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1. Purpose of the guide
The purpose of this guide is to suggest a short list of introductory readings to help
newcomers become acquainted with agent-based modeling (ABM). Our primary
intended audience is graduate students and advanced undergraduate students in the social
sciences. Teachers of ABM might also find this guide of use.
Unlike established methodologies such as statistics and mathematics, ABM has not yet
developed a widely shared understanding of what a newcomer should learn. For
decades, concepts such as the level of significance in statistics and the derivative in
mathematics have been common knowledge that newcomers could be expected to learn.
We hope that our selected readings will promote a shared understanding of ABM in the
social sciences, not only among newcomers to ABM but also among researchers who
already use ABM.
As a clarifying note on terminology, although this guide is directed specifically to social
scientists, researchers in a wide range of disciplines are now using ABM to study
complex systems. When specialized to computational economic modeling, ABM reduces
to Agent-based Computational Economics (ACE).
For the convenience of readers, a parallel on-line guide for newcomers to ABM is
available at http://www.econ.iastate.edu/tesfatsi/abmread.htm that includes links to our
suggested readings, as well as demonstration software, as availability permits.
2. Agent-based modeling and the social sciences
1
The social sciences seek to understand not only how individuals behave but also how the
interaction of many individuals leads to large-scale outcomes. Understanding a political
or economic system requires more than an understanding of the individuals that comprise
the system. It also requires understanding how the individuals interact with each other,
and how the results can be more than the sum of the parts.
ABM is well suited for this social science objective. It is a method for studying systems
exhibiting the following two properties: (1) the system is composed of interacting agents;
and (2) the system exhibits emergent properties, that is, properties arising from the
interactions of the agents that cannot be deduced simply by aggregating the properties of
the agents. When the interaction of the agents is contingent on past experience, and
especially when the agents continually adapt to that experience, mathematical analysis is
typically very limited in its ability to derive the dynamic consequences. In this case,
ABM might be the only practical method of analysis.
1
For more detailed discussions of many of the points raised in this section, see Robert Axelrod,
Complexity of Cooperation (1997, Princeton University Press, Princeton, NJ), especially pp. 206-
221, and Leigh Tesfatsion, “Agent-Based Computational Economics: A Constructive Approach to
Economic Theory,” in this Handbook
.
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