
York: JP Morgan and the Global Impact Investing Network (GIIN)
Schreiner M, Sherraden M, Clancy M, Johnson L, Curley J, Zhan M, Beverly SG
and Grinstein-Weiss M (2005) ‘Assets and the Poor: Evidence from Individual
Development Accounts ‘, in Sherraden M (ed) Inclusion in the American Dream:
Assets, Poverty, and Public Policy, Oxford: Oxford University Press
Seddon J (2003) Freedom from Command and Control: A Better Way to Make the
Work Work, Buckingham: Vanguard Consulting Ltd
Sen A (1999) Development as Freedom, Oxford: Oxford University Press
Senge P (1990) The fifth discipline: the art and practice of the learning organization,
New York: Doubleday
Social Enterprise UK (2012) The Shadow State: a report about outsourcing of
public services, London: Social Enterprise UK. http://www.socialenterprise.org.
uk/advice-services/publications/the-shadow-state-report-about-outsourcingpublic-services
48
IPPR | Mass collaboration: How we can transform the impact of public funding

Stoke-on-Trent City Council (2013) ‘Designing Citizen-shaped Services: The
‘Rebalance Me’ Approach’, presentation. http://redditch.whub.org.uk/
cms/pdf/003_Designing_Citizen_Shaped_Services_-_The_Rebalance_Me_
Approach_17_10_2013.pdf
Tennyson R (2005) The Brokering Guidebook: Navigating effective sustainable
development partnerships, International Business Leadership Forum.
http://thepartneringinitiative.org/w/resources/toolbook-series/the-brokeringguidebook/
Westley F, Tjornbo O, Schultz L, Olsson P, Folke C, Crona B and Bodin Ö (2013) ‘A
Theory of Transformative Agency in Linked Social-Ecological Systems’, Ecology
and Society, 18(3). http://www.ecologyandsociety.org/vol18/iss3/art27/
49
IPPR | Mass collaboration: How we can transform the impact of public funding

APPENDIX:
OPEN OUTCOMES MAP, v0.4
The open outcomes process is designed to build consensus through ongoing peer
review of shared data and reporting standards. Organisations will always be able to
choose metrics and data sources that are appropriate to them and their users, but
the provenance and reliability of this data will be clear to all those that use it. Where
possible they will draw on shared metrics (with data sources recommended for the
users in question) and existing good data sources as set out below.
Social, environmental and economic outcomes and metrics
Social
1. Right start in life – weight, secure attachment, health review
2. School readiness – letters, shapes, motor skills, reading books, number recognition
3. School commitment – attendance, effort, behaviour
4. Educational attainment – grades and qualifications, by stage of education
5. Can do – control, self-esteem, general self-efficacy, range of personal skills/
strengths, by perceived self-efficacy
6. Employability – job search, interview skills, communication skills, attitude and
motivation, task specific skills
7. Work – net income, job security, locus of control, training opportunities
8. Money management – using bank accounts, understanding of the mechanics of
credit, confidence in tasks such as filling out financial forms, drawing up a budget
9. Living within means – household income, household spend across main
items, percentage of income spent on annual debt repayment, number of
months arrears on debt repayment, number of months arrears on rent payment,
percentage of income spent on energy costs
10. Access to financial services – cost of credit, use of a range of financial
products
11. Home – secure tenure, satisfaction, suitable, state of repair
12. Independent living – self-control, wellbeing, activities of daily living, access to
local shops and services, falls
13. Healthy living – ‘5+ a day’, sleep, activity, alcohol, smoking
14. Health status – self-rated health, BMI, resting pulse, blood pressure, registered
with GP
15. Mental wellbeing – mental wellbeing, clinical condition, depression, access to
appropriate support
16. Managing long-term health conditions – control over medical support,
access to information, satisfaction with support, self-confidence in managing
condition, reduced use of hospital
17. Substance abuse – level of drug use, drinking, smoking, hospital use
18. Wellbeing – with life, family, friends, social activity
19. Social connections – number of intimate friends, wider network, level of trust,
to friends, wider network and others, plus perceptions on locality
50
IPPR | Mass collaboration: How we can transform the impact of public funding

20. Meaningful activity – hours a week involved in art/music, sport, social activity
21. Cultural experience – quality of overall experience, risk and innovation,
production quality, access
22. Civic involvement – volunteering, voting, governance, politics/campaigning
23. Rights and responsibilities of citizens – understand welfare rights, civil rights,
wider legal rights, information on opportunities for engagement
24. Safety – law abiding, fear of crime, collective efficacy, victim of crime
25. Perception of local area – place survey
26. Enterprise development – businesses started, business sustained, jobs
created, jobs sustained, investment accessed, growth in capabilities (number
and level, change in turnover, confidence about future)
27. Access to information – access to the internet at home, access to the internet
elsewhere, confidence in finding the information you need, perceived access to
information
28. Improvements in policy and legislation – number of supporters, adoption of
policy, legislation/policy guidance, adoption of key actions
29. Housing supply – new units developed, units sustained, BREEAM rating, 19
average gross square metres, use of environmentally responsible construction
techniques, area of brownfield or previously contaminated land reused
Environmental
30. Improved access to and enjoyment of the natural environment – number
of people visiting and number of visits to conserved spaces; cost of entry;
satisfaction rating
31. Reduced personal impact on the environment – number of people who
recycle, number of people who actively attempt to reduce waste and water
usage, number of people using sustainable transport, number of people who
actively attempt to save energy and minimise their carbon footprint, amount of
energy saved through energy efficiency improvements
32. Improved water use and efficiency – volume of water consumed, volume of
water recycled, volume of rainwater harvested, volume of water saved through
efficiency schemes, volume of wastewater, method of discharge, impact on
locality, measures of local pollution levels
33. Improvements in general waste and recycling – overall waste: percentage
recycled, reused, donated, going to landfill; input materials: volume and
proportion, from recycled/reused sources
34. Reduction in harmful waste and pollution – reduction in chemicals (eg
nitrogen oxide, sulpher dioxide), particulate material, ozone depletors, toxic
and chemical emissions to water, soil reductions (volume and type), hazardous
waste, spills, volume of harmful waste responsibly disposed of, remediation of
environmental damage from pollution
35. Conservation of natural spaces, natural heritage and biodiversity – area of
natural space or heritage: habitats, forests, water bodies, coastlines conserved,
area of natural space or heritage restored or created, area of derelict or
brownfield sites converted, number of trees planted; population numbers:
changes in wildlife/plant species, levels of biodiversity; air quality measures,
number of visitors to conserved spaces
36. Organic farming – volume of organic produce, area of land farmed sustainably,
associated reductions in greenhouse gas emissions and environmental damage
(reductions in use of fertiliser, mitigation of soil erosion, meeting recognised
standards for sustainable agriculture)
19 See http://www.breeam.org/about.jsp?id=66
51
IPPR | Mass collaboration: How we can transform the impact of public funding

37. Increased generation of renewable energy – megawatt hours of renewable
energy generated, greenhouse gas emissions, sale or retirement of certified
emissions reductions, lifetime greenhouse gas emissions (of project, installation,
product)
38. Buildings meet environmental standards – BREEAM rating, energy use and
onsite energy generation, percentage of building with natural light, natural
ventilation, volume of water consumed, recycled on site, volume of waste
produced, recycled, reductions in greenhouse gas emissions and pollution, area
of brownfield or previously contaminated land reused, populations of species of
plants/animals conserved
39. Sustainable transport – provision of sustainable transport options; uptake of
sustainable transport alternative; percentage of the population walking, cycling,
using public transport; reduction in levels of unsustainable company travel by air
miles and car miles; related reduction in greenhouse gas emissions; impact on
locality (measures of local pollution levels and consequences)
Economic
40. Good employer – average annual salary, average hours across all contracts
(including zero hours), ratio of lowest to highest paid, tax payment as
percentage of turnover in tax jurisdiction, amount spent in area of presence as a
percentage of local turnover (salaries, goods and services etc).
52
IPPR | Mass collaboration: How we can transform the impact of public funding

Attachment B
Mass Collaboration Systems
on The World Wide Web
Page 177 of 206

Mass Collaboration Systems on the World-Wide Web
AnHai Doan 1 , Raghu Ramakrishnan 2 , Alon Y. Halevy 3
1 University of Wisconsin, 2 Yahoo! Research, 3 Google Inc.
The Age-Old Practice of Mass Collaboration is Transforming the Web and Giving Rise to a New Field
Mass collaboration systems enlist a multitude of humans
to help solve a wide variety of problems. Over the
past decade, numerous such systems have appeared on
the World-Wide Web. Prime examples include Wikipedia,
Linux, Yahoo! Answers, Amazon’s Mechanical Turk,
and much effort is being directed at developing many
more.
As is typical for an emerging area, this effort has appeared
under many names, including peer production,
user-powered systems, user-generated content, collaborative
systems, community systems, social systems, social
search, social media, collective intelligence, wikinomics,
crowd wisdom, smart mobs, crowd-sourcing,
and human computation. The topic has been discussed
extensively in books, popular press, and academia (e.g.,
[31, 32, 25, 2, 36, 17, 3, 7]). But this body of work has
considered mostly efforts in the physical world (e.g., [31,
32, 25]). Some do consider mass collaboration systems
on the Web, but only certain system types (e.g., [34,
30]) or challenges (e.g., how to evaluate users [14]).
This survey attempts to provide a global picture of
mass collaboration systems on the Web. We define and
classify such systems, then describe a broad sample of
systems. The sample ranges from relatively simple wellestablished
systems such as reviewing books to complex
emerging systems that build structured knowledge bases
to systems that “piggy back” on other popular systems.
We then discuss fundamental challenges such as how to
recruit and evaluate users, and to merge their contributions.
Finally, we discuss future directions. Given the
space limitation, we do not attempt to be exhaustive.
Rather, we sketch only the most important aspects of
the global picture, using real-world examples. The goal
is to further our collective understanding – both conceptual
and practical – of this important emerging topic.
1. MASS COLLABORATION SYSTEMS
We define mass collaboration (MC) systems, then discuss
how to classify them using a set of dimensions.
1.1 Defining MC Systems
Defining MC systems turns out to be surprisingly
tricky. Since many view Wikipedia and Linux as wellknown
MC examples, as a natural starting point, we can
say that an MC system enlists a mass of users to explicitly
collaborate to build a long-lasting artifact that is
beneficial to the whole community.
This definition however appears too restricted. It ex-
.
cludes for example the ESP game [33], where users implicitly
collaborate to label images as a side effect while
playing the game. ESP clearly benefits from a mass
of users. More importantly, it faces the same humancentric
challenges of Wikipedia and Linux, such as how
to recruit and evaluate users, and to combine their contributions.
Given this, it seems unsatisfactory to consider
only explicit collaborations; we ought to allow implicit
ones as well.
The definition also excludes, for example, an Amazon’s
Mechanical Turk-based system that enlists users
to find a missing boat in thousands of satellite images
[20]. Here, users do not build any artifact, arguably
nothing is long lasting, and no community exists
either (just users coming together for this particular
task). And yet, like ESP, this system clearly benefits
from users, and faces similar human-centric challenges.
Given this, it ought to be considered an MC system,
and the goal of building artifacts ought to be relaxed
into the more general goal of solving problems. Indeed,
it appears that in principle any non-trivial problem can
benefit from mass collaboration: we can describe the
problem on the Web, solicit user inputs, then examine
the inputs to develop a solution. This system may not
be practical (and better systems may exist), but it can
arguably be considered a primitive MC system.
Consequently, we do not restrict the type of collaboration
nor the target problem. Rather, we view MC
as a general-purpose problem solving method. We say
that a system is an MC system if it enlists a mass of
humans to help solve a problem defined by the system
owners, and if in doing so, it addresses the following
four fundamental challenges: (1) How to recruit and
retain users? (2) What contributions can users make?
(3) How to combine user contributions to solve the target
problem? and (4) How to evaluate users and their
contributions?
Not all human-centric systems address these challenges.
Consider a system that manages car traffic in Madison,
Wisconsin. Its goal is to coordinate the behaviors of a
mass of human drivers (that already exist within the
system), to minimize traffic jams, say. Clearly, this
system does not want to recruit more human drivers
(in fact, it wants far fewer of them). We call such systems
mass management (MM) systems. MM techniques
(a.k.a., “crowd coordination” [31]) can be relevant to
MC contexts. But the two system classes are clearly
distinct.
In this survey we focus on MC systems that leverage
the Web to solve the above four challenges (or a significant
subset of them). The Web is unique in that it

