Citation

Yang Y, Pah AR, Uzzi B. Proc. Natl. Acad. Sci. U. S. A. 2019; ePub(ePub): ePub.

Affiliation

Kellogg School of Management, Northwestern University, Evanston, IL 60208.

Copyright

(Copyright © 2019, National Academy of Sciences)

DOI

10.1073/pnas.1901975116

PMID

31591241

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.

Language: en

Keywords

counter-terrorism; human conflict; organizational behavior; statistical models; terrorism