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On the learning patterns and adaptive behavior of terrorist organizations

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journal contribution
posted on 20.11.2019, 10:10 by JG Jaspersen, Gilberto MontibellerGilberto Montibeller
© 2019 Elsevier B.V. The threat to national security posed by terrorists makes the design of evidence-based counter-terrorism strategies paramount. As terrorist organizations are purposeful entities, it is crucial to understand their decision processes if we want to plan defenses and counter-measures. In particular, there is evidence that terrorist organizations are both adaptive in their behavior and driven by multiple objectives in their actions. In this paper, we use insights from learning theory and compare several different reinforcement learning models regarding their ability to predict terrorist organizations’ actions. Using data on target choices of terrorist attacks and two different objectives (renown and revenge), we show that a total reinforcement learning with power (Luce) choice probabilities and information discounting can be used to model the adaptive behavior of terrorist organizations. The model renders out-of-sample predictions which are comparable in their validity to those observed for learning in laboratory studies. We draw implications for counter-terrorism strategies by comparing the predictive validity of the different models and their calibrated parameters. Our results also offer a starting point for studying the convergence process in game theoretic analyses of conflicts involving terrorists.

History

School

  • Business and Economics

Department

  • Business

Published in

European Journal of Operational Research

Volume

282

Issue

1

Pages

221-234

Publisher

Elsevier

Version

AM (Accepted Manuscript)

Rights holder

© Elsevier

Publisher statement

This paper was accepted for publication in the journal European Journal of Operational Research and the definitive published version is available at https://doi.org/10.1016/j.ejor.2019.09.011

Acceptance date

06/09/2019

Publication date

2019-09-18

Copyright date

2020

ISSN

0377-2217

Language

en

Depositor

Prof Gilberto Montibeller . Deposit date: 15 November 2019