can help recruit a large number of users, enable a high
degree of automation, and provide a large set of social
software (e.g., email, wiki, discussion group, blogging,
and tagging) that MC systems can use to manage their
users. As such, compared to the physical world, the
Web can dramatically improve existing MC systems and
give birth to novel system types.
1.2 Classifying MC systems
MC systems can be classified along many dimensions.
In what follows we discuss nine dimensions that we consider
most important. The two that immediately come
to mind are the nature of collaboration and type of target
problem. As discussed in Section 1.1, collaboration can
be explicit or implicit, and the target problem can be
any problem defined by the system owners (e.g., building
temporary or permanent artifacts, executing tasks).
The next four dimensions refer respectively to how an
MC system solves the four fundamental challenges described
in Section 1.1: how to recruit and retain users,
what can users do, how to combine their inputs, and
how to evaluate them? In Section 3 we will discuss
these challenges and the corresponding dimensions in
detail. In the rest of this section we discuss instead the
remaining three dimensions: degree of manual effort,
role of human users, and stand-alone versus piggy-back
architectures.
Degree of manual effort: When building an MC system,
we must decide how much manual effort is required to
solve each of the four MC challenges. This can range
from relatively little (e.g., combining ratings) to substantial
(e.g., combining code), and clearly also depends
on how much the system is automated. Then we must
decide how to divide the manual effort between the users
and the system owners. Some systems ask the users to
do relatively little and the owners a lot. For example,
to detect malicious users, the users may simply click
a button to report suspicious behaviors, whereas the
owners must carefully examine all relevant evidence to
determine if a user is indeed malicious. Some systems
do the reverse. For example, most of the manual burden
of merging Wikipedia edits fall on the users (who
are currently editing), not the owners.
Role of human users: We consider four basic roles of
humans in an MC system. (a) Slaves: humans help
solve the problem in a divide-and-conquer fashion, to
minimize the resources (e.g., time, effort) of the owners.
Examples are ESP and finding a missing boat in
satellite images using Mechanical Turk. (b) Perspective
providers: humans contribute different perspectives, which
when combined often produce a better solution (than
with a single human). Examples are reviewing books
and aggregating user bets to make predictions [31]. (c)
Content providers: humans contribute self-generated
content (e.g., videos on YouTube, images on Flickr).
(d) Component providers: humans function as components
in the target artifact, such as a social network, or
simply just a community of users (so that the owner can
sell ads, say). Humans often play multiple roles within
a single MC system (e.g., slaves, perspective providers,
and content providers in Wikipedia). It is important
to know these roles because that may determine how
to recruit. For example, to use humans as perspective
providers, it is important to recruit a diverse mass where
each human can make independent decisions, to avoid
“group think” [31].
Stand-alone versus piggy-back: When building an MC
system, we may decide to“piggy back”on a well-established
system, by exploiting “traces” that users leave in that
system to solve our target problem. For example, Google’s
“Did you mean” and Yahoo’s Search Assist utilize the
search log and user clicks of a search engine to correct
spelling mistakes. Another system may exploit
user purchases in an online bookstore (e.g., Amazon) to
recommend books. Unlike “stand-alone” systems, such
“piggy-back”systems do not have to solve the challenges
of recruiting users and deciding what they can do. But
they still have to decide on how to evaluate users and
their inputs (i.e., traces in this case), and to combine
such inputs to solve the target problem.
2. SAMPLE MC SYSTEMS ON THE WEB
Building on the above discussion of MC dimensions,
we now discuss MC systems on the Web. We first describe
a set of basic system types, then show how deployed
MC systems often combine multiple such types.
2.1 Basic System Types
Table 1 shows a set of basic MC system types. The set
is not meant to be exhaustive; it shows only those types
that have received most attention. From left to right,
it is organized by collaboration, architecture, the need
to recruit users, and then by the actions users can take.
We now discuss the set, starting with explicit systems.
Explicit Systems: These stand-alone systems let
users collaborate explicitly. In particular, users can
evaluate, share, network, build artifacts, and execute
tasks. We discuss these systems in turn.
1. Evaluating: These systems let users evaluate
“items” (e.g., books, movies, Web pages, other users)
using textual comments, numeric scores, or tags (e.g.,
[12]).
2. Sharing: These systems let users share “items”
such as products, services, textual knowledge, and structured
knowledge. Systems that share products and services
include Napster, YouTube, CPAN, and the site
programmableweb.com (for sharing files, videos, software,
and mashups, respectively). Systems that share
textual knowledge include mailing lists, Twitter, howto
repositories (e.g., ehow.com, which lets users contribute
and search how-to articles), Q&A Web sites
(e.g., Yahoo! Answers [5]), online customer support
systems (e.g., QUIQ [24], which powered Ask Jeeves’
AnswerPoint, a Yahoo! Answers-like site; QUIQ was
probably the first to use the term “mass collaboration”
in discussing online communities [23]). Systems that
share structured knowledge (e.g., relational, XML, RDF
data) include Swivel, Many Eyes, Google Fusion Tables,

Figure 1: A sample of basic MC system types on the World-Wide Web
Google Base, many e-science Web sites (e.g., bmrb.wisc.edu,
galaxyzoo.org), and many peer-to-peer systems developed
in the Semantic Web, database, AI, and IR communities
(e.g., Orchestra [10], [29]). Swivel for example
bills itself as the “YouTube of structured data”, which
lets users share, query, and visualize census- and voting
data, among others. In general, sharing systems can
be central (e.g., YouTube, ehow, Google Fusion Tables,
Swivel) or distributed, in a peer-to-peer fashion (e.g.,
Napster, Orchestra).
3. Networking: These systems let users collaboratively
construct a large social network graph, by adding
nodes and edges over time (i.e., homepages, friendships).
Then they exploit the graph to provide services (e.g.,
friend updates, ads, etc). To a lesser degree, blogging
systems are also networking systems in that bloggers
often link to other bloggers.
A key distinguishing aspect of systems that evaluate,
share, or network is that they do not merge user inputs,
or do so automatically in relatively simple fashions. For
example, evaluation systems typically do not merge textual
user reviews. They often merge user inputs such
as movie ratings, but do so automatically using some
formulas. Similarly, networking systems automatically
“merge”user inputs by adding them as nodes and edges
to a social network graph. As a result, users of such
systems do not need (and in fact often are not allowed)
to edit other users’ input.
4. Building Artifacts: In contrast, systems that let
users build artifacts such as Wikipedia often merge user
inputs “tightly”, and require users to edit and merge
one another’s inputs. A well-known artifact is software
(e.g., Apache, Linux, Hadoop). Another popular artifact
is textual knowledge bases (KBs). To build such
KBs (e.g., Wikipedia), users contribute data such as
sentences, paragraphs, Web pages, then edit and merge
one another’s contributions. The knowledge capture (kcap.org)
and AI communities have studied building such
KBs for over a decade. A well-known early attempt is
openmind [30], which enlists volunteers to build a KB
of commonsense facts (e.g.,“the sky is blue”). Recently,
the success of Wikipedia has inspired many“community
wikipedias”, such as Intellipedia (for the US intelligence
community) and EcoliHub (at ecolicommunity.org, to
capture all information about the E. Coli bacterium).
Yet another popular target artifact is structured KBs.

For example, the set of all Wikipedia infoboxes (i.e.,
attribute-value pairs such as city-name = Madison, state
= WI) can be viewed as a structured KB collaboratively
created by Wikipedia users. Indeed, this KB has recently
been extracted as DBpedia and used in several
applications (see dbpedia.org). Freebase.com builds an
open structured database, where users can create and
populate schemas to describe topics of interest, then
build collections of interlinked topics using a flexible
graph model of data. As yet another example, Google
Fusion Tables (tables.googlelabs.com) lets users upload
tabular data and collaborate on it by merging tables
from different sources, commenting on data items, and
sharing visualizations on the Web.
Several recent academic projects have also studied
building structured KBs in an MC fashion. The IWP
project [36] extracts structured data from the textual
pages of Wikipedia, then asks users to verify the extraction
accuracy. The Cimple/DBLife project [1, 7]
lets users correct the extracted structured data, exposed
in wiki pages, then add even more textual and
structured data. Thus, it builds structured “community
wikipedias”, whose wiki pages mix textual data
with structured data (that comes from an underlying
structured KB). Other related works include YAGO-
NAGA [11], BioPortal [19] and many recent projects in
the Web, Semantic Web, and AI communities [3, 18, 4].
In general, building a structured KB often requires selecting
a set of data sources, extracting structured data
from them, then integrating the data (e.g., matching
and merging “David Smith” and “D. M. Smith”). Users
can help these steps in two ways. First, they can improve
the automatic algorithms of the steps (if any), by
editing their code, creating more training data [17], answering
their questions [14, 15], or providing feedback
on their output [36, 14]. Second, users can manually
participate in the steps. For example, they can manually
add or remove data sources, extract or integrate
structured data, or add even more structured data, data
not available in the current sources but judged relevant
[7]. In addition, an MC system may perform inferences
over its KB to infer more structured data. To help this
step, users can contribute inference rules and domain
knowledge [27]. During all such activities, users can
naturally cross-edit and merge one another’s contributions,
just like in those systems that build textual KBs.
Another interesting target problem is building and
improving systems running on the Web. The project
Wikia Search (search.wikia.com) lets users build an opensource
search engine, by contributing code, suggesting
URLs to crawl, and editing search result pages (e.g.,
promoting or demoting URLs). Wikia Search was recently
disbanded, but similar features (e.g., editing search
pages) appear in other search engines (e.g., Google, mahalo.com).
Freebase lets users create custom browsing
and search systems (deployed at Freebase), using
the community-curated data and a suite of development
tools (such as the Metaweb query language and a
hosted development environment). Eurekster.com lets
users collaboratively build vertical search engines called
swickis, by customizing a generic search engine (e.g.,
specifying all URLs that the system should crawl). Finally,
MOBS, an academic project [15, 14], studies how
to collaboratively build data integration systems, those
that provide a uniform query interface to a set of data
sources. MOBS enlists users to create a crucial system
component, namely the semantic mappings (e.g., “location”
= “address”) between the data sources.
In general, users can help build and improve a system
running on the Web in several ways. First, they
can edit the system’s code. Second, the system typically
contains a set of internal components (e.g., URLs
to crawl, semantic mappings), and users can help improve
these, without even touching the system’s code
(e.g., adding new URLs, correcting mappings). Third,
users can edit system inputs and outputs. In the case of
a search engine, for instance, users can suggest that if
someone queries for “home equity loan for seniors”, the
system should also suggest querying for “reverse mortgage”.
Users can also edit search result pages (e.g., promoting
and demoting URLs, as mentioned earlier). Finally,
users can monitor the running system and provide
feedback.
We note that besides software, KBs, and systems,
many other target artifacts have also been considered.
Examples include community newspapers built by asking
users to contribute and evaluate articles (e.g., Digg)
and massive multi-player games that build virtual artifacts
(e.g., Second Life, a 3D virtual world partly built
and maintained by users).
5. Executing Tasks: The last type of explicit systems
that we consider is those that execute tasks. Examples
include finding extra-terrestials, mining for gold,
searching for missing people [2, 31, 32, 25], and cooperative
debugging (cs.wisc.edu/cbi, early work of this
project received the ACM Doctoral Dissertation Award
in 2005). As a recent well-known example, in the 2008
election the Obama team ran a large online MC operation
that asked numerous volunteers to help mobilize
voters. To apply MC to a task, we must find task parts
that can be “crowd-sourced”, such that each user can
make a contribution and the contributions in turn can
be combined to solve the parts. Finding such parts
and combining user contributions are often task specific.
“Crowd-sourcing”the parts however can be fairly
general, and systems have been developed to assist that
process. For example, Amazon’s Mechanical Turk can
help distribute pieces of a task to a mass of users (and
several recent interesting toolkits have even been developed
for using Mechanical Turk [13]). It was used
recently to search for Jim Gray, a database researcher
lost at sea, by asking volunteers to examine pieces of
satellite images for any sign of Jim Gray’s boat [20].
Implicit Systems: As discussed earlier, such systems
let users collaborate implicitly to solve a problem of the
system owners. They fall into two groups: stand-alone
and piggy-back.
A stand-alone system provides a service such that
when using it users implicitly collaborate (as a side
effect) to solve a problem. Many such systems exist,
and Table 1 lists a few representative examples. The

