Divide to defend: Collusive security games

Abstract

Research on security games has focused on settings where the defender must protect against either a single adversary or multiple, independent adversaries. However, there are a variety of real-world security domains where adversaries may benefit from colluding in their actions against the defender, e.g., wildlife poaching, urban crime and drug trafficking. Given such adversary collusion may be more detrimental for the defender, she has an incentive to break up collusion by playing off the self-interest of individual adversaries. As we show in this paper, breaking up such collusion is difficult given bounded rationality of human adversaries; we therefore investigate algorithms for the defender assuming both rational and boundedly rational adversaries. The contributions of this paper include (i) collusive security games (COSGs), a model for security games involving potential collusion among adversaries, (ii) SPECTRE-R, an algorithm to solve COSGs and break collusion assuming rational adversaries, (iii) observations and analyses of adversary behavior and the underlying factors including bounded rationality, imbalanced- resource-allocation effect, coverage perception, and individualism/collectivism attitudes within COSGs with data from 700 human subjects, (iv) a learned human behavioral model that incorporates these factors to predict when collusion will occur, (v) SPECTREBR, an enhanced algorithm which optimizes against the learned behavior model to provide demonstrably better performing defender strategies against human subjects compared to SPECTRE-R.