Validating game-theoretic models of terrorism: Insights from machine learning

Validating game-theoretic models of terrorism: Insights from machine learning

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 nonlinea...

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Bibliographic Details
Published in:Games Vol. 12; no. 3; pp. 1 - 20
Main Authors: Bang, James T, Basuchoudhary, Atin, Mitra, Aniruddha
Format: Journal Article
Language:English
Published: Basel MDPI 01-09-2021
MDPI AG
Subjects:
Algorithms
Counterterrorism
Econometrics
Estimates
Game theory
Games
Hypotheses
Machine learning
Military aid
Terrorism
Variables
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Summary: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.
ISSN:2073-4336
2073-4336
DOI:10.3390/g12030054