Agent-based modeling is a long-standing but underused method that allows researchers to simulate artificial worlds for hypothesis testing and theory building. Agent-based models (ABMs) offer unprecedented control and statistical power by allowing researchers to precisely specify the behavior of any number of agents and observe their interactions over time. ABMs are especially useful when investigating group behavior or evolutionary processes and can uniquely reveal nonlinear dynamics and emergence—the process whereby local interactions aggregate into often-surprising collective phenomena such as spatial segregation and relational homophily. We review several illustrative ABMs, describe the strengths and limitations of this method, and address two misconceptions about ABMs: reductionism and “you get out what you put in.” We also offer maxims for good and bad ABMs, give practical tips for beginner modelers, and include a list of resources and other models. We conclude with a seven-step guide to creating your own model.
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