agentbased

Agent-based modeling (ABM) is a computational modeling approach in which systems are represented as a collection of autonomous, interacting agents. Each agent has state, behavior, and decision rules, and may adapt or learn over time. The model defines an environment that agents inhabit, as well as the rules for interaction, movement, and observation. Through local interactions among agents, complex, system-level phenomena—emergent properties—can arise that are not obvious from the individual rules alone.

Agents can represent individuals, households, organizations, devices, or other actors relevant to the domain. ABM supports heterogeneity among agents and can incorporate data at various levels, including learning, adaptation, and strategy change. Time is typically discrete, and the environment may be spatial, network-based, or hybrid.

Common applications include social sciences (migration, cooperation, market dynamics), economics, epidemiology, ecology, urban planning and housing, traffic and crowd dynamics, and supply chain and logistics. ABM has roots in ecological and artificial-life research and was popularized in the social sciences and economics during the 1990s and 2000s; notable milestones include Sugarscape by Epstein and Axtell and the work of Railsback and Grimm in ecology.

Implementation is supported by several software platforms such as NetLogo, Repast, MASON, and Mesa. Building an ABM generally involves defining agent types and states, decision rules, the environment, interaction topology, data collection, calibration, and validation.

Strengths include the ability to model heterogeneous agents, local interactions, and emergent phenomena; flexibility to explore hypothetical scenarios. Limitations include data demands, model complexity, computational requirements, and challenges in validating emergent outcomes, which can complicate interpretation and generalization.