Artikel
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 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.
- Sprache
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Englisch
- Erschienen in
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Journal: Games ; ISSN: 2073-4336 ; Volume: 12 ; Year: 2021 ; Issue: 3 ; Pages: 1-20 ; Basel: MDPI
- Klassifikation
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Wirtschaft
- Thema
-
game theory
machine learning
terrorism
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Bang, James T.
Basuchoudhary, Atin
Mitra, Aniruddha
- Ereignis
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Veröffentlichung
- (wer)
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MDPI
- (wo)
-
Basel
- (wann)
-
2021
- DOI
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doi:10.3390/g12030054
- Handle
- Letzte Aktualisierung
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10.03.2025, 11:43 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Bang, James T.
- Basuchoudhary, Atin
- Mitra, Aniruddha
- MDPI
Entstanden
- 2021
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