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Almost 50 years ago Thomas Schelling published the first agent-based model (ABM) in the social sciences. It showed how relatively modest residential preferences on the part of individual households could result in marked patterns of neighborhood residential segregation. Since then, and especially recently, applications have blossomed in many fields ranging from opinion dynamics to supply chain management, from language evolution to disease epidemiology, from consumer behavior to urban planning. The second edition of Introduction to Agent-Based Models targets this broad audience. The author, Nigel Gilbert, is one of the founders of computational social science and an authority on agent-based models.

As Professor Gilbert defines it, agent-based modeling is “a computational method that enables a researcher to create, analyze, and experiment with models composed of agents that interact within the environment.” ABMs range from highly abstract simplified models to facsimile models that attempt to replicate real observations. They explicitly link micro and macro levels of analysis, as illustrated by Schelling’s model of households and neighborhoods. Because agent-based models incorporate dynamic interdependencies among the individual agents, the consequences for macrolevel change in these models are emergent, frequently nonlinear, and sometimes surprising, as was the case with Schelling’s model.

Like the first edition, the second edition of Introduction to Agent-Based Models is for beginners. It is suitable as a supplement for undergraduate as well as graduate courses in formal models, simulation, and computational social science; it is also a quick first introduction for any interested social science practitioner. The author carefully defines concepts, outlines the steps involved in planning, building, and reporting ABMs, and includes a helpful glossary. Readers are shown how to use the NetLogo modeling environment, freely available to students, teachers, and researchers worldwide, to build and run a simple ABM. NetLogo helps readers get their feet wet, even those with little background in coding. The second edition of Introduction to Agent-Based Models retains the strengths of the first but updates the material, expands the coverage of verification, validation, and documentation, and addresses some new topics such as the use of ABMs to inform public policy. As was true for the first edition, the goal is to make readers better consumers of published ABMs and to provide the foundation for them ultimately to be creators of these models.

Agent-based modeling is a fast-moving area, especially in breadth of application. In addition, ABMs are increasingly a focus of interdisciplinary collaboration, between social/behavioral scientists from different disciplines (e.g., sociology and geography), between social/behavioral science and natural science (e.g., environmental science), and between social/behavioral science and computer science. Depending on purpose, the rules central to agent-based models can be derived from theory, past empirical research, and/or conversations with local experts. Indeed, ABMs are increasingly used in community-based participatory research. Given these trends, the need for a generally accessible primer is even greater now than when the first edition was published in 2007. This second edition fully satisfies that need.

Barbara Entwisle

Series Editor