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Sequential, nonzero-sum "Blotto": Allocating defensive resources prior to attack

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  • Powell, Robert

Abstract

The strategic allocation of resources across multiple fronts has long been studied in the context of Blotto games in which two players simultaneously select their allocations. However many allocation problems are sequential. For example, a state trying to defend against a terrorist attack generally allocates some or all of its resources before the attacker decides where to strike. This paper studies the allocation problem confronting a defender who must decide how to distribute limited resources across multiple sites before an attacker chooses where to strike. Unlike many Blotto games which only have very complicated mixed-strategy equilibria, the sequential, nonzero-sum "Blotto" game always has a very simple pure-strategy subgame perfect equilibrium. Further, the defender always plays the same pure strategy in any equilibrium, and the attacker's equilibrium response is generically unique and entails no mixing. The defender minmaxes the attacker in equilibrium even though the game is nonzero-sum, and the attacker strikes the site among its best replies that minimizes the defender's expected losses.

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  • Powell, Robert, 2009. "Sequential, nonzero-sum "Blotto": Allocating defensive resources prior to attack," Games and Economic Behavior, Elsevier, vol. 67(2), pages 611-615, November.
  • Handle: RePEc:eee:gamebe:v:67:y:2009:i:2:p:611-615
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    Cited by:

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    2. John Duffy & Alexander Matros, 2013. "Stochastic Asymmetric Blotto Games: Theory and Experimental Evidence," Working Paper 509, Department of Economics, University of Pittsburgh, revised Nov 2013.
    3. Duffy, John & Matros, Alexander, 2017. "Stochastic asymmetric Blotto games: An experimental study," Journal of Economic Behavior & Organization, Elsevier, vol. 139(C), pages 88-105.
    4. Bier, Vicki M. & Kosanoglu, Fuat, 2015. "Target-oriented utility theory for modeling the deterrent effects of counterterrorism," Reliability Engineering and System Safety, Elsevier, vol. 136(C), pages 35-46.
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    6. Kjell Hausken, 2014. "Choosing what to protect when attacker resources and asset valuations are uncertain," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 24(3), pages 23-44.
    7. Yang Jiao & Zijun Luo, 2019. "A model of terrorism and counterterrorism with location choices," Public Choice, Springer, vol. 179(3), pages 301-313, June.
    8. Shan, Xiaojun & Zhuang, Jun, 2013. "Hybrid defensive resource allocations in the face of partially strategic attackers in a sequential defender–attacker game," European Journal of Operational Research, Elsevier, vol. 228(1), pages 262-272.
    9. Sanjeev Goyal & Adrien Vigier, 2014. "Attack, Defence, and Contagion in Networks," Review of Economic Studies, Oxford University Press, vol. 81(4), pages 1518-1542.
    10. Hausken, Kjell, 2024. "Fifty Years of Operations Research in Defense," European Journal of Operational Research, Elsevier, vol. 318(2), pages 355-368.
    11. Hunt, Kyle & Zhuang, Jun, 2024. "A review of attacker-defender games: Current state and paths forward," European Journal of Operational Research, Elsevier, vol. 313(2), pages 401-417.
    12. Musegaas, Marieke & Schlicher, Loe & Blok, Herman, 2022. "Stackelberg production-protection games: Defending crop production against intentional attacks," European Journal of Operational Research, Elsevier, vol. 297(1), pages 102-119.
    13. Nakao, Keisuke, 2017. "Denial vs. Punishment: Strategies Shape War, but War Itself Affects Strategies," MPRA Paper 81418, University Library of Munich, Germany.
    14. Yosef Rinott & Marco Scarsini & Yaming Yu, 2012. "A Colonel Blotto Gladiator Game," Mathematics of Operations Research, INFORMS, vol. 37(4), pages 574-590, November.
    15. Garret Ridinger & Richard S. John & Michael McBride & Nicholas Scurich, 2016. "Attacker Deterrence and Perceived Risk in a Stackelberg Security Game," Risk Analysis, John Wiley & Sons, vol. 36(8), pages 1666-1681, August.
    16. Jie Xu & Jun Zhuang, 2016. "Modeling costly learning and counter-learning in a defender-attacker game with private defender information," Annals of Operations Research, Springer, vol. 236(1), pages 271-289, January.
    17. Meir, Reshef & Kalai, Gil & Tennenholtz, Moshe, 2018. "Bidding games and efficient allocations," Games and Economic Behavior, Elsevier, vol. 112(C), pages 166-193.
    18. Wenzel, Lars & Wolf, André, 2013. "Protection against major catastrophes: An economic perspective," HWWI Research Papers 137, Hamburg Institute of International Economics (HWWI).
    19. Antoine Pietri, 2017. "Les modèles de « rivalité coercitive » dans l’analyse économique des conflits," Revue d'économie politique, Dalloz, vol. 127(3), pages 307-352.
    20. Deutsch, Yael, 2021. "A polynomial-time method to compute all Nash equilibria solutions of a general two-person inspection game," European Journal of Operational Research, Elsevier, vol. 288(3), pages 1036-1052.
    21. Scott Macdonell & Nick Mastronardi, 2015. "Waging simple wars: a complete characterization of two-battlefield Blotto equilibria," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 58(1), pages 183-216, January.
    22. Ur Rehman Faiz, 2015. "The Spatial Analysis of Terrorism in Pakistan," Asian Journal of Law and Economics, De Gruyter, vol. 6(2), pages 125-165, October.
    23. Oléron Evans, Thomas P. & Bishop, Steven R., 2013. "Static search games played over graphs and general metric spaces," European Journal of Operational Research, Elsevier, vol. 231(3), pages 667-689.
    24. Kim, Geofferey Jiyun & Kim, Jerim & Kim, Bara, 2018. "A lottery Blotto game with heterogeneous items of asymmetric valuations," Economics Letters, Elsevier, vol. 173(C), pages 1-5.

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    Blotto Minmax Defense Terrorism;

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