Robust Resource Allocation in Security Games and Ensemble Modeling of Adversary Behavior

Abstract:

Game theoretic algorithms have been used to optimize the allocation of security resources to improve the protection of critical infrastructure against threats when limits on security resources prevent full protection of all targets. Past approaches have assumed adversaries will always behave to maximize their expected utility, failing to address real-world adversaries who are not perfectly rational. Instead, adversaries may be boundedly rational, i.e., they generally act to increase their expected value but do not consistently maximize it. A successful approach to addressing bounded adversary rationality has been a robust approach that does not explicitly model adversary behavior. However, these robust algorithms implicitly rely on an efficiently computable weak model of adversary behavior, which does not necessarily match adversary behavior trends. We therefore propose a new robust algorithm that provides a more refined model of adversary behavior that retains the advantage of efficient computation. We also develop an ensemble method used to tune the algorithm’s parameters, and compare this method’s accuracy in predicting adversary behavior to previous work. We test these contributions in security games against human subjects to show the advantages of our approach.