Recent real-world deployments of Stackelberg se-curity games make it critical that we address hu-man adversaries ’ bounded rationality in comput-ing optimal strategies. To that end, this paper provides three key contributions: (i) new efficient algorithms for computing optimal strategic solu-tions using Prospect Theory and Quantal Response Equilibrium; (ii) the most comprehensive experi-ment to date studying the effectiveness of different models against human subjects for security games; and (iii) new techniques for generating representa-tive payoff structures for behavioral experiments in generic classes of games. Our results with human subjects show that our new techniques outperform the leading contender for modeling human behav-ior in security games. 1
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