ESP game [33] lets users play a game of guessing common
words that describe images (shown independently
to each user), then uses those words to label images.
Google Image Labeler builds on this game, and many
other“games with a purpose”exist [34]. Prediction markets
[25, 31] let users bet on events (e.g., elections, sport
events), then aggregate the bets to make predictions.
The intuition is that the “collective wisdom” is often
accurate (under certain conditions, see [31]), and that
this helps incorporate “inside” information that users
have. The Internet Movie Database (IMDB) lets users
import movies into private accounts (hosted by IMDB).
It designed the accounts such that users are strongly
motivated to rate the imported movies, as doing so
bring many private benefits (e.g., they can query to find
all imported action movies rated at least 7/10, or the
system can recommend action movies highly rated by
people with similar taste). IMDB then aggregates all
private ratings to obtain a public rating for each movie,
for the benefit of the public. reCAPTCHA asks users to
solve captchas to prove they are humans (to gain access
to a site), then leverages the results for digitizing written
text [35]. Finally, it can be argued that the target
problem of many systems (that provide user services)
is simply to grow a large community of users, for various
reasons (e.g., personal satisfaction, charging subscription
fees, selling ads, selling the systems to other
companies). Buy/sell/auction websites (e.g., eBay) and
massive multi-player games (e.g., World of Warcraft) for
instance fit this description. Here, by simply joining the
system, users can be viewed as implicitly collaborating
to solve the target problem (of growing user communities).
The second kind of implicit system that we consider
is piggy-back systems. Such a system exploits the user
traces of yet another system (thus making the users
of this latter system implicitly collaborate) to solve a
problem. For example, over time many piggy-back MC
systems have been built on top of major search engines
(e.g., Google, Yahoo!, Microsoft). These systems exploit
the traces of search engine users (e.g., search logs,
user clicks) for a wide range of tasks (e.g., spelling correction,
finding synonyms, flu epidemic prediction, keyword
generation for ads [8]). Other examples include
exploiting user purchases to recommend products [28],
and exploiting click logs to improve the presentation of
a Web site [21].
2.2 MC Systems on the Web
We now build on basic system types to discuss deployed
MC systems on the Web. Founded on static
HTML pages, the Web soon offered many interactive
services. Some services serve machines (e.g., DNS servers,
Google Map API server), but most serve humans. Many
such services do not need to recruit users (in the sense
that the more the better). Examples include pay-parkingticket
services (for city residents) and room-reservation
services. (We call these mass management systems in
Section 1.1.) Many services however face MC challenges,
including the need to grow large user bases. For
example, online stores such as Amazon want a growing
user base for their services, to maximize profits,
and startups such as epinions.com grow their user bases
for advertising. They started out as primitive MC systems,
but quickly improved over time with additional
MC features (e.g., reviewing, rating, networking). Then
around 2003, aided by the proliferation of social software
(e.g., discussion groups, wiki, blog), many “fullfledged”MC
systems (e.g., Wikipedia, Flickr, YouTube,
Facebook, MySpace) appeared, marking the arrival of
“Web 2.0”. This Web is growing rapidly, with many
new MC systems being developed and non-MC systems
adding MC features.
These MC systems often combine multiple basic MC
features. For example, Wikipedia primarily builds a
textual KB. But it also builds a structured KB (via infoboxes)
and hosts many knowledge sharing forums (i.e.,
discussion groups). YouTube lets users both share and
evaluate videos. Community portals often combine all
MC features discussed so far. Finally, we note that the
Semantic Web, an ambitious attempt to add structure
to the Web, can be viewed as an MC attempt to share
structured data, and to integrate such data to build a
Web-scale structured KB. The World-Wide Web itself
is perhaps the largest MC system of all, encompassing
everything that we have discussed.
3. CHALLENGES AND SOLUTIONS
We now discuss the key challenges of MC systems:
how to recruit users, what contributions they can make,
how to combine the contributions, and how to evaluate
users and contributions.
1. How to Recruit and Retain Users? Recruiting
users is one of the most important MC challenges, for
which five major solutions exist. First, we can require
users to make contributions if we have the authority
to do so (e.g., a manager may require 100 employees
to help build a company-wide system). Second, we can
pay users. Mechanical Turk for example provides a way
to pay users on the Web to help with a task. Third, we
can ask for volunteers. This solution is free and easy
to execute, and hence is most popular. Most current
MC systems on the Web (e.g., Wikipedia, YouTube)
use this solution. The downside of volunteering is that
it is hard to predict how many users we can recruit for
a particular application.
The fourth solution is to make users pay for service.
The basic idea is to require the users of a system A to
“pay” for using A, by contributing to an MC system
B. Consider for example a blog website (i.e., system
A), where a user U can leave a comment only after
solving a puzzle (called a captcha) to prove that U is a
human. As a part of the puzzle, we can ask U to retype
a word that an OCR program has failed to recognize
(i.e., the “payment”), thereby contributing to an MC
effort on digitizing written text (i.e., system B). This
is the key idea behind the reCAPTCHA project [35].
The MOBS project [14, 15] employs the same solution.
In particular, it ran experiments where a user U can
access a Web site (e.g., a class homepage) only after
answering a relatively simple question (e.g., is string

“1960” in “born in 1960” a birth date?). MOBS then
leverages the answers to help build a data integration
system. This solution works best when the “payment”
is unintrusive or cognitively simple, to avoid deterring
users from using system A.
The fifth solution is to piggy back on the user traces
of a well-established system (e.g., building a spelling
correction system by exploiting user traces of a search
engine, see Section 1.2). This gives us a steady stream
of users. But we must solve the difficult challenge of
determining how the traces can be exploited for our
purpose.
Once we have selected a recruitment strategy, we should
consider how to further encourage and retain users. Many
encouragement and retention (E&R) schemes exist. We
briefly discuss the most popular ones. First, we can
provide instant gratification, by immediately showing
a user how his or her contribution makes a difference
[16]. Second, we can provide an enjoyable experience or
a necessary service, such as game playing (while making
a contribution) [33]. Third, we can provide ways to establish,
measure, and show fame/trust/reputation (e.g.,
[9, 15, 27, 26]). Fourth, we can set up competitions,
such as showing top rated users. Finally, we can provide
ownership situations, where a user may feel he or
she“owns”a part of the system, and thus is compelled to
“cultivate” that part. For example, zillow.com displays
houses and estimates their market prices. It provides a
way for a house owner to “claim” his or her house and
provide the correct data (e.g., number of beds), which
in turn helps improve the price estimation.
The above E&R schemes apply naturally to volunteering,
but can also work well for other recruitment
solutions. For example, after requiring a set of users to
contribute, we can still provide instant gratification, enjoyable
experience, fame management, etc. to maximize
user participation. Finally, we note that deployed MC
systems often employ a mixture of recruitment methods
(e.g., bootstrapping with“requirement”or“paying”,
then switching to “volunteering”once the system is sufficiently
“mature”).
2. What Contributions Can Users Make? In
many MC systems the kinds of contributions users can
make are somewhat limited. For example, to evaluate,
users review, rate, or tag; to share, users add items to a
central Web site; to network, users link to other users;
to find a missing boat in satellite images, users examine
those images.
In more complex MC systems, however, users often
can make a far wider range of contributions, from simple“low-hanging
fruit”to cognitively complex ones. For
example, when building a structured KB, users can add
a URL, flag incorrect data, and supply attribute-value
pairs (as low-hanging fruit) [6, 7]. But they can also
supply inference rules, resolve controversial issues, and
merge conflicting inputs (as cognitively complex contributions)
[27]. The challenge then is to define this range
of possible contributions (and design the system such
that it can gather a critical mass of such contributions).
Toward this goal, we should consider four important
factors. First, how cognitively demanding are the contributions?
An MC system often has a way to classify
users into groups, such as guests, regulars, editors, admins,
and “dictators”. We should take care to design
cognitively appropriate contribution types for different
user groups. Low-ranking users (e.g., guests, regulars)
often want to make only “easy” contributions (e.g., answering
a simple question, editing 1-2 sentences, flagging
an incorrect data piece). If the cognitive load
is high, they may be reluctant to participate. Highranking
users (e.g., editors, admins) are more willing to
make “hard” contributions (e.g., resolving controversial
issues).
Second, what should be the impact of a contribution?
We can measure the potential impact by considering
how the contribution potentially affects the MC system.
For example, editing a sentence in a Wikipedia
page largely affects only that page, whereas revising an
edit policy may potentially affect million of pages. As
another example, when building a structured KB, flagging
an incorrect data piece typically has less potential
impact than supplying an inference rule, which may be
used in many “parts” of the MC system. Quantifying
the potential impact of a contribution type in a complex
MC system may be difficult [14, 15]. But it is important
to do so, because we typically have far fewer highranking
users such as editors and admins (than regulars,
say). To maximize the total contribution of these
few users, we should ask them to make potentially-highimpact
contributions whenever possible.
Third, what about machine contributions? If an MC
system employs an algorithm for a task, then we want
human users to make contributions that are easy for
humans, but difficult for machines. For example, examining
textual and image descriptions to decide if two
products match is relatively easy for humans but very
difficult for machines. In short, the MC work should be
distributed between human users and machines according
to what each of them is best at, in a complementary
and synergistic fashion.
Finally, the user interface should make it easy for
users to contribute. This is highly non-trivial. For example,
how can users easily enter domain knowledge
such as “no current living person was born before 1850”
(which can be used in a KB to detect incorrect birth
dates, say)? A natural language format (e.g., in openmind.org)
is easy for users, but hard for machines to
understand and use, and a formal language format has
the reverse problem. As another example, when building
a structured KB, contributing attribute-value pairs
is relatively easy (as Wikipedia infoboxes and Freebase
demonstrate). But contributing more complex structured
data pieces can be quite difficult for naive users,
as this often requires them to learn the KB schema,
among others [7].
3. How to Combine User Contributions? Many
MC systems do not combine contributions, or do so in
a “loose” fashion. For example, current evaluation systems
do not combine reviews, and combine numeric ratings
using relatively simple formulas. Networking sys-

tems simply link contributions (homepages and friendships)
to form a social network graph. More complex
MC systems, however, such as those that build software,
KBs, systems, and games, combine contributions
more“tightly”. Exactly how this happens is application
dependent. Wikipedia for example lets users manually
merge edits, while ESP does so automatically, by waiting
until two users agree on a common word.
No matter how contributions are combined, a key
problem is to decide what to do if users differ, such
as when three users assert “A” and two users “not A”.
Both automatic and manual solutions have been developed
for this problem. Current automatic solutions
typically combine contributions weighted by some user
scores. The work [14, 15] for example lets users vote
on the correctness of system components (the semantic
mappings of a data integration systems in this case
[22]), then combines the votes weighted by the trustworthiness
of each user. The work [27] lets users contribute
structured KB fragments, then combines them into a
coherent probabilistic KB, by computing the probabilities
that each user is correct, then weighting contributed
fragments by these probabilities.
Manual dispute management solutions typically let
users fight and settle among themselves. Unresolved issues
then percolate up the user hierarchy. Systems such
as Wikipedia and Linux employ such methods. Automatic
solutions are more efficient. But they work only
for relatively simple forms of contributions (e.g., voting),
or forms that are complex but amenable to algorithmic
manipulation (e.g., structured KB fragments).
Manual solutions are still the currently preferred way
to combine “messy” conflicting contributions.
To further complicate the matter, sometimes not just
human users, but machines also make contributions.
Combining such contributions is difficult. To see why,
suppose we employ a machine M to help create Wikipedia
infoboxes (as proposed in [36]). Suppose on Day 1 M
asserts population = 5500 in a city infobox. On Day 2,
a user U may correct this into population = 7500, based
on his or her knowledge. On Day 3, however, M may
have managed to process more Web data, and obtained
higher confidence that population = 5500 is indeed correct.
Should M override U’s assertion? And if so, how
can M explain its reasoning to U? The main problem
here is that it is difficult for a machine to enter into a
manual dispute with a human user. The currently preferred
method is for M to alert U, and then leave it up
to U to decide what to do. But this method clearly will
not scale with the number of conflicting contributions.
4. How to Evaluate Users and Contributions?
MC systems often must manage malicious users. To do
so, we can use a combination of techniques on blocking,
detection, and deterrence. First, we can block many
malicious users by limiting who can make what kinds of
contributions. Many e-science MC systems for example
allow anyone to submit data, but only certain domain
scientists to clean and merge this data into the central
database.
Second, we can detect malicious users and contributions
using a variety of techniques. Manual techniques
include monitoring the system by the owners, distributing
the monitoring workload among a set of trusted
users, and enlisting ordinary users (e.g., flagging bad
contributions on message boards). Automatic methods
typically involve some tests. For example, a system can
ask users questions for which it already knows the answers,
then use the answers of the users to compute
their reliability scores [15, 35]. Many other schemes to
compute users’ reliability/trust/fame/reputation have
been proposed (e.g., [9, 26]).
Finally, we can deter malicious users with threats of
“punishment”. A common punishment is banning. A
newer, more controversial form of punishment is“public
shaming”, where a user U judged malicious is publicly
branded as a malicious or “crazy” user for the rest of
the community (possibly without U’s knowledge). For
example, a chat room may allow users to rate other
users. If the (hidden) score of a user U goes below
a threshold, other users will only see a mechanically
garbled version of U’s comments, whereas U continues
to see his or her comments exactly as written.
No matter how well we manage malicious users, malicious
contributions often still seep into the system. If
so, the MC system must find a way to undo those. If the
system does not combine contributions (e.g., reviews) or
does so only in a“loose”fashion (e.g., ratings), undo-ing
is relatively easy. If the system combines contributions
“tightly”, but keeps them “localized”, then we can still
undo with relatively simple logging. For example, user
edits in Wikipedia can be combined extensively within a
single page, but kept“localized”to that page (not propagated
to other pages). Consequently, we can undo with
page-level logging, as Wikipedia does. If the contributions
however are “pushed deep” into the system, then
undo-ing can be very difficult. For example, suppose
an inference rule R is contributed to a KB on Day 1.
We then use R to infer many facts, apply other rules to
these facts and other facts in the KB to infer more facts,
let users edit the facts extensively, and so on. Then on
Day 3, should R be found incorrect, it would be very
difficult to remove R without reverting the KB to its
state on Day 1, thereby losing all good contributions
made between Day 1 and Day 3.
At the other end of the user spectrum, many MC systems
also identify and leverage influential users, using
both manual and automatic techniques. For example,
productive users in Wikipedia can be manually identified
(e.g., recommended by a user), promoted, and given
more responsibilities. As another example, certain users
of social networks are influential in that they influence
buy/sell decisions of other users. Consequently, some
work has examined how to automatically identify these
users, then leverage them in viral marketing within a
user community [26].
4. CONCLUDING REMARKS
We have discussed MC systems on the World-Wide
Web. Our discussion shows that mass collaboration can
be applied to a wide variety of problems, and that it
raises numerous interesting technical and social chal-

