Agent-Based Modeling and Simulation, Introduction to
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Discrete mathematical modeling · Simulation · Complex systems Agent-based modeling (ABM) is a computational modeling paradigm that is markedly useful in studying complex systems composed of a large number of interacting entities with many degrees of freedom. Other names for ABM are individualbased modeling (IBM) or multi-agent systems (MAS). Physicists often use the term microsimulation or interaction-based computing. The basic idea of ABM is to construct the computational counterpart of a conceptual model of a system under study on the basis of discrete entities (agents) with defined properties and behavioral rules and then to simulate them in a computer to mimic the real phenomena. The definition of agent is somewhat fuzzy as witnessed by the fact that the models found in the literature adopt an extremely heterogeneous rationale. The agent is an autonomous entity having its own internal state reflecting its perception of the environment and interacting with other entities according to more or less sophisticated rules. In practice, the term agent is used to indicate entities ranging all the way from simple pieces of software to “conscious” entities with learning capabilities. For example, there are “helper” agents for web retrieval, robotic agents to explore inhospitable environments, buyer/seller agents in an economy, and so on. Roughly speaking, an entity is an “agent” if it has some degree of autonomy, that is, if it is distinguishable from its environment by some kind of spatial, temporal, or functional attribute: an agent must be identifiable. Moreover, it is usually required that an agent must have some
autonomy of action and that it must be able to engage in tasks in an environment without direct external control. From simple agents, which interact locally with simple rules of behavior, merely responding befittingly to environmental cues, and not necessarily striving for an overall goal, we observe a synergy which leads to a higher-level whole with much more intricate behavior than the component agents (holism, meaning all, entire, total). Agents can be identified on the basis of a set of properties that must characterize an entity and, in particular, autonomy (the capability of operating without intervention by humans, and a certain degree of control over its own state); social ability (the capability of interacting by employing some kind of agent communication language); reactivity (the ability to perceive an environment in which it is situated and respond to perceived changes); and pro-activeness (the ability to take the initiative, starting some activity according to internal goals rather than as a reaction to an external stimulus). Moreover, it is also conceptually important to define what the agent “environment” in an ABM is. In general, given the relative immaturity of this modeling paradigm and the broad spectrum of disciplines in which it is applied, a clear-cut and widely accepted definition of high-level concepts of agents, environment, interactions, and so on is still lacking. Therefore a real ABM onto
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