Agent-Based Modeling

Agent-based modeling represents people, organizations, vehicles, or other entities as individual agents whose decisions and interactions generate system-level outcomes. Researchers use rule-based and game-theoretic models, networks, reinforcement learning, and increasingly large language models, sometimes combining these with equations or continuous simulations. Applications include epidemics, markets, traffic, evacuation, public policy, and social opinion, where simulations can test mechanisms and compare interventions that are difficult to study directly. A central challenge is establishing that model behavior is empirically credible and robust: richer, larger simulations can be harder to validate, and fluent language-model agents do not by themselves guarantee realistic collective behavior.