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

Robert Axelrod, and Leigh Tesfatsion. 2005. A Guide for Newcomers to Agent-Based Modeling in the Social Sciences. In Handbook of Computational Economics: Agent-Based Computational Economics , edited by K. L. Judd and L. Tesfatsion: North-Holland.

This guides gives a short introduction to Agent-based modeling and the social sciences and suggests a list of introductory readings to help newcomers become acquainted with agent-based modeling (ABM)

Social sciences seeks the understanding how the individuals interact with each other, and how the results can be more than the sum of the parts (how large-scall effects arise from the micro-processes of interactions among many agents). ABM is a method for studying the following 2 properties:

• the system is composed of interacting agents
• the system exhibits emergent properties. 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.

The 3rd Way
ABM (and simulation in general) is a third way of doing science in additiona to deduction and induction. Simulation, like deduction, starts with a set of explicit assumption. But unlike deduction, simulation does not prove theorems with generality. Instead, simulation generates data suitable for analysis by induction.

ABM is a methodological approach that can be used to pursue the following goals:

Empirical understanding
Can particular types of observed global regularities can be reliably generated from particular types of agent-based models.

Normative understanding
Evaluating whether designs proposed for social policies, institutions, or processes will result in socially desirable system performance over time.

Heuristic
The way a greater insight can be attained about the fundamental causal mechanisms in social systems? The large-scale effects of interacting agents are often surprising because ti can be hard to anticipate the full consequences of event simple forms of interaction.

The best example to depict heurisitc in agent-based models is the city segregation model developed by Thomas Schelling: Schelling, Thomas C. (1978), Micromotives and Macrobehavior, Norton, New York, pp. 137-57.

This classic work demonstrates what can happen when behavior in the aggregate is more than the simple summation of individual behaviors. The highlighted pages present an agent-based model that shows how a high degree of residential segregation can emerge from the location choices of fairly tolerant individuals.

Methodological advancement
Provide methods and tool necessary to undertake study of social systems through controlled computational experiments.

The suggested readings are categorized in:

• Complexity and ABM
• Emergence of collective behavior
• Evolution
• Learning
• Norms
• Markets
• Institutional design
• Networks
• Modeling techniques