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Deterrence and Risk Preferences in Sequential Attacker–Defender Games with Continuous Efforts

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  • Vineet M. Payyappalli
  • Jun Zhuang
  • Victor Richmond R. Jose

Abstract

Most attacker–defender games consider players as risk neutral, whereas in reality attackers and defenders may be risk seeking or risk averse. This article studies the impact of players' risk preferences on their equilibrium behavior and its effect on the notion of deterrence. In particular, we study the effects of risk preferences in a single‐period, sequential game where a defender has a continuous range of investment levels that could be strategically chosen to potentially deter an attack. This article presents analytic results related to the effect of attacker and defender risk preferences on the optimal defense effort level and their impact on the deterrence level. Numerical illustrations and some discussion of the effect of risk preferences on deterrence and the utility of using such a model are provided, as well as sensitivity analysis of continuous attack investment levels and uncertainty in the defender's beliefs about the attacker's risk preference. A key contribution of this article is the identification of specific scenarios in which the defender using a model that takes into account risk preferences would be better off than a defender using a traditional risk‐neutral model. This study provides insights that could be used by policy analysts and decisionmakers involved in investment decisions in security and safety.

Suggested Citation

  • Vineet M. Payyappalli & Jun Zhuang & Victor Richmond R. Jose, 2017. "Deterrence and Risk Preferences in Sequential Attacker–Defender Games with Continuous Efforts," Risk Analysis, John Wiley & Sons, vol. 37(11), pages 2229-2245, November.
    • Handle: RePEc:wly:riskan:v:37:y:2017:i:11:p:2229-2245
    DOI: 10.1111/risa.12768
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    References listed on IDEAS

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    Cited by:

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    2. Zhang, Xiaoxiong & Ye, Yanqing & Tan, Yuejin, 2020. "How to protect a genuine target against an attacker trying to detect false targets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    3. Lin, Chen & Xiao, Hui & Kou, Gang & Peng, Rui, 2020. "Defending a series system with individual protection, overarching protection, and disinformation," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    4. Zhiheng Xu & Jun Zhuang, 2019. "A Study on a Sequential One‐Defender‐N‐Attacker Game," Risk Analysis, John Wiley & Sons, vol. 39(6), pages 1414-1432, June.
    5. Li, Xiang & Qi, Xiangtong, 2021. "On pricing and quality decisions with risk aversion," Omega, Elsevier, vol. 98(C).
    6. Li, Yapeng & Qiao, Shun & Deng, Ye & Wu, Jun, 2019. "Stackelberg game in critical infrastructures from a network science perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 705-714.
    7. Qingqing Zhai & Rui Peng & Jun Zhuang, 2020. "Defender–Attacker Games with Asymmetric Player Utilities," Risk Analysis, John Wiley & Sons, vol. 40(2), pages 408-420, February.
    8. Peng, Rui & Xiao, Hui & Guo, Jianjun & Lin, Chen, 2020. "Defending a parallel system against a strategic attacker with redundancy, protection and disinformation," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    9. Hunt, Kyle & Agarwal, Puneet & Zhuang, Jun, 2022. "On the adoption of new technology to enhance counterterrorism measures: An attacker–defender game with risk preferences," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    10. Hunt, Kyle & Agarwal, Puneet & Zhuang, Jun, 2021. "Technology adoption for airport security: Modeling public disclosure and secrecy in an attacker-defender game," Reliability Engineering and System Safety, Elsevier, vol. 207(C).

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