Application of Models of Decision Making During Uncertainty to Simulations of Military Command and Control Systems

摘要

Most military decision making requires a sequence of actions. These actions may include aspects of intelligence gathering, troop movement, artillery fire, etc. Typically, these actions are tied to a specific goal that might include securing a region or disrupting the enemy forces. Furthermore, when one is making the necessary decisions to reach the specific goal, there may be much uncertainty about the situation. Where exactly are the enemy troops. What is their objective. The use of Bayesian statistics makes it possible to compute optimal performance for military-like situations. This research develops a model that provides the theoretical best performance that can be achieved in the task. The current paradigm for the description and understanding of the nature of command and control (C2) system (C2S) operations and performance within the U.S. Army is undergoing a radical change. Tactical battlefield C2 is extremely complicated to orchestrate and conduct in an effective manner. With the introduction of a myriad of new information systems, sophisticated new weapons with unprecedented capabilities for lethality, new requirements for battlefield integration, and the total reorganization of force structures into a new modular concept, the need for effective understanding of how this force structure can work effectively as a system entity increases dramatically. The C2S has become complicated to the point as to escape the ability for intuitive understanding of how individual components or subsystems can improve or degrade the operation of the overall system. The goal of this research is to understand the cognitive limitations associated with sequential decision making with uncertainty in these types of situations through predictive computer simulation. When empirical research investigating optimal decision making during uncertainty is combined with evolving simulations of military C2, the potential now exists to correlate optimal decision making performance with actual.