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Zero-sum stochastic games with the average-value-at-risk criterion

Author

Listed:
  • Qiuli Liu

    (South China Normal University)

  • Wai-Ki Ching

    (The University of Hong Kong)

  • Xianping Guo

    (Sun Yat-Sen University)

Abstract

This paper introduces an average-value-at-risk (AVaR) criterion for discrete-time zero-sum stochastic games with varying discount factors. The state space is a Borel space, the action space is denumerable, and the payoff function is allowed to be unbounded. We first transform the AVaR game problem into a bi-level optimization-game problem in which the outer optimization problem is a problem of minimizing a function of a single variable and the inner game problem has been shown to be equivalent to a so-called expected-discounted-positive-deviation (EDPD) game for discrete-time stochastic game. We solve the EDPD game problem in advance. More precisely, under suitable conditions, we not only establish the Shapley equation, the existence of the value of the game, and saddle points, but also prove that the saddle points can be computed by introducing a primal linear program and a dual linear program. Then, we show that the outer problem can be settled by solving the EDPD game problem. Furthermore, we provide an algorithm for computing (or at least approximating) the value of the game and the saddle points for the AVaR game problem. Finally, as an application, we apply our main results to an inventory-production system with numerical experiments.

Suggested Citation

  • Qiuli Liu & Wai-Ki Ching & Xianping Guo, 2023. "Zero-sum stochastic games with the average-value-at-risk criterion," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(3), pages 618-647, October.
    • Handle: RePEc:spr:topjnl:v:31:y:2023:i:3:d:10.1007_s11750-023-00655-7
    DOI: 10.1007/s11750-023-00655-7
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    References listed on IDEAS

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    1. Basu, Arnab & Ghosh, Mrinal Kanti, 2014. "Zero-sum risk-sensitive stochastic games on a countable state space," Stochastic Processes and their Applications, Elsevier, vol. 124(1), pages 961-983.
    2. Nicole Bäuerle & Jonathan Ott, 2011. "Markov Decision Processes with Average-Value-at-Risk criteria," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 74(3), pages 361-379, December.
    3. Wenzhao Zhang & Yonghui Huang & Xianping Guo, 2014. "Nonzero-sum constrained discrete-time Markov games: the case of unbounded costs," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 1074-1102, October.
    4. Kang Boda & Jerzy Filar, 2006. "Time Consistent Dynamic Risk Measures," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 63(1), pages 169-186, February.
    5. Bäuerle, Nicole & Rieder, Ulrich, 2017. "Zero-sum risk-sensitive stochastic games," Stochastic Processes and their Applications, Elsevier, vol. 127(2), pages 622-642.
    6. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
    7. Arnab Basu & Mrinal K. Ghosh, 2012. "Zero-Sum Risk-Sensitive Stochastic Differential Games," Mathematics of Operations Research, INFORMS, vol. 37(3), pages 437-449, August.
    8. Christopher W. Miller & Insoon Yang, 2015. "Optimal Control of Conditional Value-at-Risk in Continuous Time," Papers 1512.05015, arXiv.org, revised Jan 2017.
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