Empirical Comparisons of Descriptive Multi-objective Adversary Models in Stackelberg Security Games
Stackelberg Security Games (SSG) have been used to model defender- attacker relationships for analyzing real-world security resource allocation problems. Research has focused on generating algorithms that are optimal and efficient for defenders, based on a presumed model of adversary choices. However, relatively less has been done descriptively to investigate how well those models capture adversary choices and psychological assumptions about adversary decision making. Using data from three experiments, including over 1000 human subjects playing over 25000 games, this study evaluates adversary choices by comparing 9 adversary models both nomothetically and ideographically in a SSG setting. We found that participants tended to be consistent with utility maximization and avoid a target with high probability of being protected even if the reward or expected value of that target is high. It was also found in two experiments that adversary choices were dependent on the defender’s payoffs, even after accounting for attacker’s own payoffs.
Keywordsadversary modeling Stackelberg Security Game utility function
Unable to display preview. Download preview PDF.
- 1.Pita, J., John, R., Maheswaran, R., Tambe, M., Yang, R., Kraus, S.: A robust approach to addressing human adversaries in security games. In: Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems, vol. 3 (2012)Google Scholar
- 3.Nguyen, T.H., Yang, R., Azaria, A., Kraus, S., Tambe, M.: Analyzing the effectiveness of adversary modeling in security games. In: Conference on Artificial Intelligence (2013)Google Scholar
- 5.McFadden, D.L.: Quantal choice analaysis: A survey. Annals of Economic and Social Measurement 5(4), 363–390 (1976)Google Scholar
- 6.McFadden, D.: Economic choices. American Economic Review, 351–378 (2001)Google Scholar
- 12.Brunswik, E.: The conceptual framework of psychology, vol. 1. University of Chicago Press (1952)Google Scholar
- 14.Keeney, R.L., Raiffa, H.: Decisions with multiple objectives: Preferences and value trade-offs (1976)Google Scholar
- 15.Scholz, F.: Maximum likelihood estimation. Encyclopedia of Statistical Sciences (1985)Google Scholar
- 17.Burnham, K.P., Anderson, D.R.: Model selection and multimodel inference: A practical information-theoretic approach. Springer (2002)Google Scholar