Adversarial Behavior in Complex Adaptive Systems: An Overview of ICST’s Research on Competitive Adaptation in Militant Networks

30 Pages Posted: 19 Jul 2010 Last revised: 8 Sep 2010

See all articles by John Horgan

John Horgan

International Center for the Study of Terrorism

Michael J. Kenney

affiliation not provided to SSRN

Kathleen M. Carley

Carnegie Mellon University; Carnegie Mellon University - H. John Heinz III School of Public Policy and Management; Institute for Software Research - Carnegie Mellon University

Mia M. Bloom

affiliation not provided to SSRN

Cale D. Horne

University of Georgia - School of Public and International Affairs

Kurt Braddock

Pennsylvania State University

Peter Vining

affiliation not provided to SSRN

Nicole Zinni

Pennsylvania State University

Date Written: 2010

Abstract

There is widespread agreement among scholars and practitioners that the counterterrorism literature suffers from a lack of primary-source field research. The absence of solid ethnographic research has yielded studies that suffer from a lack of rigorous analysis and often result in opinion masquerading as analysis. The lack of field research is also due to a failure to integrate ethnographic research into modeling efforts, as well as a failure more broadly to appreciate the significance of ethnographically valid data in human, social, cultural, and behavioral studies in a systematic investigation of terrorist behavior. The project briefly outlined in this paper seeks to redress this deficiency by combining the strengths of ethnographic field research (collected by social scientists at Penn State) with the sophisticated modeling capabilities of computer scientists (at Carnegie Mellon University). Specifically, we are analyzing data from interview transcripts, news reports, and other open sources concerning the militant activist group Al-Muhajiroun and the terrorist groups Provisional Irish Republican Army (PIRA) and Revolutionary Armed Forces of Colombia (FARC). Using competitive adaptation as a comparative organizational framework, this project focuses on the process by which adversaries learn from each other in complex adaptive systems and tailor their activities to achieve their organizational goals in light of their opponents‟ actions. Ultimately, we will develop a meso-level model of militant networks that combines insights from political science, organizational theory, psychology, network science, and computational modeling.

Keywords: terrorism, Al-Muhajiroun, competitive adaptation, learning

Suggested Citation

Horgan, John and Kenney, Michael J. and Carley, Kathleen M. and Bloom, Mia M. and Horne, Cale D. and Braddock, Kurt and Vining, Peter and Zinni, Nicole, Adversarial Behavior in Complex Adaptive Systems: An Overview of ICST’s Research on Competitive Adaptation in Militant Networks (2010). APSA 2010 Annual Meeting Paper. Available at SSRN: https://ssrn.com/abstract=1642196

John Horgan

International Center for the Study of Terrorism ( email )

University Park
State College, PA 16802
United States

Michael J. Kenney

affiliation not provided to SSRN ( email )

Kathleen M. Carley

Carnegie Mellon University ( email )

Pittsburgh, PA 15213-3890
United States
412-268-6016 (Phone)

Carnegie Mellon University - H. John Heinz III School of Public Policy and Management ( email )

Pittsburgh, PA 15213-3890
United States

Institute for Software Research - Carnegie Mellon University ( email )

School of Computer Science
5000 Forbes Avenue
Pittsburgh, PA 15213
United States

HOME PAGE: http://isri.cs.cmu.edu/

Mia M. Bloom

affiliation not provided to SSRN ( email )

No Address Available

Cale D. Horne

University of Georgia - School of Public and International Affairs ( email )

Athens, GA 30602-6254
United States

Kurt Braddock

Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

Peter Vining (Contact Author)

affiliation not provided to SSRN ( email )

No Address Available

Nicole Zinni

Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

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