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Peace Economics, Peace Science and Public Policy

Editor-in-Chief: Caruso, Raul

Ed. by Bove, Vincenzo / Kibris, Arzu / Sekeris, Petros

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Predicting Terrorism with Machine Learning: Lessons from “Predicting Terrorism: A Machine Learning Approach”

Atin Basuchoudhary / James T. Bang
Published Online: 2018-11-29 | DOI: https://doi.org/10.1515/peps-2018-0040


This paper highlights how machine learning can help explain terrorism. We note that even though machine learning has a reputation for black box prediction, in fact, it can provide deeply nuanced explanations of terrorism. Moreover, machine learning is not sensitive to the sometimes heroic statistical assumptions necessary when parametric econometrics is applied to the study of terrorism. This increases the reliability of explanations while adding contextual nuance that captures the flavor of individualized case analysis. Nevertheless, this approach also gives us a sense of the replicability of results. We, therefore, suggest that it further expands the role of science in terrorism research.

Keywords: predicting terrorism; terrorism risk; Machine Learning; Artificial Intelligence; predictive analytics


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About the article

Published Online: 2018-11-29

Citation Information: Peace Economics, Peace Science and Public Policy, 20180040, ISSN (Online) 1554-8597, DOI: https://doi.org/10.1515/peps-2018-0040.

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