Quantifying the future lethality of terror organizations

Yang Yang (), Adam R. Pah and Brian Uzzi ()
Additional contact information
Yang Yang: Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL 60208; Kellogg School of Management, Northwestern University, Evanston, IL 60208
Adam R. Pah: Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL 60208; Kellogg School of Management, Northwestern University, Evanston, IL 60208
Brian Uzzi: Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL 60208; Kellogg School of Management, Northwestern University, Evanston, IL 60208

Proceedings of the National Academy of Sciences, 2019, vol. 116, issue 43, 21463-21468

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.

Keywords: terrorism; counter-terrorism; human conflict; organizational behavior; statistical models (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.pnas.org/content/116/43/21463.full (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nas:journl:v:116:y:2019:p:21463-21468

Access Statistics for this article

More articles in Proceedings of the National Academy of Sciences from Proceedings of the National Academy of Sciences
Bibliographic data for series maintained by PNAS Product Team ().