Estimating the historical and future probabilities of large terrorist events

TL;DR

This paper introduces a statistical method combining semi-parametric tail modeling and bootstrap techniques to estimate the likelihood of large terrorist events historically and in the future, accounting for data variability.

Contribution

The paper presents a novel statistical algorithm for estimating the probability of extreme social events, integrating tail behavior modeling with bootstrap methods.

Findings

Estimated 11-35% chance of a 9/11-sized event since 1968.

Results are robust across different conditions and data truncations.

Forecasts indicate a significant probability of similar events in the next decade.

Abstract

Quantities with right-skewed distributions are ubiquitous in complex social systems, including political conflict, economics and social networks, and these systems sometimes produce extremely large events. For instance, the 9/11 terrorist events produced nearly 3000 fatalities, nearly six times more than the next largest event. But, was this enormous loss of life statistically unlikely given modern terrorism's historical record? Accurately estimating the probability of such an event is complicated by the large fluctuations in the empirical distribution's upper tail. We present a generic statistical algorithm for making such estimates, which combines semi-parametric models of tail behavior and a nonparametric bootstrap. Applied to a global database of terrorist events, we estimate the worldwide historical probability of observing at least one 9/11-sized or larger event since 1968 to be 11-35%. These results are robust to conditioning on global variations in economic development, domestic versus international events, the type of weapon used and a truncated history that stops at 1998. We then use this procedure to make a data-driven statistical forecast of at least one similar event over the next decade.