ArticleProceedings of the National Academy of Sciences of the United States of America2019
Quantifying the future lethality of terror organizations.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Quantifying social media predictors of violence during the 6 January US Capitol insurrection using Granger causality.Journal of the Royal Society, Interface · 2024Article
- Article
- Learning future terrorist targets through temporal meta-graphs.Scientific reports · 2021Article
- Undocumented immigration and terrorism: Is there a connection?Social science research · 2021Article
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Authors and funding
3 authors.
Funding
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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.
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