An
agent-based model (
ABM) is one of a class of
computational models for
simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) with a view to assessing their effects on the system as a whole. It combines elements of
game theory,
complex systems,
emergence,
computational sociology,
multi-agent systems, and
evolutionary programming.
Monte Carlo methods are used to introduce randomness. Particularly within ecology, ABMs are also called
individual-based models (
IBMs), and individuals within IBMs may be simpler than fully autonomous agents within ABMs. A review of recent literature on individual-based models, agent-based models, and multiagent systems shows that ABMs are used on non-computing related scientific domains including
biology, ecology and
social science. Agent-based modeling is related to, but distinct from, the concept of
multi-agent systems or
multi-agent simulation in that the goal of ABM is to search for explanatory insight into the collective behavior of agents obeying simple rules, typically in natural systems, rather than in designing agents or solving specific practical or engineering problems.