Agent-Based Modeling and Simulation (ABMS) is a framework that allows experimentation with simulated complex systems. It conceives complex systems as a set of agents that interact and adapt to changing environments. Starting with a description or theory of individual agents, it seeks to formulate rules of behavior such as rational behavior (behavioral economics), stimulus-driven motion (cell biology), flocking (animal behavior), utility maximization (organization behavior) and so on. Based on these rules, ABMS can then be used to study the behavior of a system as a whole – that is, of how the system could evolve in time. By generating unexpected patterns of behavior that might emerge from interactions among simple agents, ABMS provides insights into both individual agent and overall system behaviors. It can help the experimenter anticipate system interactions, structures, and possible evolutionary paths, allowing them to answer questions such as the following:
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What agent rules influence system behavior and how?
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What types of agents, if any, are going to dominate the system?
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How fast are the changes going to be?
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How stable, turbulent, or resilient the system is going to be?
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How valid are our current theories of agent behavior?
By providing insights and answers to these questions, ABMS proves to be a useful framework for physical, biological, and social scientists alike, as well as managers, decision-makers, and policy-makers.