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Agent Based Modeling - An Introduction
Agent-Based Models use individual computer objects as players or agents. Governed by a set of rules, these agents are turned loose in a computer-generated landscape to perform their appointed task such as trading, segregating, spreading disease or minority opinions, reacting, creating mayhem, bank fraud, or any number of other mischievous endeavors. Often, their resultant behavior has a remarkable similarity to observed reality and can also lead to an understanding of emergent behavior.
Agent-based models, ABM, are non-deterministic in that the outcome itself is not modeled nor often known. In conventional modeling, equations that fit the final stage are often developed and used to model not only the final state but also the development thereof. However, in ABM individual “players” or agents with a basic set of rules each act as “individuals”. The holistic behavior of these agents is the result of the interaction of each individual agent with other agents and with the environment that in turn acts upon the individual agent. The advent of sufficient computational power in the last decades has allowed the incorporation of sufficient number of agents and a large enough landscape to provide interesting and meaningful results.