A Survey of Quantitative Models of Terror Group Behavior and an Analysis of Strategic Disclosure of Behavioral Models

There are many applications (e.g., counter-terrorism) where we can automatically learn a quantitative model from realworld data about terror group behavior. In this paper, we first provide a survey of quantitative models of terrorist groups. To date, however, the best-known quantitative models of terror group behavior are based on various types of quantitative logic programs. After our survey, we address an important question posed to us by Nobel laureate, Tom Schelling. Once a set of quantitative logic behavior rules about an adversary has been learned, should these rules be disclosed or not? We develop a game theoretic framework in order to answer this question with a defender who has to decide what rules to release publicly and which ones to keep hidden. We first study the attacker's optimal attack strategy, given a set of disclosed rules, and then we study the problem of which rules to disclose so that the attacker's optimal strategy has minimal effectiveness. We study the complexity of both problems, present algorithms to solve both, and then present a (1-1/e )-approximation algorithm that (under some restrictions) uses a submodularity property to compute the optimal defender strategy. Finally, we provide experimental results showing that our framework works well in practice-these results are also shown to be statistically significant.