A Complexity Method for Assessing Counterterrorism Policies
- Claudio Cioffi-RevillaAffiliated withCenter for Social Complexity and Department of Computational Social Science, Krasnow Institute for Advanced Study, George Mason University Email author
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.
- A Complexity Method for Assessing Counterterrorism Policies
- Book Title
- Evidence-Based Counterterrorism Policy
- pp 151-165
- Print ISBN
- Online ISBN
- Series Title
- Springer Series on Evidence-Based Crime Policy
- Series Volume
- Springer New York
- Copyright Holder
- Springer Science+Business Media, LLC
- Additional Links
- eBook Packages
- Editor Affiliations
- ID1. Center for Evidence- Based Crime Policy, Criminology, Law, and Society, George Mason University
- ID2. School of Criminal Justice, Rutgers University
- Author Affiliations
- 1. Center for Social Complexity and Department of Computational Social Science, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
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