Optimal Blends of History and Intelligence for Robust Antiterrorism Policy

Antiterrorism analysis requires that security agencies blend evidence on historical patterns of terrorist behavior with incomplete intelligence on terrorist adversaries to predict possible terrorist operations and devise appropriate countermeasures. We model interactions between reactive, adaptive and intelligent adversaries embedded in minimally sufficient organizational settings to study the optimal analytic mixture, expressed as historical memory reach-back and the number of anticipatory scenarios, that should be used to design antiterrorism policy. 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 advocated by game-theoretic reasoning unlikely to succeed.

[1]  Robert Powell,et al.  Allocating Defensive Resources with Private Information about Vulnerability , 2007, American Political Science Review.

[2]  Paul W. Goldberg,et al.  The complexity of computing a Nash equilibrium , 2006, STOC '06.

[3]  David R. Frelinger,et al.  Understanding Why Terrorist Operations Succeed or Fail , 2009 .

[4]  J. Rosenhead,et al.  Robustness and Optimality as Criteria for Strategic Decisions , 1972 .

[5]  Oguzhan Alagöz,et al.  Modeling secrecy and deception in a multiple-period attacker-defender signaling game , 2010, Eur. J. Oper. Res..

[6]  Todd Sandler,et al.  Games and Terrorism , 2009 .

[7]  Anand S. Rao,et al.  Modeling Rational Agents within a BDI-Architecture , 1997, KR.

[8]  Robert L. Axtell,et al.  WHY AGENTS? ON THE VARIED MOTIVATIONS FOR AGENT COMPUTING IN THE SOCIAL SCIENCES , 2000 .

[9]  Maciej Latek,et al.  Dynamics of Agent Organizations: Application to Modeling Irregular Warfare , 2009, MABS.

[10]  Todd Sandler,et al.  Games and Terrorism Recent Developments , 2008 .

[11]  Edmund H. Durfee,et al.  Recursive Agent Modeling Using Limited Rationality , 1995, ICMAS.

[12]  Milind Tambe,et al.  Security and Game Theory: IRIS – A Tool for Strategic Security Allocation in Transportation Networks , 2011, AAMAS 2011.

[13]  H. Van Dyke Parunak,et al.  Stigmergic Modeling of Hierarchical Task Networks , 2009, MABS.

[14]  Charles R. McLean,et al.  Homeland security simulation domain: a needs analysis overview , 2008 .

[15]  Sanguk Noh,et al.  Bayesian Update of Recursive Agent Models , 2004, User Modeling and User-Adapted Interaction.

[16]  Erik Jenelius,et al.  Critical infrastructure protection under imperfect attacker perception , 2010, Int. J. Crit. Infrastructure Prot..

[17]  Michael P. Wellman,et al.  Learning about other agents in a dynamic multiagent system , 2001, Cognitive Systems Research.

[18]  Brian Fishman,et al.  Dysfunction and Decline: Lessons Learned from Inside Al-Qa'ida in Iraq , 2009 .

[19]  Mark P. Taylor,et al.  The use of technical analysis in the foreign exchange market , 1992 .

[20]  Jun Zhuang,et al.  Robustness of Optimal Defensive Resource Allocations in the Face of Less Fully Rational Attacker , 2009 .

[21]  J. B. Jr. Gilmer,et al.  The use of recursive simulation to support decisionmaking , 2003, Proceedings of the 2003 Winter Simulation Conference, 2003..

[22]  Austin Tate,et al.  Generating Project Networks , 1977, IJCAI.

[23]  Milind Tambe,et al.  Strategic Security Placement in Network Domains with Applications to Transit Security , 2009 .

[24]  Edmund H. Durfee,et al.  Predicting the Expected Behavior of Agents that Learn About Agents: The CLRI Framework , 2004, Autonomous Agents and Multi-Agent Systems.

[25]  V. Crawford,et al.  Learning How to Cooperate: Optimal Play in Repeated Coordination Games , 1990 .

[26]  John B. Gilmer,et al.  Issues in event analysis for recursive simulation , 2005, Proceedings of the Winter Simulation Conference, 2005..

[27]  Kathleen M. Carley,et al.  Construct - A Multi-Agent Network Model for the Co-Evolution of Agents and Socio-Cultural Environments , 2004 .