Robert Axelrod, The Complexity of Cooperation: Agent-Based Models of
Competition and Collaboration, Princeton University Press, Princeton, NJ,
A. V. Banerjee, "A Simple Model of Herd Behavior", Quarterly
Journal of Economics 107 (1992), 797-817.
S. Bikhchandani, D. Hirshleifer, and I. Welch, "A Theory of Fads,
Custom, and Cultural Change as Information Cascades", Journal of
Political Economy 100 (1992), 992-1026.
Lars-Erik Cederman, Emergent Actors in World Politics: How States and
Nations Develop and Dissolve, Princeton University Press, Princeton, NJ,
The author defines "state" and "nation" in Weberian terms. A
state is a territorial organization exercising legimitimate control
over its own territory, undisturbed by internal power competition or external
intervention. A nation is a "community of sentiment which would
adequately manifest itself in a state of its own" and hence "tends to produce
a state of its own." Building on concepts from complex adaptive systems
modeling, the author presents a series of models that separate the state from
the nation and incorporates these as emergent rather than preconceived
by David Lazer (Kennedy School of Government, Harvard) is available.
Lars-Erik Cederman, "Modeling the Size of Wars",
American Political Science Review, Vol. 97, 2003, pp. 135-150
Published article available (in 2007) at
Power-law distributions, scaling laws, and self-organized
criticality are features of many frequency distributions, from word usage to
avalanches, and from firms to cities. A set of events is said to behave in
accordance with a power law distribution if large events are rarer
than small events, and specifically if the frequency of an event is
inversely proportional to its size. An example is the distribution of
the sizes of wars. Cederman uses an agent-based model of war and state
formation in the context of technological change to account for this observed
regularity. His paper is a good example of how a fairly complicated model
and its implications can be clearly presented, with details left to an
Myong-Hun Chang and Joseph E. Harrington Jr., "Agent-Based Models of Organizations",
Chapter 26 in Leigh Tesfatsion and Kenneth L. Judd (Eds.),
Handbook of Computational Economics II: Agent-Based Computational Economics, Handbooks in Economics Series, Elsevier/North-Holland, 2006.
J. Enelow and M. Hinich (eds.), Advances in the Spatial Theory of
Voting, Cambridge University Press, New York, 1990.
Kenneth L. Judd and Scott E. Page, "Computational Public Economics",
pages 195-202 in Journal of Public Economic Theory, Special Issue on
Computational Models in Public Economic Theory, Volume 6, Number 2, May 2004.
Scott E. Page,
"Computational Methods and Models of Politics",
in Leigh Tesfatsion and Kenneth L. Judd (editors),
Handbook of Computational Economics, Vol. 2: Agent-Based Computational
Economics, Handbooks in Economics Series, North-Holland/Elsevier, Amsterdam,
In this chapter, we assess recent contributions of computational
models to the study of politics. We focus primarily on agent-based
models developed by economists and political scientists. These
models address collective action problems, questions related to
institutional design and performance, issues in international
relations, and electoral competition. In our view, complex systems
and computational techniques will have a large and growing impact on
research on politics in the near future. This optimism follows
from the observation that the concepts used in computational
methodology in general and agent-based models in particular resonate
deeply within political science because of the domains of study in
the discipline and because early findings from agent-based models
align with widely known empirical regularities in the political
world. In the process of making our arguments, we survey a portion
of the growing literature within political science.
Kenneth Kollman, John H. Miller, and Scott E. Page, "Computational
Political Economy", pp. 461-490 in W. Brian Arthur, Steven Durlauf, and
David Lane (eds.), The Economy as an Evolving Complex Economy II, SFI
Studies in the Sciences of Complexity, Volume XXVII, Addison-Wesley, Reading,
Kenneth Kollman, John H. Miller, and Scott E. Page, "Political
Institutions and Sorting in a Tiebout Model", American Economic
Review, Volume 87, 1997, 977-992.
