Command Decision Modeling in Distributed Combat Simulation

The rapid explosion in the volume and diversity of information circulating on the modern battlefield presents a huge challenge to the decision makers and warfighters who must plan and dynamically adapt to changing circumstances while assimilating new or changing information, often under conditions of time pressure, uncertainty, information overload and stress. There is also the corresponding challenge to those attempting to model the battlefield situation. In the past, humans in the loop have been required to successfully run the simulations. In constructive simulations, the challenge for battlefield modelers is in predicting accurate outcomes which rests on the ability of the model to portray the limits in information processing and decision making of the operators and decision makers. The MATREX simulation models attempt to fill this requirement at a very rudimentary level, primarily representing basic platform and aggregate behaviors. However, the models do not sufficiently represent human level complex decision processes and do not take into account how human workload in one model impacts performance in another. This Phase I proposal describes the approach DCS will take to study the existing methodologies to improve the representation of automated decision-making in combat simulations and its fidelity to human decision making in the same scenarios.