lenges. Given the success of current MC systems, we
expect that this emerging field will grow rapidly. In the
near future, we foresee three major directions: more
generic platforms, more applications and structure, and
more users and complex contributions.
First, the various systems built in the past decade
have clearly demonstrated the value of mass collaboration.
The race is now on to move beyond building individual
systems, toward building general MC platforms
that can be used to develop such systems quickly.
Second, we expect that mass collaboration will be
applied to ever more classes of applications. Many of
these applications will be formal and structured in some
sense, making it easier to employ automatic techniques
and to coordinate them with human users. In particular,
a large chunk of the Web is about data and services.
Consequently, we expect that mass collaboration
to build structured databases and structured services
(e.g., Web services with formalized input and output)
will receive increasing attention.
Finally, we expect that many techniques will be developed
to engage an ever broader range of users in
mass collaboration, and to enable them, especially naive
users, to make increasingly complex contributions, such
as creating software programs and building mashups
(without writing any code), and specifying complex structured
data pieces (without knowing any structured query
languages).
5. REFERENCES
[1] The Cimple/DBLife project.
http://pages.cs.wisc.edu/~anhai/projects/cimple.
[2] Person of the year: You, 2006. Special issue,
http://www.time.com/time/magazine/article/
0,9171,1569514,00.html.
[3] Wikipedia and artificial intelligence: An evolving
synergy, 2008. AAAI-08 Workshop.
[4] Workshop on collaborative construction, management
and linking of structured knowledge (CK 2009), 2009.
http://users.ecs.soton.ac.uk/gc3/iswc-workshop.
[5] L. A. Adamic, J. Zhang, E. Bakshy, and M. S.
Ackerman. Knowledge sharing and yahoo answers:
Everyone knows something. In WWW, 2008.
[6] X. Chai, B. Vuong, A. Doan, and J. F. Naughton.
Efficiently incorporating user feedback into
information extraction and integration programs. In
SIGMOD, 2009.
[7] P. DeRose, X. Chai, B. J. Gao, W. Shen, A. Doan,
P. Bohannon, and X. Zhu. Building community
wikipedias: A machine-human partnership approach.
In ICDE, 2008.
[8] A. Fuxman, P. Tsaparas, K. Achan, and R. Agrawal.
Using the wisdom of the crowds for keyword
generation. In WWW, 2008.
[9] J. Golbeck. Computing and applying trust in
web-based social network, 2005. Ph.D. Dissertation,
University of Maryland.
[10] Z. G. Ives, N. Khandelwal, A. Kapur, and M. Cakir.
Orchestra: Rapid, collaborative sharing of dynamic
data. In CIDR, 2005.
[11] G. Kasneci, M. Ramanath, F. Suchanek, and
G. Weikum. The yago-naga approach to knowledge
discovery. SIGMOD Record, 37(4):41–47, 2008.
[12] G. Koutrika, B. Bercovitz, F. Kaliszan, H. Liou, and
H. Garcia-Molina. Courserank: A closed-community
social system through the magnifying glass. In The
3rd Int’l AAAI Conference on Weblogs and Social
Media (ICWSM), 2009.
[13] G. Little, L. B. Chilton, R. C. Miller, and
M. Goldman. Turkit: Tools for iterative tasks on
mechanical turk, 2009. Technical Report. Available
from glittle.org.
[14] R. McCann, A. Doan, V. Varadarajan, and
A. Kramnik. Building data integration systems: A
mass collaboration approach. In WebDB, 2003.
[15] R. McCann, W. Shen, and A. Doan. Matching
schemas in online communities: A web 2.0 approach.
In ICDE, 2008.
[16] L. McDowell, O. Etzioni, S. D. Gribble, A. Y. Halevy,
H. M. Levy, W. Pentney, D. Verma, and S. Vlasseva.
Mangrove: Enticing ordinary people onto the semantic
web via instant gratification. In ISWC, 2003.
[17] R. Mihalcea and T. Chklovski. Building sense tagged
corpora with volunteer contributions over the web. In
RANLP, 2003.
[18] N. F. Noy, A. Chugh, and H. Alani. The CKC
challenge: Exploring tools for collaborative knowledge
construction. IEEE Intelligent Systems, 23(1):64–68,
2008.
[19] N. F. Noy, N. Griffith, and M. A. Munsen. Collecting
community-based mappings in an ontology repository.
In ISWC, 2008.
[20] M. Olson. The amateur search. SIGMOD Record,
37(2):21–24, 2008.
[21] M. Perkowitz and O. Etzioni. Adaptive web sites.
CACM, 43(8), 2000.
[22] E. Rahm and P. A. Bernstein. A survey of approaches
to automatic schema matching. VLDB J.,
10(4):334–350, 2001.
[23] R. Ramakrishnan. Collaboration and data mining,
2001. Keynote talk, KDD.
[24] R. Ramakrishnan, A. Baptist, V. Ercegovac,
M. Hanselman, N. Kabra, A. Marathe, and U. Shaft.
Mass collaboration: A case study. In IDEAS, 2004.
[25] H. Rheingold. Smart Mobs. Perseus Publishing, 2003.
[26] M. Richardson and P. Domingos. Mining
knowledge-sharing sites for viral marketing. In KDD,
2002.
[27] M. Richardson and P. Domingos. Building large
knowledge bases by mass collaboration. In K-CAP,
2003.
[28] B. M. Sarwar, G. Karypis, J. A. Konstan, and
J. Riedl. Item-based collaborative filtering
recommendation algorithms. In WWW, 2001.
[29] R. Steinmetz and K. Wehrle, editors. Peer-to-Peer
Systems and Applications, volume 3485 of Lecture
Notes in Computer Science. Springer, 2005.
[30] D. G. Stork. Using open data collection for intelligent
software. IEEE Computer, 33(10):104–106, 2000.
[31] J. Surowiecki. The Wisdom of Crowds. Anchor Books,
2005.
[32] D. Tapscott and A. D. Williams. Wikinomics.
Portfolio, 2006.
[33] L. von Ahn and L. Dabbish. Labeling images with a
computer game. In Proc. of CHI, 2004.
[34] L. von Ahn and L. Dabbish. Designing games with a
purpose. Communications of the ACM, 51(8):58–67,
2008.
[35] L. von Ahn, B. Maurer, C. McMillen, D. Abraham,
and M. Blum. recaptcha: Human-based character
recognition via web security measures. Science,
321(5895):1465–1468, 2008.
[36] D. S. Weld, F. Wu, E. Adar, S. Amershi, J. Fogarty,
R. Hoffmann, K. Patel, and M. Skinner. Intelligence in
wikipedia. In AAAI, 2008.

Page 178 of 206

Attachment C
The Potential of Mass Collaboration
to Produce Social Innovation
Page 179 of 206

The potential of mass
collaboration to
produce social innovation
Ola Tjornbo
The Waterloo Institute of
Social Innovation and Resilience, Canada
Social Frontiers
The next edge of social innovation research

Social Frontiers
The potential of collective intelligence to produce
social innovation
2
The potential of collective intelligence
to produce social innovation
By Ola Tjornbo, Post-Doctoral Researcher, Waterloo Institute of Social Innovation and Resilience
Abstract
Can Collective Intelligence be used to produce social innovation? The advance of information and
communication technologies in the 21 st century seems to have unlocked the potential of collective
intelligence, enabling us to mobilize large crowds to solve problems and produce novelty. However,
despite early optimism, more recent scholarship suggests that collective intelligence has serious
limits and in particular that it is not suitable for dealing with the types of complex problems that
social innovators inevitably face. In order to evaluate more carefully the potential of collective intelligence
to support social innovation I present a framework for looking at social innovation processes
as a number of distinct phases and mechanisms. I then look at three examples of different
types of online platforms used to mobilize collective intelligence. My analysis suggests that each
of these has some capacity to support some elements of a social innovation process, but that as
the theoretical literature would suggest none of them are useful throughout the process. However,
since each of these different platforms has different strengths and weaknesses, by linking them together
and utilizing the right platform at the right time, we may be able to harness collective intelligence
to greatly enhance social innovation capacity.
Introduction
The development of modern information and communication technologies has led to a renewed interest
in the phenomenon of collective intelligence. Collective intelligence refers to the capacity to
mobilize and coordinate the knowledge, skills and creativity possessed by large groups of individuals,
and combine them into a greater whole. In the light of this development, there have been many
optimistic predictions about the potential of crowds to solve social problems (e.g. Rushkoff 2003;
Howe 2006; Tapscott 2006; 2010). But are these tools valuable to the production of social innovation?
It is becoming increasingly clear that collective intelligence has serious limitations when it
comes to dealing with complex problems that are politically contested (Sunstein 2008) and require
careful coordination (Nielsen 2012, Kittur, Lee, & Kraut, 2009). Its usefulness is limited when
dealing with politicized or complex problems however, suggesting it may not be suitable for social
innovation.
On the other hand, social innovation is deeply reliant on the capacity to combine the ideas, knowledge
and resources possessed by disparate groups in order to create an impact; something collective
intelligence can obviously do well. . Moreover, in practice, there are several sites online that
are already using collective intelligence to promote innovation and perhaps also social innovation.
In this article, drawing on the work of Brian Arthur (2009) and a number of social innovation
scholars (Mulgan et al. 2007; Westley et al. 2007; Mumford 2002), I provide a framework for examining
how collective intelligence can support social innovation. I divide social innovation into
phases and mechanisms. I then explore how three existing collective intelligence platforms have
promoted social innovation. These three cases illustrate the different models that exist for tapping
collective intelligence online, with each one having different strengths and weaknesses in terms
of social innovation. Based on my analysis, I suggest that using collective intelligence to produce
social innovation is possible, but no single collective intelligence platform is likely to be useful
throughout a whole social innovation process.

