Thanh H. Nguyen,James Pita,

Motivated by recent deployments of Stackelberg security games (SSGs), two competing approaches have emerged which either in-tegrate models of human decision making into game-theoretic algo-rithms or apply robust optimization techniques that avoid adversary modeling. Recently, a robust technique (MATCH) has been shown to significantly outperform the leading modeling-based algorithms (e.g., Quantal Response (QR)) even in the presence of significant amounts of subject data. As a result, the effectiveness of using hu-man behaviors in solving SSGs remains in question. We study this question in this paper