A new urban paradigm pathways to sustainable development

Urban governance and ex ante
policy evaluation: an agent-based
model approach 1
by Bernardo Alves Furtado, Isaque Daniel
Rocha Eberhardt and Alexandre Messa 2
This article suggests that better
urban governance may be obtained
via ex ante policy analysis. Focusing
on prognostics, rather than diagnostics,
may empower cities in their drive to
foster ‘sustainable development’.
Governance of cities is a complex matter
(Bettencourt 2015). It involves heterogeneous
citizens and interests, a number of
institutions and values, and businesses of
all denominations. These interactions happen
over space in time and are conditioned
by legislation, politics, administrative
boundaries and the environment.
Given this context of multiple actors
and multiple interactions that occur
dynamically over heterogeneous space,
urban governance should definitely
make use of all data available on all
actors, on their interactions, on their
interests, on their location, if possible
even on their plans. However, beyond
collecting and managing such data
coherently, good governance can only
happen when data are organised in
such a way that they can make sense
and resemble the actual processes and
outputs of cities. If the city mechanisms
can be fully grasped, then policy
recommendations and governance
should come automatically as a result.
Complex systems (Furtado, Sakowski, and
Tóvolli 2015; Mitchell 2011) encapsulate
the view that cities are the emergent,
ever-changing result of interactions
among heterogeneous actors (Bettencourt
2015). Agent-based modelling (ABM)
is the methodology that comprises the
theoretical baseline of complex systems.
In ABM, a computational simulation runs
a model in which ‘agents’ are entities that
represent citizens, businesses, institutions
and governments (Gilbert and Terna 2000;
Macal 2016; North and Macal 2007;
Sayama 2015; Wilensky and Rand 2015).
This article presents an ABM framework—
Spatially-bounded Economic Agent-based
Lab (SEAL)—that aims at simulating
citizens, businesses and governments
within political-administrative environment
boundaries that can be used to evaluate
policy proposals ex ante and thus serve
as an effective governance tool for the
various levels of the government.
The following section of the article
contains a description of the model,
presenting some of its preliminary and
planned applications, and the final section
discusses the possibilities, advantages and
limitations of applying the ABM to urban
governance and policy evaluation.
The basic model: SEAL
SEAL was originally built to investigate
the collection of taxes and the
redistribution of public services across
municipalities in metropolitan regions
(Furtado and Eberhardt 2016a). Taking
advantage of the additive, modular
structure that is typical of the ABM, the
model has evolved from a case study
into an empirical framework that enables
multiple analyses.
The framework is built in Python
3.4.4 3 (Downey 2012) in a full objectoriented-programming
(OOP) paradigm.
That is in accordance with the theory,
allowing agents to be independent
and react individually according to
their personal states and methods,
but also according to their local,
familiar and temporary environment.
Thus, SEAL contains classes for citizens,
families, businesses and governments (of
each municipality) and is based on official
data. The citizens and their family collectives
interact with businesses, the government
and each other in three markets.
In the goods market, families make
consumption decisions on homogenous
products from a selection of different
businesses. Families make their decisions
based on both prices and distance.
In the labour market, businesses seek
qualified workers or workers who live
closest, whereas likely employees look
for businesses that pay higher salaries.
In addition, given its importance for urban
analysis, there is also a real estate market.
Governments are responsible for
collecting taxes from businesses within
their own jurisdiction. Taxes are then
used to proportionally increase the
municipalities’ own quality of life index,
which is a proxy of available services
for citizens. Production and commuting
happen every day, whereas most other
activities happen sequentially at the
end of every month (see Figure 1):
• y process demographics
(births, aging and deaths);
• y firms make payments;
• y family members consume;
• y governments collect taxes;
• y governments spend the taxes
collected on improving the quality
of municipal life;
• y firms calculate profits and update prices;
• y the labour market is processed;
• y the real estate market is processed; and
• y statistics and output are processed.
Sensitivity analysis should also be
conducted; it helps build the robustness
of the model and evaluate the influence
of different policymaking.
Policy applications: current and planned
Up until now, SEAL has been applied
as a theoretical preliminary exercise
in which the municipalities are merged
for tax purposes (Furtado and Eberhardt
2016a) 4 and as a general analysis of the
influence of macroeconomic changes
into commuting demand (Furtado
and Eberhardt 2016b).
30