Published article available at
The authors develop an agent-based model to explore how social outcomes
are affected by the political institutions used to aggregate individual
choices on local public goods issues, such as whether or not to finance a
community swimming pool. Examples of such political institutions are
referenda, two-party competition, and proportional representation. For each
tested political institution, assumed to be commonly in use across all
jurisdictions, citizens "vote with their feet" in each time period regarding
which jurisdiction they wish to inhabit. The policy positions resulting in
any given jurisdiction depend on the preferences of the citizens located
within that jurisdiction, in a manner determined by the political institution
in force. Citizens can continue to relocate in response to changing local
policy positions, and local policy positions can continue to change in
response to citizen relocations. The authors find that social efficiency is
highest under political institutions such as two-party competition or
proportional representation that initially induce citizens to undertake a
suitable degree of experimentation among alternative jurisdictions.
R. H. Sander, D. Schreiber, and J. Doherty, "Empirically
Testing a Computational Model: The Example of Housing Segregation"(pdf,53KB),
UCLA working paper, 2000.
Abstract: Using the Swarm simulation environment,
this paper constructs a computational model of racial housing
segregation that extends the Schelling Segregation Model.
Preference functions are derived from empirical data on neighborhood
racial composition and from a variety of other factors conjectured
to be important in housing decisions. The model is used to examine
the contemporary debate about the nature and causes of housing
Thomas Schelling, Micromotives and Macrobehavior, W. W. Norton,
New York, 1978.
Paul M. Torrens, "New Tools for Simulated Housing Choices"(pdf,943KB),
presented at the Special Fannie Mae Foundation Session: Housing and
the New Economy, Washington, D.C., May 2001.
This paper presents a framework for urban geographic simulation that infuses
approaches derived from geocomputation and complexity (e.g, Schelling-type
cellular automata multi-agent models) with standard techniques that have been
tried and tested in operational land-use and transport simulation.
Annotated list of pointers to
ACE/CAS Computational Laboratories and Demonstration Software
for ACE in particular and complex adaptive systems in general. Included are
several spatial demos (e.g., the Schelling Segregation Model and demos for
cellular automata) of possible use for voting and Tiebout (local public
Resource Sites, Groups, and Some Early Individual Researchers
Association for Evolutionary Economics (AFEE)
is an international organization of economists and other social scientists
devoted to the analysis of economies as evolving, socially constructed, and
politically governed systems. The intellectual heritage of AFEE is that of
the original institutional economics created and developed by early
twentieth-century economists such as Thorstein Veblen, John R. Commons, and
Wesley Mitchell. The AFEE sponsors the Journal of Economic Issues,
published quarterly, whose primary mission is to present articles that use
and develop the core ideas of institutional economics in discussions of
current economic problems and policy alternatives.
(School of Public Policy Studies, University of Michigan, Ann Arbor):
Complexity of cooperation; Evolution of cooperation.
(Swiss Federal Institute of Technology (ETH), Zurich): Agent-based
computational modeling; International relations theory; Nationalism,
integration, and disintegration processes; Historical sociology; RePast
(Java-based toolkit for agent-based simulation).
Joshua M. Epstein
(Emergency Medicine, Economics, Biostatistics, and Environmental Health, Johns Hopkins University, Washington D.C.): Sugarscape; Growing societies from the bottom up; Modelling of complex
social systems, with application to international security, environmental,
and policy areas.
(Wharton, University of Pennsylvania):
Industrial organization; Political economy; Evolutionary economics; Game
(Department of Political Science, University of Michigan, Ann Arbor):
Computational political economy; Political parties and electoral landscapes;
Interest groups, ideological bias, and Congressional committees; Development
of national political parties; Effects of multi-layered electoral competition
in federal political systems.
John H. Miller
(Social and Decision Sciences, Carnegie Mellon University,
Pittsburgh, Pennsylvania): Computational economic modelling; Complex
adaptive behavior in social and political systems; Artificial adaptive agent
(Department of Political Science and Center for Complex Systems, University
of Michigan, Ann Arbor, U.S.A.): Problem solving by heterogeneous agents; On
the emergence of cities; Diversity and optimality; Political institutions and
sorting in a Tiebout model.
(Department of Politics, University of Exeter, UK): Agent-based computational modeling of the formation of political parties, housing segregation, and political cognition; Swarm modeling.