On the Frequency of Severe Terrorist Events

By Aaron Clauset, Maxwell Young, Kristian Skrede Gleditsch. 2006

389 citations recorded by the source corpus.

In the spirit of Lewis Richardson’s original study of the statistics of deadly conflicts, we study the frequency and severity of terrorist attacks worldwide since 1968. We show that these events are uniformly characterized by the phenomenon of “scale invariance,” that is, the frequency scales as an inverse power of the severity, P(x) Αx-α. We find that this property is a robust feature of terrorism, persisting when we control for economic development of the target country, the type of weapon used, and even for short time scales. Further, we show that the center of the distribution oscillates slightly with a period of roughly τ≈ 13 years, that there exist significant temporal correlations in the frequency of severe events, and that current models of event incidence cannot account for these variations or the scale invariance property of global terrorism. Finally, we describe a simple toy model for the generation of these statistics and briefly discuss its implications.

Subfields: Computational Theory and Mathematics, Modeling and Simulation, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/c039a36fbba6dca2c4f06e10c7c0dd07757b2227

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Citations in the corpus

Saving Human Lives: What Complexity Science and Information Systems can Contribute (2014, neutral): “Topics addressed range from crowd disasters [4] to crime [5,6], terrorism [7–9], wars [10–12], and the epidemic spreading of diseases [13,14].”
Power-law distributions in binned empirical data (2012, methodology): “The approach advocated in Clauset, Shalizi and Newman (2009), originally proposed in Clauset, Young and Gleditsch (2007), is a distance-based method [Reiss and Thomas (2007)] that chooses x min by minimizing the distributional distance between the ﬁtted model and the empirical data above that…”
Power-Law Distributions in Empirical Data (2007, methodology): “(18) When xmin > 1, a similar equation holds, but with the zeta functions replaced by generalized zetas (Bauke, 2007; Clauset et al., 2007): ζ′(α̂, xmin) ζ(α̂, xmin) = − 1 n n ∑ i=1 lnxi, (19) where the prime denotes differentiation with respect to the first argument.”
Spike avalanches in vivo suggest a driven, slightly subcritical brain state (2014, neutral): “…in Systems Neuroscience www.frontiersin.org June 2014 | Volume 8 | Article 108 | 4 f (s) ∼ e− (ln(s)−μ) 2 2σ2 with parameters μ = 0.89 ± 0.25 and variance σ 2 = 1.2 ± 0.1, given a bin size of 1 average inter event interval (<IEI>) (see Clauset et al., 2007; Priesemann et al., 2013 for details).”
Fitting Heavy Tailed Distributions: The poweRlaw Package (2014, positive): “The maximum likelihood estimator (MLE) for the continuous power law is Table 1: Estimating the uncertainty in x min (Clauset, Young, and Gleditsch 2007) 1: Set N equal to the number of values in the original data set.”