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Stochastic linear programming games with concave preferences

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  • Uhan, Nelson A.

Abstract

We study stochastic linear programming games: a class of stochastic cooperative games whose payoffs under any realization of uncertainty are determined by a specially structured linear program. These games can model a variety of settings, including inventory centralization and cooperative network fortification. We focus on the core of these games under an allocation scheme that determines how payoffs are distributed before the uncertainty is realized, and allows for arbitrarily different distributions for each realization of the uncertainty. Assuming that each player’s preferences over random payoffs are represented by a concave monetary utility functional, we prove that these games have a nonempty core. Furthermore, by establishing a connection between stochastic linear programming games, linear programming games and linear semi-infinite programming games, we show that an allocation in the core can be computed efficiently under some circumstances.

Suggested Citation

  • Uhan, Nelson A., 2015. "Stochastic linear programming games with concave preferences," European Journal of Operational Research, Elsevier, vol. 243(2), pages 637-646.
  • Handle: RePEc:eee:ejores:v:243:y:2015:i:2:p:637-646
    DOI: 10.1016/j.ejor.2014.12.025
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    Cited by:

    1. M. A. Goberna & M. A. López, 2017. "Recent contributions to linear semi-infinite optimization," 4OR, Springer, vol. 15(3), pages 221-264, September.
    2. Ichiro Nishizaki & Tomohiro Hayashida & Shinya Sekizaki & Kojiro Furumi, 2023. "A two-stage linear production planning model with partial cooperation under stochastic demands," Annals of Operations Research, Springer, vol. 320(1), pages 293-324, January.
    3. Xuan Vinh Doan & Tri-Dung Nguyen, 2019. "Technical Note—Robust Newsvendor Games with Ambiguity in Demand Distributions," Operations Research, INFORMS, vol. 68(4), pages 1047-1062, July.
    4. M. A. Goberna & M. A. López, 2018. "Recent contributions to linear semi-infinite optimization: an update," Annals of Operations Research, Springer, vol. 271(1), pages 237-278, December.
    5. Mike G. Tsionas & Dionisis Philippas & Constantin Zopounidis, 2023. "Exploring Uncertainty, Sensitivity and Robust Solutions in Mathematical Programming Through Bayesian Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 205-227, June.

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    Game theory; Stochastic cooperative game;

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