Jim Rutt defines agent-based modeling as a computational methodology where individual entities, known as agents, operate based on a set of rules and interact within a defined environment to simulate complex systems and emergent behaviors. In his view, this technique allows for granular exploration of the dynamics within a system by focusing on the actions and interactions of its smallest units, rather than depending on aggregate equations typical of traditional modeling. Rutt emphasizes the power of agent-based models (ABMs) in offering unique insights into phenomena ranging from social behaviors to economic systems by capturing the heterogeneity and adaptability of agents. This bottom-up approach enables researchers to observe how macro patterns can emerge from micro-level processes, making it a potent tool for understanding real-world complexity.

See also: emergence, evolutionary computing, evolution, game theory