Modeling human adversary decision making in security games: an initial report

Authors:
Thanh H. Nguyen;James Pita;Rajiv Maheswaran;Milind Tambe;Amos Azaria;Sarit Kraus
Affiliations:
University of Southern California, Los Angeles, CA, USA;University of Southern California, Los Angeles, USA;University of Southern California, Los Angeles, USA;University of Southern California, Los Angeles, USA;Bar-Ilan University, Ramat Gan, Israel;Bar Ilan University, Ramat Gan, Israel
Venue:
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Year:
2013

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Abstract

Motivated by recent deployments of Stackelberg security games (SSGs), two competing approaches have emerged which either integrate models of human decision making into game-theoretic algorithms 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 human behaviors in solving SSGs remains in question. We study this question in this paper.