A generalized aggregation-disintegration model for the frequency of severe terrorist attacks

TL;DR

This paper introduces a generalized model based on self-organized criticality to predict the distribution of severe terrorist attack frequencies, aligning well with empirical data and offering insights into terrorist organization dynamics.

Contribution

The paper extends existing models by incorporating a generalized aggregation-disintegration framework that predicts power-law distributions of attack severities, validated against empirical data.

Findings

Predicted attack severity distribution follows a power-law with exponent 2.5.

Empirical data supports the model's prediction of an exponent around 2.4.

Model's predictions are robust to certain model details.

Abstract

We present and analyze a model of the frequency of severe terrorist attacks, which generalizes the recently proposed model of Johnson et al. This model, which is based on the notion of self-organized criticality and which describes how terrorist cells might aggregate and disintegrate over time, predicts that the distribution of attack severities should follow a power-law form with an exponent of alpha=5/2. This prediction is in good agreement with current empirical estimates for terrorist attacks worldwide, which give alpha=2.4 \pm 0.2, and which we show is independent of certain details of the model. We close by discussing the utility of this model for understanding terrorism and the behavior of terrorist organizations, and mention several productive ways it could be extended mathematically or tested empirically.