COMPUTATIONAL SOCIAL SCIENCE

CSS SEMINAR - APRIL 30

Deriving Optimal Blends of History and Intelligence for Robust Counterterrorism Policy

Maciek Latek
Seyed Mohammad Mussavi Rizi
PhD Candidates
Department of Computational Social Science
George Mason University

Counter-terrorism analysis requires that homeland security organizations determine a suitable blend of evidence on the historical patterns of terrorist behavior with current, and incomplete intelligence on terrorist adversaries in order to predict possible terrorist operations and devise appropriate countermeasures.  This paper models interactions between adaptive and intelligent adversaries embedded in minimally sufficient organizational settings to examine the issue of optimal analytic mixture that should be used to design counterterrorism strategy, expressed as historical memory reach-back and the number of anticipatory scenarios. We show that history is a valuable source of information when the terrorist organization evolves and acquires new capabilities at such a rapid pace that makes optimal strategies put forth by game-theoretic reasoning unlikely to succeed.