Social Frontiers
The potential of collective intelligence to produce
social innovation
3
Problem statement: the challenge of using collective intelligence to drive social innovation
Social Innovation is defined by the Waterloo Institute for Social Innovation and Resilience as “an
initiative, product, process or program that profoundly changes the basic routines, resource and
authority flows or beliefs of any social system.” 1 Although this is just one definition it shares much
in common with those used by other authors working in this field (Mumford 2002; Wheatley &
Frieze 2006).What is particular about this perspective on social innovation is that it is systemic,
meaning that it is concerned with the impact an innovation has on a whole social system, not just
in the context of a particular organization or industry. This kind of systemic change inevitably involves
conflicts of interests, different perspectives on the system and the nature of the social problem,
and unanticipated consequences due to unpredictable relations of cause and effect. In short,
and using the language of systems perspective, social innovation is a ‘complex’ (Westley et al.
2007; Duit & Galaz 2008; Pierre & Peters 2005).
So, complexity is inevitable when dealing with social innovation. This is a problem for collective
intelligence (Nielsen 2011; Sunstein xxxxx; Sunstein). In order to mobilize collective intelligence
the participants in the project must be able to share and communicate information to each other in
such a way that the specialized knowledge that each individual possesses can be combined into a
coherent whole or ‘answer’. There are two characteristics that a problem can have that make this
far easier.
Collective intelligence is easier to apply when the amount of coordination between participants
required to solve a problem is minimal (Kittur 2008; Kittur et al. 2009). In some applications of
collective intelligence each individual only needs to supply their best answer to a problem with the
collective answer begin determined by the average of all the responses. This is called a low coordination
problem. However, in many projects the new contributions only make sense in relation
to what has gone before. A famous example of such a (very) high coordination project was the
publishing house Penguin’s attempt to write a book using an online collaboration platform, which
largely failed (Kittur et al. 2009; Pulinger 2007).
1. Collective intelligence is easier to apply to a problem that has a definite answer;
one that is clearly recognizable when it is found and where the method for finding
it is known and agreed on by the group (Nielsen 2011, Tjornbo 2013). This is
also called an ‘intellective’ as opposed to ‘jugmentive’ task (see Laughlin and Adamopolous
1982). Typically the former condition holds in fields like mathematics,
but not when dealing with social problems. Collective intelligence becomes increasingly
difficult to employ as you try to incorporate knowledge from different academic
disciplines, or, traditional as well as scientific knowledge. It is almost impossible
when the knowledge that one party professes to possess is dismissed or disputed
by other parties such as is common in highly politicized or value laden debates.
Social innovation meets neither of these conditions. It is complex, coordination requirements are
high and judgmentive evaluations are required. As such, it is tempting to say that social innovation
is simply not a good arena to use collective intelligence. However, if you look more deeply at how
social innovation happens the picture becomes less certain.
The process of social innovation
Social innovation is still an emerging field of study and thus there are still relatively few papers
dealing with how social innovation happens from a systemic perspective (Mumford & Moertl
2003). However, there are other disciplines that look at innovation in complex systems and share
very similar conceptual frameworks for understanding this phenomenon, especially research into
socio-technical systems. Here I will be describing social innovation as it has been explained by social
innovation scholars (Wheatley & Frieze 2006; Westley et al. 2007; Westley & Antadze 2010;
Mumford 2002) as well as certain authors working within socio-technical systems (e.g Geels &
Schot, 2007; Geels, 2005; Smith, Stirling, & Berkhout, 2005) and especially in the work of Brian
Arthur (2009).
1 http://sig.uwaterloo.ca/about-the-waterloo-institute-for-social-innovation-and-resilience-wisir#About%20SI

Social Frontiers
The potential of collective intelligence to produce
social innovation
4
The Stages of Social Innovation
Scholars of innovation in complex systems tend to break the process into phases, usually three (M.
Mumford, 2002; Arthur 2009) but sometimes four (Westley et al. 2013). Table 1 below presents a
typical three phase division. At each phase I identify crucial mechanisms for making the innovation
successful. These mechanisms are described in greater detail in the paragraphs below.
Table 1: Phases and mechanisms of social innovation
Phase of social
innovation
Invention
Development
Implementation
Associated mechanisms
Recombination; exchange of information and ideas between different
domains
Matching problems and solutions; clustering; niches; shadow
networks
Cross scale networks; boundary organizations; institutional
entrepreneurship
Invention
The Invention stage is when a new innovation is first born. The reason innovation is unusual, is that
it is difficult for human beings to either conceive of or accept radical reconfigurations of existing
systems (see e.g. Arthur 2009; Giddens 1984; Kuhn 2012; Schumpeter 1976). In fact, it is so difficult
that many have questioned how innovation is even possible. The most common answer seems
to be that innovations are born out of the recombination of existing ideas, practices, technologies
and other elements, to produce new and surprising outcomes. Mumford agrees and notes that social
innovation seems to emerge most often when modes of reasoning that are common in one domain
are applied to surprising effect in another domain (Mumford & Moertl 2003).
So the invention phase can be encouraged by fostering the exchange of ideas and information between
individuals working in different domains. In fact, Arthur argues that the more technologies
that exists, the more potential recombinations there are; and so the faster innovation happens.
Development
Innovative ideas, when they first emerge, do not typically have immediate and obvious applications.
The first stage of developing an innovation is often one of finding an application for it. Thus,
development can be facilitated by finding ways to match problems and solutions (this is similar to
the idea a garbage can model of decision making in Cohen et al. 1972).
The next stage of development is to adapt an initial idea to its purpose. Often this stage of development
involves linking the invention to many other ideas that help to refine it. As both Westley et al.
(2007) and Arthur (2009) have noted, successful innovations often consist of clusters of products,
programs and processes that come together to allow the invention to fulfill its purpose. I refer to
this as ‘clustering’.
Developing an innovation nevertheless requires an investment of time and usually, both human and
financial capital. Finding resources for fledgling ideas is difficult. Innovation scholars have noted
the importance of ‘niches’ in protecting innovations during this growth period (Schot & Geels
2007; Smith 2006; Kemp et al. 1998). Such niches may be housed within larger organizations and
institutions, as spaces reserved for radical innovation, or they can be small markets where the innovation
has a limited application that does not reflect its systems changing potential.

Social Frontiers
The potential of collective intelligence to produce
social innovation
5
Related to the concept of a niche is the concept of a ‘shadow network’ (Olsson et al. 2006). Shadow
networks are groups of individuals who work together to develop an innovation, often without
compensation, in order to create an alternative to the existing way of doing things. Sometimes
shadow networks can exist for a long time, developing and utilizing an idea before it ever enters
the mainstream.
Institutionalization and regime shift
As Westley notes (Westley et al. 2007 and Westley & Antadze, 2010),in order to establish themselves,
innovation often need to access resources and opportunities that are located outside the
system they are operating in. While resistance to change within a system may be high, there may
be opportunities at other scales to build support for the innovation. The exploitation of cross scale
effects is greatly facilitated by the creation of networks that span scalar boundaries (Moore &
Westley 2011) and by the work of boundary organizations that actively look to bridge the divides
between different actors (Crona & Parker 2012; Crona & Parker 2011).
An innovation may have to wait before it has an opportunity to establish itself but agents can also
work actively to look for opportunities to open up at broader scales. Throughout the innovation
process, but particularly at the institutionalization phase, the success of the innovation is heavily
dependent on the support and skills of agents, often called institutional entrepreneurs (Dorado
2005; Levy & Scully 2007; Child et al. 2007), who are able to secure resources to grow the innovation
and are adept at finding opportunities to establish its place in the system (Westley et al. 2013,
Mumford 2002).
Promoting social innovation, a collective process
A social innovation needs to move through all of these three stages (although not necessarily consecutively
since they can occur simultaneously or even out of order on occasion) and all of the
mechanisms described above are important to its progress. However, no single individual, organization
or institutions has to carry out all of these activities. In a recent article, Westley et al. (2013)
argue that agency in social innovation processes is best understood as a distributed quality, where
many different agents are involved in making a social innovation happen, contributing different
skills at different times. The same may be true of collective innovation platforms. Each may provide
some support to social innovation without being useful throughout a whole process, so that
collective intelligence still has a role to play in promoting social innovation.
Case studies
While many different applications of collective intelligence exist, there are just a few models that
promote innovation. In a recent study I identified three main types of collective intelligence platform
(Tjornbo 2013) In the case studies that follow, I explore one of the leading examples of each
of the types of collective intelligence platforms with a view to answering two questions
1. To what extent are these innovation platforms already producing social innovations?
2. How well are the three different types of online innovation platforms adapted to the task of
stimulating social innovation and to what extent can we see the mechanisms of social innovation
in action?
Following this exploration, I will offer some conclusions about the potential of each of these platforms
to enhance our capacity for social innovation.

Social Frontiers
The potential of collective intelligence to produce
social innovation
6
Challenge Grants
Challenge grants are perhaps the most established model for regularly accessing the innovative
capacity of virtual social networks. A challenge grant allows those facing a problem to put out an
open call for potential solutions. Anyone who thinks they have a solution to the challenge can submit
a proposal and they typically compete with other ‘solvers’ to win the cash prize for the best
solution, either determined by the ‘challenger’ or by an independent jury.
Challenge grants require some coordination since the ‘solvers’ have to meet the expectations of the
‘challengers’. This clearly becomes more difficult depending on the nature of the challenge issued.
As the following example illustrates though, while the challenge grant approach is most easily
applicable to simpler, technical challenges, it does still have some application for complex social
challenges.
Innocentive
Operational since 2001, Innocentive is undoubtedly a leader among open innovation platforms.
It has had over 1500 challenges posted on the site worth a sum of over 40,000,000 dollars and
can boast of some notable success stories. For example, it has produced breakthroughs in oil spill
cleanup and in treating Amyotrophic Lateral Sclerosis (ALS). 2 Like most challenge grants, the principle
aim of Innocentive is to connect people with a problem with those who think they might have
an answer.
Innocentive as a social innovation platform
The majority of challenges posted on Innocentive are undoubtedly purely technical in nature, however,
some of the challenges concern social problems and could potentially produce a social innovation.
Following the definition of a social innovation above, I identified those Innocentive challenges
that a) concerned a social problem, b) took a holistic/systemic view of problem and c) invited
solutions with a potentially disruptive impact on the way that problem was tackled, that is to
say did not constrain the problem solvers to work within an existing mode of practice. Clearly this
involved a somewhat subjective judgment and so I asked a colleague to perform the same evaluation.
Based on these criteria I identified four, of the 138 currently active Innocentive challenges, as
supportive of social innovation.
Of course, the 138 currently active problems only present a snapshot of the activities of Innocentive,
which has processed over 1650 challenges to date. However, looking at the most successful
problem solvers involved in Innocentive over the course of the last five years also gives an indication
of the primary activities of the site. Between 2007 and 2011 not one ‘top solver’ was involved
in challenges that could be described as socially innovative. 3
While Innocentive indulges in some social innovation, the data does not tell us how successful Innocentive
is in this arena and unfortunately the answer to this question is not readily available. Innocentive’s
general measure of success is that 85% of challenges find winning solutions, but there
is no such figure that focuses soley on social innovations. Yet, two of Innocentive’s high profile
success stories involve social innovation. The first, very clearly an instance of social Innovation,
was a challenge to find innovative new ways of providing education to populations in poor and developing
countries 4 and the second, where the problem is less obvious but still present, was a challenge
to find a means of measuring ‘human-potential’ 5 . Thus, although social innovation is just a
small part of Innocentive’s activities, it is possible to use the Innocentive model to stimulate social
innovation.
2 http://www.innocentive.com/about-innocentive/innovation-solutions-of-note and see also Nielsen 2011
3 http://www.innocentive.com/for-solvers/top-solvers-2011
4 http://www.innocentive.com/for-solvers/winning-solutions/21st-century-cyber-schools-challenge
5 http://www.innocentive.com/for-solvers/winning-solutions/human-potential-index-challenge

Social Frontiers
The potential of collective intelligence to produce
social innovation
7
Innocentive and the mechanisms of social innovation
Innocentive’s success seems to hinge on its ability to leverage two of the core mechanisms of social
innovation, matching problems and solutions and exchanging information across domains.
Clearly, matching problems to solutions is what Innocentive and other challenge grants do best of
all. Their success depends on being able to make people who possess the answer aware of the problem’s
existence. An 85% success rate seems to suggest that Innocentive is very good at doing this.
The challenge grant structure is also particularly good at innovation because it opens problems up
to a wide audience of potential solvers. A typical way for an organization or individual to attempt
to find a solution to a problem might be to hire a consultant or other experts in the particular field
they’re operating in, but these people are often too committed to existing ways of operating or the
established best practices, to generate truly innovative ideas. As the literature on social innovation
suggests, innovation is usually the product of the novel combination of adjacent fields of knowledge
(Arthur). This certainly holds true for Innocentive, where many winning solutions have come
from experts in different fields than the challenger (Nielsen 2011).
However, as good as they may be at the invention and early development stage of social innovation,
challenge grants may not be doing enough at the later stages of the process. This is in line
with previous research that has indicated that while challenge grants are good at stimulating new
invention, they are poor at supporting innovations through to implementation (Tjornbo & Westley
2012). Once a solution has been matched to a problem, there is not much more support available
from Innocentive in terms of developing the idea. The section of the site entitled ‘Solver Resources’
mostly contains a few brief articles on the basics of how to answer challenges. The most
important tools they offer for developing ideas further seem to be focused on community building.
For example, there are built in supports for people hoping to partner with others in designing their
solution and an online forum where members of Innocentive can chat about a broad range of topics.
But these tools seem to have limited impact. The global forum, for example, sees a new topic
opened at mostonce or twice a month and most of these receive two or fewer replies. Currently, the
first three posts in this forum are all observations about how difficult it is to form a team 6 . Based
on a sample of twenty randomly selected challenges the average number of public comments in the
public project rooms is less than 3. If the impression created by the forums is correct then Innocentive
is missing out on opportunities to build shadow networks.
In addition, Innocentive does not have built-in tools to help innovations establish themselves in
broader systems. Once a solution is accepted by a challenger then the role of the site, and possibly
of the innovator, may be over. There is no systematic attempt to encourage the involvement of institutional
entrepreneurs, to develop such skills, or to look for cross scale opportunities. All of this
is left up to the challenger or innovator.. Perhaps it is no coincidence that the two successful social
innovations profiled on the site were achieved in partnership with The Economist magazine, which
may have helped to raise the profile of the competitions.
Innovation Communities
Innovation communities do not promote innovation generally; rather they focus on just one problem
and attempt to find solutions to it. The emphasis in these groups is not on generating ideas, but
in fine-tuning them and actually seeing them successfully implemented in the real world. Unlike
the other innovation platforms, therefore, they rely heavily on their ability to coordinate action.
This can be accomplished in a number of different ways. For example, Wikipedia has developed
an elaborate set of rules and guidelines for evaluating articles and has a dedicated group of volunteer
moderators who do most of the work of editing and fine tuning articles (Butler et al. 2008). In
order to succeed, they need to keep volunteers motivated and prevent fragmentation of the project
(Hertel et al. 2003; Mustonen 2003).
6 https://www.innocentive.com/ar/board/solver

