Open Innovation 2.0 Yearbook 2013 - European Commission - Europa

36 O P E N I N N O V A T I O N 2 0 1 3
The question which immediately comes to mind is
why these components make cities intelligent. If the
presence of the ‘city’ component is somehow selfexplained
(we search for intelligent cities and city
intelligence), we should turn to definitions and understandings
of intelligence to justify the presence of
the other two components of the standard model.
The paper of Legg and Hutter [8] on definitions of
intelligence offers an inventory of definitions, the
largest and most well-referenced collection according
to the authors. They list 18 definitions of intelligence
that have been proposed by groups or organisations,
35 definitions by psychologists, and 18
by researchers in artificial intelligence. Then, they
scan through these definitions pulling out commonly
occurring features and conclude that intelligence
has three key attributes which occur simultaneously,
such as the property of an individual
agent to interact with its environment, the ability
to succeed or profit with respect to some goal or
objective, and to adapt to different objectives and
environments. These attributes refer, on the one
hand, to the ability to collect, process and exchange
information, the ability for perceiving, storing and
retrieving information, calculating, reasoning, learning,
acquiring knowledge and, on the other hand,
to the ability to find solutions and innovate, plan,
apply knowledge to practice, solve novel problems,
create products, and achieve complex goals in complex
environments.
Understanding ‘intelligence’ with respect to the
abilities of ‘information processing’ and ‘problemsolving’
justifies the presence of the ‘digital’ and
‘innovation’ components in the intelligent cities’
standard model. If cities are to become intelligent,
they should enable large-scale and city-wide communication
and information processing (through
digital interaction), and define pathways that resolve
cities’ problems and challenges (through innovation
networks and ecosystems). The fundamental components
have different roles to this end. Cities offer
the human communities, skills and resources, the
physical infrastructure of human action, the capacity
for governance and management. But cities are
also fields of conflicts, contradictions, problems to
resolve, and challenges to address. Innovation and
innovation ecosystems define how solutions to city
challenges are produced; how citizens and organisations
respond to challenges; how to create new
products and services to address challenges; how
to adapt to changing conditions. The Internet offers
capabilities for information processing and the digital
agglomeration of resources, and makes cities
interactive, capable of gathering, storing, and disseminating
information.
Complementarities among cities’ innovation processes
and digital interactions are not only related
to their different roles. Innovation itself changes dramatically
as it is immersed into the Internet. Somehow,
innovation nodes ‘explode’ and multiply geometrically
as large urban communities are involved and
undertake innovation tasks and social media organise
interactions among the members of communities.
Crowdsourcing is a good case for understanding
such interactions between innovation, social media,
and large communities. Crowdsourcing comes from
the combination of ‘crowd’ and ‘outsourcing’ and
the main idea is to assign a task to a large group
of people or a community [9]. It is an extreme form
of open innovation in which tasks are not assigned
to selected external providers, but to the crowd.
In the case of intelligent cites, crowdsourcing
tasks focus on innovation, while problem-solving
is expected from end-users and citizens. Crowdsourcing
is also strongly related to digital interaction,
online platforms and collaborative Web spaces
because the participation of large communities
(crowds) presupposes the use of digital media. It is
an online, distributed problem-solving and production
model [10]. It also characterises a major stage
in the evolution of the intelligent cities’ standard
model during the first decade of 21st century. Two
cases that illustrate the use of crowdsourcing in
smart cities are ‘NYC Simplicity Idea Market’ and
‘Improve-My-City’, an application used in many cities
all over the world.
NYC Simplicity Idea Market was launched in February
2011 by New York City and remained in operation
for about a year. Employees at all levels of
administration and city agencies were invited to
suggest and share ideas about improvements to city
government. Everyone could upload ideas, comment
on the ideas proposed by others, and vote for those
considered the best. Then, the most popular proposals
were reviewed by experts and the best were
implemented by the city [11]. The components of this
experiment are quite clear: a large community in the
city, estimated at 300 000 employees, was invited
to elaborate on ideas about education, safety, and
the maintenance of the city’s infrastructure. Innovation
was based on the combination of ideas generation
by employees, user-driven evaluation of ideas,
feasibility assessment by experts, and ideas implementation
by the city. A content management system
and crowdsourcing platform was used to enable
employees’ participation and assessment through
voting. Everything revolved around crowdsourcing,
involving a large community of the city, selecting
ideas by preference of the same group, and enabling
participation through social media.