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Construct is an agent-based network-centric simulation. Although frequently lumped together, agent-based simulations vary widely in complexity and computational cost some are extremely inexpensive (e.g., Swarm) and allow hundreds of thousands or even millions of agents to operate in the same simulation, while others are rather expensive and often require the support of an entire processor per agent (e.g., Soar or ACT-R). This increase in computational expense, however, is matched by construct validity to the actions of cognitively bounded humans: the least computationally expensive (per agent) simulations replicate the behavior of insects (specifically ants) while ACT-R has been able to replicate the brain activation patterns of children solving algebra problems and Soar has replicated fighter pilot operations in concert with human pilots., Supported in part by ONR under contract N00014-08-1-1186.

Construct is a multi-agent simulation tool that is commonly used to investigate dynamic behavior in complex socio-cultural systems. This technical report describes the parameters necessary to specify agents in the simulation, focusing especially on the features which help describe agent behavior. It also introduces a number of pre-defined agent classes, stock-agents which can be used to quickly build up a simulation. This document is intended both as an introduction to Construct model and as a reference guide for simulation experts and casual modelers., Continued funding under Grant No. N00014-06-1-0772, NSF under Grant No. SES-0452487, ARL under Grant No. DAAD19-01-2-0011, DAAD19-01-2-0009 and AFOSR.

In Operations Other Than War (OOTW), there is a need for intelligence analysts and military planners to anticipate the actions and responses of complex networked organizations such as terrorist groups, nation-states and key actors (such a high-level leaders). The Complex Organizational Reasoning System (CORES) is a multi-agent network simulation model that uses organizational, social, political and economic dynamics to generate predictions of the likely actions and responses of these actors when involved in an adversarial context., The original document contains color images.