Social Frontiers
The potential of collective intelligence to produce
social innovation
8
Open Source Ecology
Open Source Ecology (OSE) was spawned by the frustration of one man; farmer, technologist
and physicist Marcin Jakubowski. When he was unable to repair his brand name tractor that broke
down frequently, hedesigned a cheap, robust and easily repairable alternative that could be built entirely
using locally available materials. He then made his blue print (?) available to the public. His
work attracted outside attention and supporters and soon expanded into the vision of the Global
Village Construction Set (GVCS), a set of blueprints for 50 machines, those essential for a moden,
civilized society, that could be built and maintained locally on a small scale Jakubowski’s farm became
the site of a community dedicated to producing blueprints and prototypes of these machines,
and their work attracted the interest of others, like TED, who gave Jakubowski a platform to share
his idea. Jakubowski’s TED talk describing the Open Source Ecology has over a million views and
saw the community really launched on the global stage.
OSE as social innovation platform
There is no doubt that the OSE project is a social innovation. It is a radical reconceptualization of
manufacturing that turns its back on the centralization and global supply chains of the the mainstream
economy and a direct response to concerns about the social and environmental impacts of
globalization and the consumer economy. In and of itself, therefore, OSE demonstrates that the innovation
community approach is applicable to social innovation, and not just, as is typical, to the
collaborative production of already existing products such as encyclopedias, operating systems or
web server software.
OSE and the mechanisms of social innovation
Clearly, web platforms like OSE make use of collective intelligence after the initial conception of
the idea. The spark for the OSE was generated by one man only, Marcin Jakubowski. Further, a
prerequisite of becoming involved in the OSE projects is that participants are already attracted by
the idea of the GVCS and share at least some of Jakubowski’s values (why else would they invest
time in the project after all). This reduces a lot of the complexity inherent in using collective intelligence
for social innovation and perhaps is what allows OSE to work as a social innovation platform.
The real strength of OSE lies in developing the idea past initial invention. The farm became a niche
that attracted resources, both financial and in the shape of talented volunteers, who came to work at
the farm. These resources soon saw the production of a cluster of innovations (different prototypes
of GVCS machines).
However, OSE is a new type of niche sustained entirely by its supporters (Thomson & Jakubowski
2012). OSE became the focus of one of the early crowdfunding campaigns (online platforms that
allow members of the public to support projects with small donations), with 500 supporters of
OSE creating a small monthly revenue for Jakubowski (Thomson & Jakubowski 2012). One of
the volunteers at the farm won a Thiel “20 Under 20” Fellowship of 100,000 dollars to allow him
to continue his work on the farm. Its success, therefore, depended entirely on its ability to build a
committed shadow network of supporters.
The lesson from other open source projects is that these initiatives must attract both casual volunteers,
and a core group of very committed enthusiasts (Howe 2006). In the case of Wikipedia,
while casual volunteers create the bulk of new material, it is a small group of ‘moderators’ who ensure
that articles abide by Wikipedia’s standards and maintain a consistent style (Kittur et al. 2007).

Social Frontiers
The potential of collective intelligence to produce
social innovation
9
In the case of OSE, the project received a big boost after Jakubowski was invited to make a presentation
at a TED conference. This brought a significant amount of interest to the project, and an
infusion of extra investment and resources (Thomson & Jakubowski 2012). The central premise of
the OSE project caught on and lead to an expansion of the idea into new locations, a process social
innovation scholars sometimes refer to as ‘scaling out’ (Westley & Antadze 2010). A shadow network
has grown up around the OSE project, through the OSE forums and wiki. Most significantly,
this now includes a German OSE node with its own OSE Wiki and active forums 7 .
However, the core OSE community has not proved sustainable. The OSE forums have not been
particularly active 8 . Even more significantly, the OSE farm is no longer active, with the last of the
volunteers having departed in February 2013. The reasons for this collapse appear to be partly related
to the leadership of Marcin Jakubowski 9 . The problems associated with a charismatic leader
who is at first instrumental to the growth of a new initiative but later comes to limit it are well
known and documented in the management literature (Westley et al. 2007) and may well be at play
here. From other other open source projects we learn that a meritocratic and non-hierarchical leadership
style is essential to maintaining such communities.
Despite this lack of recent activity, the OSE project is certainly not a failure. The central idea has
been considerably developed since Jakubowski first invented it and a network has grown up around
it so that work is now being continued in other locations. However, there may in fact be a tension
between maintaining the kind of intense community needed to sustain a project like the OSE
and the activities associated with institutionalizing an innovation like identifying opportunities for
cross scale interactions.
Open Innovation Platforms
Open innovation platforms are platforms that publicize people’s good ideas. At their simplest, they
are open message boards where anyone is free to submit their proposals for public scrutiny. More
typically however, they also encourage visitors to comment on ideas and to vote for those they like,
thus giving the ‘best’ ideas greatest prominence.
Open innovation platforms do not draw much use from collective intelligence directly, since most
ideas are the product of a single mind or a small team rather than a large group. However, in allowing
for comments on ideas they create opportunities for collaboration. More importantly, by
spreading ideas effectively, they may be opening people up to a greater diversity of notions, hopefully
invigorating recombination processes.
TED
TED is without a doubt the most successful of open innovation platforms. It started in 1984 as an
organization that put on conferences bringing together speakers from the worlds of technology,
entertainment and design. Today, it is mostly famous for the videos of its talks available online
through its website. It currently hosts over 150,000 talks and some of the most popular have over
ten million views. TED differs from standard Open Innovation Platforms in that not anyone is allowed
to give a TED talk. Also, it has an unsually sophisticated multimedia distribution platform.
TED as social innovation platform
TED is undoubtedly a social innovation platform. Several of the talks on the site promote ideas
that are intended to tackle social problems, take a holistic, systemic approach, and have potentially
radical implications. For example, there is Ken Robinson’s 10 proposal to reform the education systems
in the west to put more emphasis on creativity; or Geroge Papandreou’s proposal for a Europe
without political borders 11 . This is not to say that TED is exclusively or even mainly a social inno-
7 http://wiki.opensourceecology.de/index.php?title=Main_Page/en&setlang=en
8 http://forum.opensourceecology.org/discussion/1004/why-is-ose-so-quiet-lately
9 http://opensourceecology.org/wiki/Yoonseo_Blog
10 http://www.ted.com/talks/ken_robinson_says_schools_kill_creativity.html
11 http://www.ted.com/talks/george_papandreou_imagine_a_european_democracy_without_borders.html

Social Frontiers
The potential of collective intelligence to produce
social innovation
10
vation platform though. The most common talk topics on TED are those related to its core areas –
Technology (513 talks), Entertainment (263), and Design (308) with the only exception being Science
(which garners 394). Topics like politics (130), health (103) and poverty (44) lag far behind.
TED and mechanisms of social innovation
The greatest strength of TED is its ability to communicate ideas. The most popular TED talks garner
well over ten million views while talks with hundreds of thousands of viewers are fairly commonplace.
At the most fundamental level, simply exposing people to a variety of ideas makes them
more likely to come up with innovative recombinations (Arthur 2009). Moreover, exposure brings
with it additional resources, as we have already seen in the OSE example.
Although originally TED’s design was not directed at harnessing collective intelligence to spur social
innovation, over time, TED has evolved and added tools to develop ideas beyond the talks that
are presented. One such tool is the forum, which allows for commentary on the talks. Of the three
case studies here, TED has easily the most active forum, with the number of comments on a talk
often numbering in the tens and hundreds (as opposed to OSE and Innocentive, which often only
had a few comments). rather which contrasts sharply with OSE and Innocentive. There is scope
through these discussions to develop ideas further and to create clusters, however, so far this activity
is not typically systematic, nor carried out with a particular end goal in mind.
Another new development is the TED prize. The prize is essentially a form of challenge grant
where one individual is awarded 1 million dollars for a plan that proposes a solution to a problem
that will ‘change the world’ for the better. To date there have been nineteen Ted prize winners,
tackling topics such as nutrition in schools and marine protected areas 12 . Yet another innovation
promoting development is the Ted Fellows program, which is focused on supporting the work of
young innovators 13 .
Largely, the impetus for these kinds of developments has come from the TED community (personal
communication). This online network currently has 149,441 members and its own forum.
Moreover, TED receives feedback from the participants at its physical conferences. Much of this
feedback concerns a desire to see the ideas at TED put into action with the support of the talented
people in the room and the resources they have access too. A striking example of this potential
came in the form of the Mission Blue project. This began with a TED talk from Sylvia Earle, who
argued for the creation of a series of marine protected areas to help build the resilience of ocean
ecosystems around the globe. The speech garnered a huge amount of a support, including a 1 million
pledge from philanthropist Addison Fischer. It also led to a voyage, with passengers made up
of scientists, philanthropists and celebrities, which raised over 15 million dollars 14 .
TED clearly has a potentially powerful ability to build cross scale networks able to advocate
strongly for social innovation. Another example of this came in the form of the TED Challenge
, (part of TED 2013), where small interdisciplinary groups worked together, with notable successes,
to create action on a range of issues ranging from vaccination to sex trafficking (personal
communication).
Thus far though, the kinds of deliberate activities described here are the exception rather than the
rule. At its core, TED remains an idea promoter, not an advocacy organization. Most of the attendees
at TED conferences are scientists and business people rather than politicians and TED remains
committed to a politically neutral perspective (personal communication). In fact, perhaps there is
a tension between TED’s role as a promoter of ideas and as a place of community building and its
potential role as an agency of institutional entrepreneurship and advocacy.
12 http://www.ted.com/pages/prize_about
13 http://www.ted.com/fellows
14 http://blog.ted.com/2010/04/13/ocean_hope_at_m/

Social Frontiers
The potential of collective intelligence to produce
social innovation
11
Conclusions
This study provides a framework for better understanding the role that collective intelligence specifically,
and other kinds of social media platforms more generally, can play in promoting social
innovation. Despite the complex nature of social innovation processes and the pessimistic predictions
of the theoretical literature, it is clear from the case studies presented here that collective intelligence
can play a role in promoting social innovation, both directly and indirectly. All three of
the web platforms I looked at do promote social innovation to some extent. Innocentive, the challenge
grant, featured a small sample of social innovation challenges and at least two examples of
successfully launched social innovations. OSE, the innovation community, took a radical alternative
model of production and self-sustainability and not only considerably developed the idea with
several prototypes, but also created a global shadow network dedicated to taking it further.. Finally,
TED, the open innovation platform, has publicized several social innovations and helped them to
gain greater prominence and resources, moreover it has spawned an online community dedicated
to seeing some if these innovative ideas realized in practice, and has occasionally helped to build
cross-scale networks to make this happen.
At the same time, no one platform seems to be able to support a social innovation from invention
through to implementation. In fact, each of these different types of platforms seems particularly
strong in one particular phase; invention in the case of TED, development in the case of OSE and
Innocentive (though this latter also plays a role in invention and seems more to straddle these two
phases). Moreover, none of these platforms utilized all of the mechanisms associated with any one
phase. Table 2 below shows the mechanisms each of the platforms utilized most effectively. It is
striking that none of these platforms was particularly active in the implementation phase, although
TED seems to have the greatest potential in this area.
It seems likely, given the degree of specialization observed here, that it is very difficult for any one
platform to utilize all of the mechanisms effectively. As one might expect, based on network theory,
there are tradeoffs involved in choosing to support either the formation of a strongly bonded
community or shadow network or the formation of more loosely coupled cross scale communities.
Equally though, there were opportunities to draw on mechanisms that the platforms themselves
were not doing enough to exploit, such as Innocentive’s failure to promote greater use of its forums,
or TED’s hesitation around mobilizing its potential as a network organization.
Ultimately, this study suggests that those interested in promoting social innovation should make
greater use of the full range of collective innovation platforms in order to best use the strengths of
each. However, more work is needed to further investigate the patterns suggested by this study.

