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Agent-Based Modeling (ABM) is a simulation-based method for studying complex systems by modeling autonomous agents and their interactions. It enables analysis of emergent phenomena, policy experiments, and scenario testing. ABM is used in social sciences, ecology, and economics to reveal micro–macro linkages.
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What organizes, connects, or makes decisions possible.
Agent-based modeling (ABM) simulates a system through individual agents with their own states and rules interacting in an environment.
The approach emerged from a need to explain complex systems through the behavior and interactions of individual participants. It combines ideas from simulation, computer science, and the social and natural sciences rather than modeling only aggregate averages.
Build a small world: agents have properties and decision rules, the environment provides resources and constraints, and time advances in steps or events. Observable system patterns emerge from local interactions. Experiments vary rules or initial conditions and compare the consequences.
An agent represents an acting unit with state and behavior.
The environment defines resources, spaces, and possible interactions.
Rules translate assumptions about decisions into executable steps.
System patterns arise from many local interactions.
ABM suits questions where heterogeneity, adaptation, and interaction matter, for example in traffic, ecology, or markets. Results are scenarios under model assumptions, not unvalidated predictions.
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