A Complexity Method for Assessing Counterterrorism Policies

  • Claudio Cioffi-Revilla
Part of the Springer Series on Evidence-Based Crime Policy book series (SSEBCP, volume 3)


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.


Threat Environment Terrorism Event Political Uncertainty High Probability Density Complex System Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Center for Social Complexity and Department of Computational Social Science, Krasnow Institute for Advanced StudyGeorge Mason UniversityFairfaxUSA

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