Abstract
The incidence of terrorism is marked by uncertainty in time of onset, location, severity, and other attributes. This method applies the theory of political uncertainty and complexity theory to assessment of counterterrorism (CT) policies. Results from this method provide new, potentially actionable insights on the effect of CT policies by examining changes in the time between terrorism incidents T and the severity of such events S (fatalities). Most terrorism incidence patterns lack the “normal” (bell-shaped) or Gaussian distribution that is characteristic of equilibrium systems. Instead, terrorism event distributions often show heavy tails, symptomatic of non-equilibrium dynamics, in some cases approximating a power law with critical or near-critical exponent value of 2. Empirical hazard force analysis comparing pre- and postinterventions can also provide insights on CT policy effectiveness and change in the threat environment. Selected policy implications are discussed, including the usefulness of real-time and anticipatory analytical strategies.
Assessing the performance of counterterrorism (CT) policies and interventions poses some special challenges beyond those normally encountered in assessing the impact of other policies. This is primarily because the incidence of terrorism events is marked by significant uncertainty along several dimensions, such as time of onset, location, intensity, and other incident-related attributes. The analysis presented here applies the theory of political uncertainty and complexity theory to assessment of counterterrorism (CT) policies. Results from this approach can provide new and potentially actionable insights on the effect of CT policies by examining changes in the time between terrorism incidents T and the severity of such events S (fatalities). Empirical hazard force analysis of pre- and post-CT interventions can also provide insights on event severity as well as dynamical change. Selected policy implications are discussed, including the usefulness of real-time and anticipatory analytical strategies.
This paper proceeds as follows. The first section provides motivation for the methods presented and a brief discussion of earlier relevant literature. The second sections presents an integrated methodology for data analysis and model testing, based on the theory of political uncertainty and social complexity theory. The essence of these methods is to use terrorist incident data as signals for understanding patterns of occurrence, such as onset and severity, and more importantly, the latent, underlying dynamics that are causally responsible for observed occurrences. Although technically these methods are statistical, mathematical, and computational, they are essentially information extraction methods for understanding terrorist incidence patterns and deriving new metrics for evaluation. The last section presents a discussion of main results available through these methods and some general conclusions, including discussion of evidence-based CT policy evaluation. The discussion of policy implications is innovative for the integrated multidisciplinary methods used in this analysis, which combine political uncertainty theory and complexity modeling or complex systems theory.
Chapter prepared for Cynthia Lum and Leslie Kennedy, eds. 2011. Evidence-Based Counterterrorism Policy.Springer-Verlag.
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References
Allison, G. (2004). Nuclear terrorism: The ultimate preventable catastrophe. New York: Henry Holt and Company.
Bartholomew, D. J. (1973, 1982). Stochastic Models for Social Processes. New York: John Wiley and Sons.
Boccara, N. (2004). Modeling complex systems. New York: Springer.
Cioffi-Revilla, C. A. (1990). The scientific measurement of international conflict: Handbook of datasets on crises and wars, 1495–1988. Boulder, CO: Lynne Rienner.
Cioffi-Revilla, C. (1998). Politics and uncertainty: Theory, models and applications. Cambridge and New York: Cambridge University Press.
Cioffi-Revilla, C. (2003). Power Laws of Conflict: Scaling in Warfare and Terrorism. In Claudio Cioffi-Revilla (Ed.), Power Laws and Non-Equilibrium Distributions in the Social Sciences, Mason Center for Social Complexity, Fairfax, VA 22030. Book manuscript.
Cioffi-Revilla, C., Sean, P., Sean, L., James, L.O., & Jason, T. (2004). Mnemonic Structure and Sociality: A Computational Agent-Based Simulation Model. In David Sallach and Charles Macal (Eds.), Proceedings of the Agent 2004 Conference on Social Dynamics: Interaction, Reflexivity and Emergence, Chicago, IL: Argonne National Laboratory and University of Chicago.
Cioffi-Revilla, C. (2006). Power laws of conflict: Scaling in warfare and terrorism. In C. Cioffi-Revilla (Ed.), Power laws and non-equilibrium dynamics in the social sciences. Unpublished edited volume.
