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

Author

Listed:
  • James T. Bang

    (Department of Economics, St. Ambrose University, Davenport, IA 52803, USA)

  • Atin Basuchoudhary

    (Department of Economics and Business, Virginia Military Institute, Lexington, VA 24450, USA)

  • Aniruddha Mitra

    (Economics Program, Bard College, Annandale-On-Hudson, NY 12504, USA)

Abstract

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.

Suggested Citation

  • James T. Bang & Atin Basuchoudhary & Aniruddha Mitra, 2021. "Validating Game-Theoretic Models of Terrorism: Insights from Machine Learning," Games, MDPI, vol. 12(3), pages 1-20, June.
    • Handle: RePEc:gam:jgames:v:12:y:2021:i:3:p:54-:d:585666
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    References listed on IDEAS

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    Cited by:

    1. Atin Basuchoudhary, 2021. "Why Is Civil Conflict Path Dependent? A Cultural Explanation," Games, MDPI, vol. 12(4), pages 1-12, December.
    2. João Ricardo Faria & Daniel Arce, 2022. "A Preface for the Special Issue “Economics of Conflict and Terrorism”," Games, MDPI, vol. 13(2), pages 1-2, April.

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