Social Frontiers
The potential of collective intelligence to produce
social innovation
12
Bibliography
Arthur, B. 2009. The Nature of Technology: What it is and
How it Evolves. Free Press.
Butler, B., Joyce, E. & Pike, J., 2008. Don’t look now, but
we’ve created a bureaucracy: the nature and roles of policies
and rules in wikipedia. In Proceeding of the twenty-sixth
annual SIGCHI conference on Human factors in computing
systems. ACM, pp. 1101–1110. Available at: http://dl.acm.
org/citation.cfm?id=1357227 [Accessed March 1, 2012].
Child, J., Lu, Y. & Tsai, T., 2007. Institutional Entrepreneurship
in Building an Environmental Protection System
for the People’s Republic of China. Organization Studies,
28(7), pp.1013–1034. Available at: http://oss.sagepub.com/
cgi/doi/10.1177/0170840607078112 [Accessed February
19, 2011].
Cohen, M., March, J. & Olsen, J., 1972. A Garbage Can
Model of Organizational Choice. Administrative Science
Quarterly, 17(1), pp.1–25.
Crona, B. & Parker, J., 2012. Learning in support of governance:
Theories, methods, and a framework to assess how
bridging organizations contribute to adaptive resource governance.
Ecology and Society, 17(1). Available at: http://dlc.
dlib.indiana.edu/dlc/handle/10535/8172 [Accessed September
30, 2013].
Crona, B.I. & Parker, J.N., 2011. Network Determinants
of Knowledge Utilization: Preliminary Lessons From a
Boundary Organization. Science Communication, 33(4),
pp.448–471. Available at: http://scx.sagepub.com/cgi/
doi/10.1177/1075547011408116 [Accessed September 30,
2013].
Dorado, S., 2005. Institutional Entrepreneurship, Partaking,
and Convening. Organization Studies, 26(3),
pp.385–414. Available at: http://oss.sagepub.com/cgi/
doi/10.1177/0170840605050873 [Accessed October 8,
2010].
Duit, A. & Galaz, V., 2008. Governance and ComplexityEmerging
Issues for Governance Theory. Governance, 21(3),
pp.311–335. Available at: http://doi.wiley.com/10.1111/
j.1468-0491.2008.00402.x.
Geels, F., 2005. Processes and patterns in transitions and
system innovations: Refining the co-evolutionary multi-level
perspective. Technological Forecasting and Social Change,
72(6), pp.681–696. Available at: http://linkinghub.elsevier.
com/retrieve/pii/S0040162505000569 [Accessed September
29, 2010].
Geels, F. & Schot, J., 2007. Typology of sociotechnical
transition pathways. Research Policy, 36(3), pp.399–417.
Available at: http://linkinghub.elsevier.com/retrieve/pii/
S0048733307000248.
Giddens, A., 1984. The constitution of society: outline
of the theory of structuration, University of California
Press. Available at: http://books.google.com/
books?id=x2bf4g9Z6ZwC&pgis=1 [Accessed April 20,
2011].
Hertel, G., Niedner, S. & Herrmann, S., 2003. Motivation of
software developers in Open Source projects: an Internetbased
survey of contributors to the Linux kernel. Research
Policy, 32(7), pp.1159–1177. Available at: http://linkinghub.
elsevier.com/retrieve/pii/S0048733303000477 [Accessed
July 18, 2012].
Kemp, R., Schot, J. & Hoogma, R., 1998. Regime shifts to
sustainability through processes of niche formation: the approach
of strategic niche management. Technology Analysis
& Strategic Management, 10(2), pp.175–198. Available at:
http://www.informaworld.com/index/779917259.pdf [Accessed
June 2, 2011].
Kittur, A., 2008. Harnessing the wisdom of crowds in wikipedia:
quality through coordination. In Proceedings of the
2008 ACM conference on. pp. 37–46. Available at: http://
dl.acm.org/citation.cfm?id=1460572 [Accessed July 18,
2012].
Kittur, A. et al., 2007. Power of the few vs. wisdom of the
crowd: Wikipedia and the rise of the bourgeoisie. , 1(2),
p.19. Available at: http://edouard-lopez.com/fac/ICPS - S7/
Complexit?/2008-Wikipedia-As-A-Complex-System/Power
of the Few vs. Wisdom of the Crowd: Wikipedia and the Rise
of the Bourgeoisie.pdf [Accessed November 25, 2011].
Kittur, A., Lee, B. & Kraut, R.E., 2009. Coordination in
collective intelligence: the role of team structure and task
interdependence. In Proceedings of the 27th international
conference on human factors in computing systems.
pp. 1495–1504. Available at: http://dl.acm.org/citation.
cfm?id=1518928 [Accessed July 18, 2012].
Kuhn, T.S., 2012. The Structure of Scientific Revolutions,
University of Chicago Press.
Levy, D. & Scully, M., 2007. The Institutional Entrepreneur
as Modern Prince: The Strategic Face of Power in Contested
Fields. Organization Studies, 28(7), pp.971–991. Available at:
http://oss.sagepub.com/cgi/doi/10.1177/0170840607078109
[Accessed August 12, 2010].
Moore, M.L. & Westley, F., 2011. Surmountable chasms:
networks and social innovation for resilient systems. Ecology
and society, 16(1), p.5. Available at: http://www.
plexusinstitute.org/resource/collection/5FD4ACEF-
7B50-4388-A93E-109B0988049F/Moore-Westley-SurmountableChasms-2011.pdf
[Accessed August 27, 2013].
Mulgan, G. et al., 2007. Social Innovation: What it is, why it
matters and how it can br accelerated. Scientist.
Mumford, M., 2002. Social innovation: ten cases from Benjamin
Franklin. Creativity research journal, (August 2013),
pp.37–41. Available at: http://www.tandfonline.com/doi/
abs/10.1207/S15326934CRJ1402_11 [Accessed August 27,
2013].
Mumford, M.D. & Moertl, P., 2003. Cases of Social Innovation:
Lessons From Two Innovations in the 20th Century.
Creativity Research Journal, 15(2-3), pp.261–266. Available
at: http://www.tandfonline.com/doi/abs/10.1080/10400419.2
003.9651418 [Accessed August 8, 2013].
Mustonen, M., 2003. Copyleft—the economics of Linux
and other open source software. Information Economics and
Policy, 15(1), pp.99–121. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0167624502000902
[Accessed July
18, 2012].
Nielsen, M., 2011. Reinventing Discovery: The New Era of
Networked Science, Princeton University Press. Available
at: http://www.amazon.com/Reinventing-Discovery-The-
Networked-Science/dp/0691148902 [Accessed February 27,
2013].
Howe, J., 2006. The Rise of Crowdsourcing. Wired Magazine,
June(14), pp.1–5. Available at: http://www.wired.com/
wired/archive/14.06/crowds_pr.html.

Social Frontiers
The potential of collective intelligence to produce
social innovation
13
Olsson, P. et al., 2006. Shooting the rapids: navigating transitions
to adaptive governance of social-ecological systems.
Ecology and Society, 11(1), p.18. Available at: http://www.
ecologyandsociety.org/vol11/iss1/art18/ES-2005-1595.pdf
[Accessed August 9, 2011].
Pierre, J. & Peters, B.G., 2005. Governing Complex Societies:
Trajectories and Scenarios, Palgrave Macmillan.
Available at: http://www.amazon.com/Governing-Complex-
Societies-Trajectories-Scenarios/dp/1403946604 [Accessed
August 1, 2011].
Pulinger, K., 2007. Living with A Million Penguins: inside
the wiki-novel | Books | theguardian.com. The Guardian.
Available at: http://www.theguardian.com/books/booksblog/2007/mar/12/livingwithamillionpenguins
[Accessed
September 30, 2013].
Schot, J. & Geels, F.W., 2007. Niches in evolutionary theories
of technical change. Journal of Evolutionary Economics,
17(5), pp.605–622. Available at: http://www.springerlink.com/index/10.1007/s00191-007-0057-5
[Accessed
January 24, 2011].
Schumpeter, J.A., 1976. Capitalism, socialism and democracy,
Harper Colophon.
Smith, a, Stirling, a & Berkhout, F., 2005. The governance
of sustainable socio-technical transitions. Research Policy,
34(10), pp.1491–1510. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0048733305001721.
Smith, A., 2006. Green niches in sustainable development:
the case of organic food in the United Kingdom. Environment
and Planning C: Government and Policy, 24(3),
pp.439–458. Available at: http://www.envplan.com/abstract.
cgi?id=c0514j [Accessed April 13, 2011].
Thomson, C. & Jakubowski, M., 2012. Toward an Open
Source Civilization. innovations, 7(3), pp.53–70. Available
at: http://www.mitpressjournals.org/doi/pdf/10.1162/
INOV_a_00139 [Accessed September 13, 2013].
Tjornbo, O. & Westley, F.R., 2012. Game Changers : The
Big Green Challenge and the Role of Challenge Grants in
Social Innovation. Journal of Social Entrepreneurship, 3(2),
pp.37–41.
Westley, F. & Antadze, N., 2010. Making a Difference Strategies
for Scaling Social Innovation for Greater Impact. The
Innovation Journal: The Public Sector Innovation Journal,
15(2), pp.1–19.
Westley, F., Patton, M.Q. & Zimmerman, B., 2007.
Getting to Maybe: How the World Is Changed, Vintage
Canada. Available at: http://books.google.com/
books?id=431sDyOiWC0C&pgis=1 [Accessed March 29,
2011].
Westley, F., Tjornbo, O.B.T.,
Wheatley, M. & Frieze, D., 2006. Using Emergence to Take
Social Innovation to Scale. The Berkana Institute, pp.1–7.
Available at: http://w.margaretwheatley.com/articles/usingemergence.pdf
[Accessed May 6, 2011].
Personal Communication
Member of TED New York Office http://www.ted.com/pages/staff,
16 September 2013

Page 180 of 206

Page 176 of 206

Attachment D
How Field Catalysts Galvanize
Social Change
Page 181 of 206

Page 182 of 206

Advocacy Foundation Publishers
Page 183 of 206

Advocacy Foundation Publishers
The e-Advocate Quarterly
Page 184 of 206

Issue Title Quarterly
Vol. I 2015 The Fundamentals
I
The ComeUnity ReEngineering
Project Initiative
Q-1 2015
II The Adolescent Law Group Q-2 2015
III
Landmark Cases in US
Juvenile Justice (PA)
Q-3 2015
IV The First Amendment Project Q-4 2015
Vol. II 2016 Strategic Development
V The Fourth Amendment Project Q-1 2016
VI
Landmark Cases in US
Juvenile Justice (NJ)
Q-2 2016
VII Youth Court Q-3 2016
VIII
The Economic Consequences of Legal
Decision-Making
Q-4 2016
Vol. III 2017 Sustainability
IX The Sixth Amendment Project Q-1 2017
X
The Theological Foundations of
US Law & Government
Q-2 2017
XI The Eighth Amendment Project Q-3 2017
XII
The EB-5 Investor
Immigration Project*
Q-4 2017
Vol. IV 2018 Collaboration
XIII Strategic Planning Q-1 2018
XIV
The Juvenile Justice
Legislative Reform Initiative
Q-2 2018
XV The Advocacy Foundation Coalition Q-3 2018
Page 185 of 206

XVI
for Drug-Free Communities
Landmark Cases in US
Juvenile Justice (GA)
Q-4 2018
Page 186 of 206

Issue Title Quarterly
Vol. V 2019 Organizational Development
XVII The Board of Directors Q-1 2019
XVIII The Inner Circle Q-2 2019
XIX Staff & Management Q-3 2019
XX Succession Planning Q-4 2019
XXI The Budget* Bonus #1
XXII Data-Driven Resource Allocation* Bonus #2
Vol. VI 2020 Missions
XXIII Critical Thinking Q-1 2020
XXIV
The Advocacy Foundation
Endowments Initiative Project
Q-2 2020
XXV International Labor Relations Q-3 2020
XXVI Immigration Q-4 2020
Vol. VII 2021 Community Engagement
XXVII
The 21 st Century Charter Schools
Initiative
Q-1 2021
XXVIII The All-Sports Ministry @ ... Q-2 2021
XXIX Lobbying for Nonprofits Q-3 2021
XXX
XXXI
Advocacy Foundation Missions -
Domestic
Advocacy Foundation Missions -
International
Q-4 2021
Bonus
Page 187 of 206

