Individualbased

Individualbased refers to modeling approaches that represent systems as collections of autonomous individuals, each with its own state, rules, and interactions. In practice, this concept is most commonly realized as individual-based modeling (IBM) or agent-based modeling (ABM). These approaches emphasize heterogeneity among individuals and local interactions with the environment and with other individuals, allowing system-level patterns to emerge from micro-level behavior.

Key features of individualbased modeling include explicit representation of individuals (agents) with attributes such as age, location, health, or strategy; behavioral rules governing actions; a dynamic environment and often spatial structure; and often stochastic processes. Time can proceed in discrete steps or continuous time. Such models are used to explore how simple rules at the individual level can yield complex, emergent dynamics at the population or ecosystem level.

Compared with population-level models based on aggregate equations, IBM focuses on micro-level processes and heterogeneity, enabling analyses of spatial structure, social networks, and adaptive behavior. Advantages include realism in representing variation and local interactions; limitations involve data demands for parameterization, computational load, and challenges in validating emergent outcomes.

Applications span ecology and conservation, epidemiology, agriculture, economics and social sciences, urban planning, and logistics. IBM is used to model animal movement, disease transmission, foraging decisions, and crowd dynamics, among other phenomena. Common tools for building IBM include NetLogo, Repast, MASON, and AnyLogic, which support agent definition, rule specification, and simulation experiments.