Cioffi-Revilla, C., & O’Brien, S. P. (2007). Computational analysis in US foreign and defense policy. In D. Nau & J. Wilkenfeld (Eds.), Proceedings of the First International Conference on Computational Cultural Dynamics, University of Maryland, College Park, MD, 27–28 August, 2007. Available online.
Cioffi-Revilla, C., & Romero, P. P. (2009). Modeling uncertainty in adversary behavior: Attacks in Diyala Province, Iraq, 2002–2006. Studies in Conflict & Terrorism,32(3), 253–276.
Cox, D. R. (1962). Renewal theory. London: Methuen.
Feller, W. (1968). An Introduction to Probability Theory and its Applications. 3rd ed. New York: John Wiley and Sons.
Greene, W. H. (2011). Econometric analysis (7th ed.). New York: Prentice Hall.
King, G., & Lowe, W. (2003). An automated information extraction tool for international conflict data with performance as good as human coders: A rare events evaluation design. International Organization, 57, 617–642.
Kleiber, C., & Kotz, S. (2003). Statistical size distributions in economics and actuarial sciences. New York: Wiley Inter-Science.
Kline, M. (1985). Mathematics and the search for knowledge. Oxford: Oxford University Press.
LaFree, G., Dugan, L., & Korte, R. (2009). The impact of British counterterrorist strategies on political violence in Northern Ireland: Comparing deterrence and backlash models. Criminology, 47(1), 17–45.
Lewin, K. (1952). Field theory in social science: Selected theoretical papers. Chicago and London: University of Chicago Press.
Mickolus, E. F., Sandler, T., Murdock, J. M., & Flemming, P. (2004). International terrorism: Attributes of terrorist events, 19682003 (ITERATE 5). Dunn Loring, VA: Vineyard Software.
O’Brien, S. P. (2002). Anticipating the good, the bad, and the ugly: An early warning approach to conflict and instability analysis. Journal of Conflict Resolution, 46(6), 808–828.
Richardson, L.F. (1945). The distribution of wars in time. Journal of the Royal Statistical Society (Series A), 107(3–4), 242–250.
Rundle, J. B., Klein, W., Tiampo, K. F., & Gross, S. (2000). Linear pattern dynamics in nonlinear threshold systems. Physical Review E, 61(3), 2418–2431.
Rundle, J. B., Tiampo, K. F., Klein, W., & Sa Martins, J. S. (2002). Self-organization in leaky threshold systems: The influence of near-mean field dynamics and its implications for earthquakes, neurobiology, and forecasting. Proceedings of the National Academy of Sciences of the United States of America, 99(Supplement 1), 2514–2521.
Schrodt, P. A. (1989). Short term prediction of international events using a Holland classifier. Mathematical and Computer Modelling, 12, 589–600.
Singpurwalla, N. D. (2006). Reliability and risk: A Bayesian perspective. New York: Wiley.
START [National Consortium for the Study of Terrorism and Responses to Terrorism]. (2010). Global terrorism database: GTD variables & inclusion criteria. College Park, MD: START Center, University of Maryland. May 2010. Available online.
Townsley, M., Johnson, S. D., & Ratcliffe, J. H. (2008). Space time dynamics of insurgency activity in Iraq. Security Journal, 21, 139–146.
Tsvetovat, M., & Carley, K. (2005). Structural knowledge and success of anti-terrorist activity: The downside of structural equivalence. Journal of Social Structure, 6(2), 23–28.
Vansteenkiste, M., & Sheldon, K. M. (2006). There’s nothing more practical than a good theory: Integrating motivational interviewing and self-determination theory. British Journal of Clinical Psychology, 45(1), 63–82.
Acknowledgements
Thanks to two anonymous reviewers who offered comments and suggestions, and to Pedro Romero for initial testing of these ideas in the context of counterinsurgency analysis. Funding for this study was provided by the Center for Social Complexity of George Mason University and by the Office of Naval Research (ONR) under grant no. N000140810378 (Mason Baseera Project). Opinions, findings, conclusions, and recommendations expressed in this work are those of the author and do not necessarily reflect the views of the funding agencies.
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Cioffi-Revilla, C. (2012). A Complexity Method for Assessing Counterterrorism Policies. In: Lum, C., Kennedy, L. (eds) Evidence-Based Counterterrorism Policy. Springer Series on Evidence-Based Crime Policy, vol 3. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0953-3_7
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