Vol. VIII
2022 ComeUnity ReEngineering
XXXII
The Creative & Fine Arts Ministry
@ The Foundation
Q-1 2022
XXXIII The Advisory Council & Committees Q-2 2022
XXXIV
The Theological Origins
of Contemporary Judicial Process
Q-3 2022
XXXV The Second Chance Ministry @ ... Q-4 2022
Vol. IX 2023 Legal Reformation
XXXVI The Fifth Amendment Project Q-1 2023
XXXVII The Judicial Re-Engineering Initiative Q-2 2023
XXXVIII
The Inner-Cities Strategic
Revitalization Initiative
Q-3 2023
XXXVIX Habeas Corpus Q-4 2023
Vol. X 2024 ComeUnity Development
XXXVX
The Inner-City Strategic
Revitalization Plan
Q-1 2024
XXXVXI The Mentoring Initiative Q-2 2024
XXXVXII The Violence Prevention Framework Q-3 2024
XXXVXIII The Fatherhood Initiative Q-4 2024
Vol. XI 2025 Public Interest
XXXVXIV Public Interest Law Q-1 2025
L (50) Spiritual Resource Development Q-2 2025
Page 188 of 206

LI
Nonprofit Confidentiality
In The Age of Big Data
Q-3 2025
LII Interpreting The Facts Q-4 2025
Vol. XII 2026 Poverty In America
LIII
American Poverty
In The New Millennium
Q-1 2026
LIV Outcome-Based Thinking Q-2 2026
LV Transformational Social Leadership Q-3 2026
LVI The Cycle of Poverty Q-4 2026
Vol. XIII 2027 Raising Awareness
LVII ReEngineering Juvenile Justice Q-1 2027
LVIII Corporations Q-2 2027
LVIX The Prison Industrial Complex Q-3 2027
LX Restoration of Rights Q-4 2027
Vol. XIV 2028 Culturally Relevant Programming
LXI Community Culture Q-1 2028
LXII Corporate Culture Q-2 2028
LXIII Strategic Cultural Planning Q-3 2028
LXIV
The Cross-Sector/ Coordinated
Service Approach to Delinquency
Prevention
Q-4 2028
Page 189 of 206

Vol. XV 2029 Inner-Cities Revitalization
LXIV
LXV
LXVI
Part I – Strategic Housing
Revitalization
(The Twenty Percent Profit Margin)
Part II – Jobs Training, Educational
Redevelopment
and Economic Empowerment
Part III - Financial Literacy
and Sustainability
Q-1 2029
Q-2 2029
Q-3 2029
LXVII Part IV – Solutions for Homelessness Q-4 2029
LXVIII
The Strategic Home Mortgage
Initiative
Bonus
Vol. XVI 2030 Sustainability
LXVIII Social Program Sustainability Q-1 2030
LXIX
The Advocacy Foundation
Endowments Initiative
Q-2 2030
LXX Capital Gains Q-3 2030
LXXI Sustainability Investments Q-4 2030
Vol. XVII 2031 The Justice Series
LXXII Distributive Justice Q-1 2031
LXXIII Retributive Justice Q-2 2031
LXXIV Procedural Justice Q-3 2031
LXXV (75) Restorative Justice Q-4 2031
LXXVI Unjust Legal Reasoning Bonus
Page 190 of 206

Vol. XVIII 2032 Public Policy
LXXVII Public Interest Law Q-1 2032
LXXVIII Reforming Public Policy Q-2 2032
LXXVIX ... Q-3 2032
LXXVX ... Q-4 2032
Page 191 of 206

The e-Advocate Monthly Review
2018
Transformational Problem Solving January 2018
The Advocacy Foundation February 2018
Opioid Initiative
Native-American Youth March 2018
In the Juvenile Justice System
Barriers to Reducing Confinement April 2018
Latino and Hispanic Youth May 2018
In the Juvenile Justice System
Social Entrepreneurship June 2018
African-American Youth July 2018
In the Juvenile Justice System
Gang Deconstruction August 2018
Social Impact Investing September 2018
Opportunity Youth: October 2018
Disenfranchised Young People
The Economic Impact of Social November 2018
of Social Programs Development
Gun Control December 2018
2019
The U.S. Stock Market January 2019
Prison-Based Gerrymandering February 2019
Literacy-Based Prison Construction March 2019
Children of Incarcerated Parents April 2019
African-American Youth in The May 2019
Juvenile Justice System
Page 192 of 206

Racial Profiling June 2019
Mass Collaboration July 2019
Page 193 of 206

The e-Advocate Quarterly
Special Editions
Crowdfunding Winter-Spring 2017
Social Media for Nonprofits October 2017
Mass Media for Nonprofits November 2017
The Opioid Crisis in America: January 2018
Issues in Pain Management
The Opioid Crisis in America: February 2018
The Drug Culture in the U.S.
The Opioid Crisis in America: March 2018
Drug Abuse Among Veterans
The Opioid Crisis in America: April 2018
Drug Abuse Among America’s
Teens
The Opioid Crisis in America: May 2018
Alcoholism
Page 194 of 206

The e-Advocate Journal
of Theological Jurisprudence
Vol. I - 2017
The Theological Origins of Contemporary Judicial Process
Scriptural Application to The Model Criminal Code
Scriptural Application for Tort Reform
Scriptural Application to Juvenile Justice Reformation
Vol. II - 2018
Scriptural Application for The Canons of Ethics
Scriptural Application to Contracts Reform
& The Uniform Commercial Code
Scriptural Application to The Law of Property
Scriptural Application to The Law of Evidence
Page 195 of 206

Legal Missions International
Page 196 of 206

Issue Title Quarterly
Vol. I 2015
I
II
God’s Will and The 21 st Century
Democratic Process
The Community
Engagement Strategy
Q-1 2015
Q-2 2015
III Foreign Policy Q-3 2015
IV
Public Interest Law
in The New Millennium
Q-4 2015
Vol. II 2016
V Ethiopia Q-1 2016
VI Zimbabwe Q-2 2016
VII Jamaica Q-3 2016
VIII Brazil Q-4 2016
Vol. III 2017
IX India Q-1 2017
X Suriname Q-2 2017
XI The Caribbean Q-3 2017
XII United States/ Estados Unidos Q-4 2017
Vol. IV 2018
XIII Cuba Q-1 2018
XIV Guinea Q-2 2018
XV Indonesia Q-3 2018
XVI Sri Lanka Q-4 2018
Page 197 of 206

Vol. V 2019
XVII Russia Q-1 2019
XVIII Australia Q-2 2019
XIV South Korea Q-3 2019
XV Puerto Rico Q-4 2019
Issue Title Quarterly
Vol. VI 2020
XVI Trinidad & Tobago Q-1 2020
XVII Egypt Q-2 2020
XVIII Sierra Leone Q-3 2020
XIX South Africa Q-4 2020
XX Israel Bonus
Vol. VII 2021
XXI Haiti Q-1 2021
XXII Peru Q-2 2021
XXIII Costa Rica Q-3 2021
XXIV China Q-4 2021
XXV Japan Bonus
Vol VIII 2022
XXVI Chile Q-1 2022
Page 198 of 206

The e-Advocate Juvenile Justice Report
______
Vol. I – Juvenile Delinquency in The US
Vol. II. – The Prison Industrial Complex
Vol. III – Restorative/ Transformative Justice
Vol. IV – The Sixth Amendment Right to The Effective Assistance of Counsel
Vol. V – The Theological Foundations of Juvenile Justice
Vol. VI – Collaborating to Eradicate Juvenile Delinquency
Page 199 of 206

The e-Advocate Newsletter
Genesis of The Problem
Family Structure
Societal Influences
Evidence-Based Programming
Strengthening Assets v. Eliminating Deficits
2012 - Juvenile Delinquency in The US
Introduction/Ideology/Key Values
Philosophy/Application & Practice
Expungement & Pardons
Pardons & Clemency
Examples/Best Practices
2013 - Restorative Justice in The US
2014 - The Prison Industrial Complex
25% of the World's Inmates Are In the US
The Economics of Prison Enterprise
The Federal Bureau of Prisons
The After-Effects of Incarceration/Individual/Societal
The Fourth Amendment Project
The Sixth Amendment Project
The Eighth Amendment Project
The Adolescent Law Group
2015 - US Constitutional Issues In The New Millennium
Page 200 of 206

2018 - The Theological Law Firm Academy
The Theological Foundations of US Law & Government
The Economic Consequences of Legal Decision-Making
The Juvenile Justice Legislative Reform Initiative
The EB-5 International Investors Initiative
2017 - Organizational Development
The Board of Directors
The Inner Circle
Staff & Management
Succession Planning
Bonus #1 The Budget
Bonus #2 Data-Driven Resource Allocation
2018 - Sustainability
The Data-Driven Resource Allocation Process
The Quality Assurance Initiative
The Advocacy Foundation Endowments Initiative
The Community Engagement Strategy
2019 - Collaboration
Critical Thinking for Transformative Justice
International Labor Relations
Immigration
God's Will & The 21st Century Democratic Process
The Community Engagement Strategy
The 21st Century Charter Schools Initiative
2020 - Community Engagement
Page 201 of 206

Extras
The Nonprofit Advisors Group Newsletters
The 501(c)(3) Acquisition Process
The Board of Directors
The Gladiator Mentality
Strategic Planning
Fundraising
501(c)(3) Reinstatements
The Collaborative US/ International Newsletters
How You Think Is Everything
The Reciprocal Nature of Business Relationships
Accelerate Your Professional Development
The Competitive Nature of Grant Writing
Assessing The Risks
Page 202 of 206

Page 203 of 206

About The Author
John C (Jack) Johnson III
Founder & CEO
Jack was educated at Temple University, in Philadelphia, Pennsylvania and Rutgers
Law School, in Camden, New Jersey. In 1999, he moved to Atlanta, Georgia to pursue
greater opportunities to provide Advocacy and Preventive Programmatic services for atrisk/
at-promise young persons, their families, and Justice Professionals embedded in the
Juvenile Justice process in order to help facilitate its transcendence into the 21 st Century.
There, along with a small group of community and faith-based professionals, “The Advocacy Foundation, Inc." was conceived
and developed over roughly a thirteen year period, originally chartered as a Juvenile Delinquency Prevention and Educational
Support Services organization consisting of Mentoring, Tutoring, Counseling, Character Development, Community Change
Management, Practitioner Re-Education & Training, and a host of related components.
The Foundation’s Overarching Mission is “To help Individuals, Organizations, & Communities Achieve Their Full Potential”, by
implementing a wide array of evidence-based proactive multi-disciplinary "Restorative & Transformative Justice" programs &
projects currently throughout the northeast, southeast, and western international-waters regions, providing prevention and support
services to at-risk/ at-promise youth, to young adults, to their families, and to Social Service, Justice and Mental
Health professionals” everywhere. The Foundation has since relocated its headquarters to Philadelphia, Pennsylvania, and been
expanded to include a three-tier mission.
In addition to his work with the Foundation, Jack also served as an Adjunct Professor of Law & Business at National-Louis
University of Atlanta (where he taught Political Science, Business & Legal Ethics, Labor & Employment Relations, and Critical
Thinking courses to undergraduate and graduate level students). Jack has also served as Board President for a host of wellestablished
and up & coming nonprofit organizations throughout the region, including “Visions Unlimited Community
Development Systems, Inc.”, a multi-million dollar, award-winning, Violence Prevention and Gang Intervention Social Service
organization in Atlanta, as well as Vice-Chair of the Georgia/ Metropolitan Atlanta Violence Prevention Partnership, a state-wide
300 organizational member, violence prevention group led by the Morehouse School of Medicine, Emory University and The
Original, Atlanta-Based, Martin Luther King Center.
Attorney Johnson’s prior accomplishments include a wide-array of Professional Legal practice areas, including Private Firm,
Corporate and Government postings, just about all of which yielded significant professional awards & accolades, the history and
chronology of which are available for review online. Throughout his career, Jack has served a wide variety of for-profit
corporations, law firms, and nonprofit organizations as Board Chairman, Secretary, Associate, and General Counsel since 1990.
www.TheAdvocacyFoundation.org
Clayton County Youth Services Partnership, Inc. – Chair; Georgia Violence Prevention Partnership, Inc – Vice Chair; Fayette
County NAACP - Legal Redress Committee Chairman; Clayton County Fatherhood Initiative Partnership – Principal
Investigator; Morehouse School of Medicine School of Community Health Feasibility Study - Steering Committee; Atlanta
Violence Prevention Capacity Building Project – Project Partner; Clayton County Minister’s Conference, President 2006-2007;
Liberty In Life Ministries, Inc. – Board Secretary; Young Adults Talk, Inc. – Board of Directors; ROYAL, Inc - Board of
Directors; Temple University Alumni Association; Rutgers Law School Alumni Association; Sertoma International; Our
Common Welfare Board of Directors – President)2003-2005; River’s Edge Elementary School PTA (Co-President); Summerhill
Community Ministries; Outstanding Young Men of America; Employee of the Year; Academic All-American - Basketball;
Church Trustee.
Page 204 of 206

www.TheAdvocacyFoundation.org
Page 205 of 206

Page 206 of 206