A robust approach to addressing human adversaries in security games

While game-theoretic approaches have been proposed for addressing complex security resource allocation problems, many of the standard game-theoretic assumptions fail to address human adversaries who security forces will likely face. To that end, approaches have been proposed that attempt to incorporate better models of human decision-making in these security settings. We take a new approach where instead of trying to create a model of human decision-making, we leverage ideas from robust optimization techniques. In addition, we extend our approach and the previous best performing approach to also address human anchoring biases under limited observation conditions. To evaluate our approach, we perform a comprehensive examination comparing the performance of our new approach against the current leading approaches to addressing human adversaries. Finally, in our experiments we take the first ever analysis of some demographic information and personality measures that may influence decision making in security games.