Optimization-Based Agent Simulations for Evaluating the SPEYES System

Abstract : This paper presents an optimization-based agent-driven Distributed Dynamic Decision-making (DDD) simulation model to evaluate the SPEYES (Security and Patrolling Enablers Yielding Effective SASO - Support and Stability Operations) system. The key challenge is to quantify the force multiplying effect of SPEYES technologies, which span sensing, situation awareness/command and control (SA/C2), and shaping components. The performance improvements were measured in terms of timeliness, effectiveness, and efficiency of operations. The behaviors of optimization-based agents were first calibrated to those of human-in-the-loop simulations. The agent-driven simulation results indicated that integration of SPEYES sensing, SA/C2, and shaping technologies provided significant performance improvements to the force across all measures. Even at 50%-reduced force, the SPEYES system maintained significant performance improvements over regular operations with a full force and without SPEYES, thus confirming the force multiplier effect of SPEYES technologies. The findings are confirmed by human-in-the-loop simulations.