Statistical mechanics of combat with human factors

Abstract

This highly interdisciplinary project extends previous work in combat modeling and in control-theoretic descriptions of decision-making human factors in complex activities. A previous paper has established the first theory of the statistical mechanics of combat (SMC), developed using modern methods of statistical mechanics, baselined to empirical data gleaned from the National Training Center (NTC). This previous project has also established a JANUS(T)-NTC computer simulation/wargame of NTC, providing a statistical “what-if” capability for NTC scenarios. This mathematical formulation is ripe for control-theoretic extension to include human factors, a methodology previously developed in the context of teleoperated vehicles. Similar NTC scenarios differing at crucial decision points will be used for data to model the influence of decision making on combat. The results may then be used to improve present human factors and C 2 algorithms in computer simulations/wargames. Our approach is to “subordinate” the SMC nonlinear stochastic equations, fitted to NTC scenarios, to establish the 0 th order description of that combat. In practice, an equivalent mathematical-physics representation is used, more suitable for numerical and formal work, i.e., a Lagrangian representation. Theoretically, these equations are nested within a larger set of nonlinear stochastic operator-equations which include C 3 human factors, e.g., supervisory decisions. In this study, we propose to perturb this operator theory about the SMC 0 th order set of equations. Then, subsets of scenarios fit to 0 th order, originally considered to be similarly degenerate, can be further split perturbatively to distinguish C 3 decision-making influences. New methods of Very Fast Simulated Re-Annealing (VFSR), developed in the previous project, will be used for fitting these models to empirical data.