Predicting Terrorism with Machine Learning: Lessons from “Predicting Terrorism: A Machine Learning Approach”
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Atin Basuchoudhary
and
James T. Bang
Abstract
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.
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Article Note
Predicting terrorism: a machine learning approach was presented at the 19th Jan Tinbergen European Peace Science Conference (2018), Verona.
©2018 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Editorial
- Introduction to the Proceedings of the 18th Jan Tinbergen European Peace Science Conference
- Survey or Review
- Systematic Study of Gender, Conflict, and Peace
- Letters and Proceedings
- Political Initiatives and Peacekeeping: Assessing Multiple UN Conflict Resolution Tools
- US Military Response to the Risk of Terrorist Attacks
- Do Foreign Aid Projects Attract Transnational Terrorism?
- Military Spending and Inequality in Autocracies
- Beyond a Bag of Words: Using PULSAR to Extract Judgments on Specific Human Rights at Scale
- Predicting Terrorism with Machine Learning: Lessons from “Predicting Terrorism: A Machine Learning Approach”
- Conflict in Cyber-Space: The Network of Cyber Incidents, 2000–2014
- What do they Want? Rebels’ Objectives and Civil War Mediation
Articles in the same Issue
- Editorial
- Introduction to the Proceedings of the 18th Jan Tinbergen European Peace Science Conference
- Survey or Review
- Systematic Study of Gender, Conflict, and Peace
- Letters and Proceedings
- Political Initiatives and Peacekeeping: Assessing Multiple UN Conflict Resolution Tools
- US Military Response to the Risk of Terrorist Attacks
- Do Foreign Aid Projects Attract Transnational Terrorism?
- Military Spending and Inequality in Autocracies
- Beyond a Bag of Words: Using PULSAR to Extract Judgments on Specific Human Rights at Scale
- Predicting Terrorism with Machine Learning: Lessons from “Predicting Terrorism: A Machine Learning Approach”
- Conflict in Cyber-Space: The Network of Cyber Incidents, 2000–2014
- What do they Want? Rebels’ Objectives and Civil War Mediation
