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Characteristics of an Agent-Based Simulation Model

An agent-based simulation model supports “complex decision systems where a system or network is modeled as a set of autonomous decision-making units called agents that individually evaluate their situation and make decisions on the basis of a set of predefined behavior and interaction rules.”[1] This modeling technique uses a bottom-up approach, targets dynamic systems, and employs adaptive learning versus optimization. A distinguishing characteristic is the capture of the results of system component interactions – something you might witness observing a flock of birds as it moves seemingly erratically across the sky.

This definition is, oddly enough, reminiscent of every James Bond movie. Our agent, James, exists in a tremendously chaotic global environment which continues to expand geographically (across the globe and in space) and politically (with the introduction of Spectre and other nefarious secret organizations), and in this env