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Research Article

Quantifying the future lethality of terror organizations

Yang Yang, Adam R. Pah, and Brian Uzzi
PNAS October 22, 2019 116 (43) 21463-21468; first published October 7, 2019 https://doi.org/10.1073/pnas.1901975116
Yang Yang
aNorthwestern Institute on Complex Systems, Northwestern University, Evanston, IL 60208;bKellogg School of Management, Northwestern University, Evanston, IL 60208
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Adam R. Pah
aNorthwestern Institute on Complex Systems, Northwestern University, Evanston, IL 60208;bKellogg School of Management, Northwestern University, Evanston, IL 60208
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Brian Uzzi
aNorthwestern Institute on Complex Systems, Northwestern University, Evanston, IL 60208;bKellogg School of Management, Northwestern University, Evanston, IL 60208
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  • For correspondence: uzzi@kellogg.northwestern.edu
  1. Edited by Arild Underdal, University of Oslo, Oslo, Norway, and approved September 4, 2019 (received for review February 5, 2019)

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Significance

We develop and test a model that predicts the future lethality of a terror group. The model significantly increases the level of explained variance over existing models and presents early-warning signal predictions. Our early-warning model predicts the future lethality of a group using only a handful of events that occur soon after it emerges. Using the first 10 to 20 attacks or the first 10 to 20% of a group’s lifetime, our early-warning model provides about 60% of the explanatory power as would having a group’s complete lifetime data, which is a basis for improved counter insurgency resources.

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.

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

Footnotes

  • ↵1Y.Y. and A.R.P. contributed equally to this work.

  • ↵2To whom correspondence may be addressed. Email: uzzi{at}kellogg.northwestern.edu.
  • Author contributions: Y.Y., A.R.P., and B.U. designed research; Y.Y., A.R.P., and B.U. performed research; Y.Y. and A.R.P. analyzed data; and Y.Y., A.R.P., and B.U. wrote the paper.

  • The authors declare no competing interest.

  • This article is a PNAS Direct Submission.

  • This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1901975116/-/DCSupplemental.

Published under the PNAS license.

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Quantifying the future lethality of terror organizations
Yang Yang, Adam R. Pah, Brian Uzzi
Proceedings of the National Academy of Sciences Oct 2019, 116 (43) 21463-21468; DOI: 10.1073/pnas.1901975116

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Quantifying the future lethality of terror organizations
Yang Yang, Adam R. Pah, Brian Uzzi
Proceedings of the National Academy of Sciences Oct 2019, 116 (43) 21463-21468; DOI: 10.1073/pnas.1901975116
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Proceedings of the National Academy of Sciences: 116 (43)
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