On-Line Guide for Newcomers to ABM (Axelrod and Tesfatsion)

On-Line Guide for Newcomers to
Agent-Based Modeling in the Social Sciences

Robert Axelrod and Leigh Tesfatsion

Last Updated: 22 August 2017

Site Maintained By:
Leigh Tesfatsion
Professor of Econ, Math, and ECpE
Department of Economics
Iowa State University
Ames, Iowa 50011-1070
(515) 294-0138
tesfatsi AT iastate.edu


This site provides web support materials (readings and demonstration software) for Robert Axelrod and Leigh Tesfatsion, "A Guide for Newcomers to Agent-Based Modeling in the Social Sciences" (pdf,46KB) , Appendix A (pp. 1647-1659) in Leigh Tesfatsion and Kenneth L. Judd (Eds.), Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics (Table of Contents,html), Handbooks in Economics Series, Elsevier/North-Holland, Amsterdam, the Netherlands, 2006.

The authors thank Catherine Dibble, Stephanie Forrest, Ross Hammond, Ken Judd, Tom Lairson, Irene Lee, Bob Marks, John Miller, Scott Page, and Rick Riolo for helpful advice. The first author thanks the National Science Foundation (Grant No. 0240852) and the LS&A Enrichment Fund of the University of Michigan for financial support.


  1. Purpose of the On-Line Guide
  2. Agent-Based Modeling and the Social Sciences
  3. Selection Criteria
  4. Suggested Readings and Demonstration Software
    1. Complexity and ABM
    2. Emergence of Collective Behavior
    3. Evolution
    4. Learning
    5. Norms
    6. Markets
    7. Institutional Design
    8. Networks
    9. Modeling Techniques
  5. What to Do Next

Software Release Disclaimer:
All demonstration software provided below is unsupported and provided as-is, without warranty of any kind, unless otherwise specified by the provider.


The purpose of this on-line guide is to suggest a short list of introductory readings and supporting materials 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 and supporting materials 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 on-line 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).


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.

ABM begins with assumptions about agents and their interactions and then uses computer simulation to generate "histories" that can reveal the dynamic consequences of these assumptions. Thus, ABM researchers can investigate how large-scale effects arise from the micro-processes of interactions among many agents. These agents can represent people (say consumers, sellers, or voters), but they can also represent social groupings such as families, firms, communities, government agencies and nations.

Simulation in general and ABM in particular is a third way of doing science in addition to deduction and induction. Scientists use deduction to derive theorems from assumptions, and induction to find patterns in empirical data. Simulation, like deduction, starts with a rigorously specified set of assumptions regarding an actual or proposed system of interest; but, unlike deduction, simulation does not prove theorems with generality. Rather, simulation generates data suitable for analysis by induction. In contrast to typical induction, however, the simulated data comes from controlled experiments rather than from direct measurements of the real world.

Consequently, simulation differs from standard deduction and induction in both its implementation and its goals. Simulation permits increased understanding of systems through controlled computational experiments.

The specific goals pursued by ABM researchers take four forms: empirical, normative, heuristic, and methodological. The goal of empirical understanding asks: Why have particular large-scale regularities evolved and persisted, even when there is little top-down control? Examples of such regularities include standing ovations, trade networks, socially accepted monies, mutual cooperation based on reciprocity, and social norms. ABM researchers seek possible explanations grounded in the repeated interactions of agents operating in specified environments. In particular, they ask whether particular types of observed global regularities can be reliably generated from particular types of agent-based models.

A second goal is normative understanding: How can agent-based models be used as laboratories for the discovery of good designs? ABM researchers pursuing this objective are interested in evaluating whether designs proposed for social policies, institutions, or processes will result in socially desirable system performance over time. Examples include design of auction systems, voting rules, and law enforcement. The general approach is akin to filling a bucket with water to determine if it leaks. An agent-based world is constructed that captures the salient aspects of a social system operating under the design. The world is then populated with privately motivated agents with learning capabilities and allowed to develop over time. The key issue is the extent to which the resulting world outcomes are efficient, fair, and orderly, despite attempts by these privately motivated agents to gain individual advantage through strategic behavior.

A third goal is heuristic: How can greater insight be attained about the fundamental causal mechanisms in social systems? Even if the assumptions used to model a social system are simple, the consequences can be far from obvious if the system is composed of many interacting agents. The large-scale effects of interacting agents are often surprising because it can be hard to anticipate the full consequences of even simple forms of interaction. For example, one of the earliest and most elegant agent-based models - the city segregation (or "tipping") model developed by Thomas Schelling (see below) - demonstrates how residential segregation can emerge from individual choices even when everyone is fairly tolerant.

A fourth goal is method/tool advancement: How best to provide ABM researchers with the methods and tools they need to undertake the rigorous study of social systems through controlled computational experiments, and to examine the compatibility of experimentally-generated theories with real-world data? ABM researchers are exploring a variety of ways to address this goal ranging from careful consideration of methodological principles to the practical development of programming, visualization, and empirical validation tools.

In summary, ABM applied to social processes uses concepts and tools from social science and computer science. It represents a methodological approach that could ultimately permit two important developments: (1) the rigorous testing, refinement, and extension of existing theories that have proved to be difficult to formulate and evaluate using standard statistical and mathematical tools; and (2) a deeper understanding of fundamental causal mechanisms in multi-agent systems whose study is currently separated by artificial disciplinary boundaries.

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" (pdf,253KB), in Leigh Tesfatsion and Kenneth L. Judd (Eds.), Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics (Table of Contents and Abstracts), Handbooks in Economics Series, North-Holland/Elsevier, Amsterdam, the Netherlands, 2006, 904pp.


We decided at the outset to offer a short list of readings rather than make any attempt at comprehensiveness. We based our selections on two criteria: (i) the educational value of the reading for newcomers to ABM in the social sciences; and (ii) the accessibility of the reading. The specific choice of topics and readings is our own. We recognize that our selections are personal and necessarily somewhat arbitrary.












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