A process for anticipating and shaping adversarial behavior

A new approach for anticipating and shaping adversarial behavior is developed and demonstrated. The approach extends the notion of prediction, which is a forecast of the future from a third party point of view, to anticipation, which is a forecast from the perspective of an entity having partial control in a domain. Shaping utilizes the models developed for anticipation to determine actions that influence another influential entity (e.g., an enemy) and actions to direct the emergent phenomena of a domain according to an entity's objectives. The approach is developed using principles of control theory and demonstrated in the southeastern region of Afghanistan. A key capability demonstrated by this approach is its ability to handle proactive adversaries when actionable intelligence is nonexistent. In the demonstration, Taliban (Red) combat actions are anticipated from the perspective of the coalition forces (Blue) across time, across space, and by the current state of the region and then shaped to Blue's desires. Shaping identifies periods of time that simultaneous or alternating Blue combat actions in different regions help meet Blue's military and nonmilitary objectives. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011

[1]  John J. Salerno,et al.  Information fusion for situational awareness , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[2]  Thomas W. Lucas,et al.  Fitting Lanchester equations to the battles of Kursk and Ardennes , 2004 .

[3]  Roberto Szechtman,et al.  Why Defeating Insurgencies Is Hard: The Effect of Intelligence in Counterinsurgency Operations - A Best-Case Scenario , 2009, Oper. Res..

[4]  Walter Enders,et al.  After 9/11 , 2005, Transnational Terrorism.

[5]  Gary King,et al.  Improving Quantitative Studies of International Conflict: A Conjecture , 2000, American Political Science Review.

[6]  Douglas E. Adams,et al.  A control theory based hybrid architecture to anticipate and shape adversarial behavior , 2010 .

[7]  Walter Enders,et al.  The Effectiveness of Antiterrorism Policies: A Vector-Autoregression-Intervention Analysis , 1993, American Political Science Review.

[8]  J. Jelinek,et al.  Model predictive control of military operations , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[9]  Henry A. Kautz,et al.  Learning and inferring transportation routines , 2004, Artif. Intell..

[10]  John R. Freeman,et al.  Vector Autoregression and the Study of Politics , 1989 .

[11]  Davide Pettenuzzo,et al.  Forecasting Time Series Subject to Multiple Structural Breaks , 2004, SSRN Electronic Journal.

[12]  Thomas R. Mockaitis,et al.  Winning hearts and minds in the ‘war on terrorism’ , 2003 .

[13]  Huihui Jiang,et al.  Modeling and control of military operations against adversarial control , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).