Quantifying the future lethality of terror organizations

Yang Yang, Adam R. Pah, Brian Uzzi*

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

As terror groups proliferate and grow in sophistication, a major international concern is the development of scientific methods that explain and predict insurgent violence. Approaches to estimating a group’s future lethality often require data on the group’s capabilities and resources, but by the nature of the phenomenon, these data are intentionally concealed by the organizations themselves via encryption, the dark web, back-channel financing, and misinformation. Here, we present a statistical model for estimating a terror group’s future lethality using latent-variable modeling techniques to infer a group’s intrinsic capabilities and resources for inflicting harm. The analysis introduces 2 explanatory variables that are strong predictors of lethality and raise the overall explained variance when added to existing models. The explanatory variables generate a unique early-warning signal of an individual group’s future lethality based on just a few of its first attacks. Relying on the first 10 to 20 attacks or the first 10 to 20% of a group’s lifetime behavior, our model explains about 60% of the variance in a group’s future lethality as would be explained by a group’s complete lifetime data. The model’s robustness is evaluated with out-of-sample testing and simulations. The findings’ theoretical and pragmatic implications for the science of human conflict are discussed.

Original languageEnglish (US)
Pages (from-to)21463-21468
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume116
Issue number43
DOIs
StatePublished - Oct 22 2019

Keywords

  • Counter-terrorism
  • Human conflict
  • Organizational behavior
  • Statistical models
  • Terrorism

ASJC Scopus subject areas

  • General

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