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Validating Game-Theoretic Models of Terrorism: Insights from Machine Learning

1
Department of Economics, St. Ambrose University, Davenport, IA 52803, USA
2
Department of Economics and Business, Virginia Military Institute, Lexington, VA 24450, USA
3
Economics Program, Bard College, Annandale-On-Hudson, NY 12504, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Joao Ricardo Faria, Daniel Arce and Ulrich Berger
Received: 28 April 2021 / Revised: 31 May 2021 / Accepted: 23 June 2021 / Published: 30 June 2021
(This article belongs to the Special Issue Economics of Conflict and Terrorism)
There are many competing game-theoretic analyses of terrorism. Most of these models suggest nonlinear relationships between terror attacks and some variable of interest. However, to date, there have been very few attempts to empirically sift between competing models of terrorism or identify nonlinear patterns. We suggest that machine learning can be an effective way of undertaking both. This feature can help build more salient game-theoretic models to help us understand and prevent terrorism. View Full-Text
Keywords: machine learning; terrorism; game theory machine learning; terrorism; game theory
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MDPI and ACS Style

Bang, J.T.; Basuchoudhary, A.; Mitra, A. Validating Game-Theoretic Models of Terrorism: Insights from Machine Learning. Games 2021, 12, 54. https://0-doi-org.brum.beds.ac.uk/10.3390/g12030054

AMA Style

Bang JT, Basuchoudhary A, Mitra A. Validating Game-Theoretic Models of Terrorism: Insights from Machine Learning. Games. 2021; 12(3):54. https://0-doi-org.brum.beds.ac.uk/10.3390/g12030054

Chicago/Turabian Style

Bang, James T., Atin Basuchoudhary, and Aniruddha Mitra. 2021. "Validating Game-Theoretic Models of Terrorism: Insights from Machine Learning" Games 12, no. 3: 54. https://0-doi-org.brum.beds.ac.uk/10.3390/g12030054

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