March 2024 A behavioral approach to repeated Bayesian security games
William Caballero, Jake Cooley, David Banks, Phillip Jenkins
Author Affiliations +
Ann. Appl. Stat. 18(1): 199-223 (March 2024). DOI: 10.1214/23-AOAS1786


The prevalence of security threats to organizational defense demands models that support real-world policymaking. Security games are a potent tool in this regard; however, although canonical models effectively allocate limited resources, they generally do not consider adaptive, boundedly rational adversaries. Empirical findings suggest this characterization describes real-world human behavior, so the development of decision-support frameworks against such adversaries is a critical need. We examine a family of policies applicable to repeated games in which a boundedly rational adversary is modeled using a behavioral-economic theory of learning, that is, experience-weighted attraction learning. These policies take into account realistic uncertainty about the competition by adopting the perspective of adversarial risk analysis. Using Bayesian reasoning, these repeated games are decomposed into multiarm bandit problems. A collection of cost-function approximation policies are given to solve these problems. The efficacy of our approach is shown via extensive computational testing on a defense-related case study.

Funding Statement

This research is partially supported by the Air Force Office of Scientific Research (AFOSR) under the Dynamic Data and Information Processing (DDIP) portfolio and by NSF Grant DMS-1638521.


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William Caballero. Jake Cooley. David Banks. Phillip Jenkins. "A behavioral approach to repeated Bayesian security games." Ann. Appl. Stat. 18 (1) 199 - 223, March 2024.


Received: 1 March 2022; Revised: 1 April 2023; Published: March 2024
First available in Project Euclid: 31 January 2024

Digital Object Identifier: 10.1214/23-AOAS1786

Keywords: Adversarial risk analysis , Behavioral game theory , multiarm bandits , repeated games

Rights: Copyright © 2024 Institute of Mathematical Statistics


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Vol.18 • No. 1 • March 2024
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