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Optimism pessimism stability in the graph model for conflict resolution for multilateral conflicts

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  • Sabino, Emerson Rodrigues
  • Rêgo, Leandro Chaves

Abstract

In this article, we introduce a new solution concept for the graph model for conflict resolution (GMCR) for situations where decision makers (DMs) have no information about their opponents’ preferences or act not using this information. Many classical stability concepts used in the GMCR are based on a conservative attitude of DMs. Here we propose to incorporate a degree of optimism to allow for more types of attitudes under uncertainty. More specifically, inspired by the notions of Lh stability and Maximinh stability, we present the Optimism Pessimism stability for conflicts involving any finite number of DMs considering that a DM anticipates his/her own benefits looking an arbitrary number of rounds ahead, called horizon and denoted by h, and uses his/her degree of optimism to evaluate his/her anticipated payoff according to the worst and best scenarios that can be achieved. We investigate how the degree of optimism and the conflict horizon influence the stability of states. We also analyse how the optimism pessimism stability is related to other stabilities commonly used in GMCR. Finally, this new solution concept is applied to the multilateral dispute over water export from Lake Gisborne.

Suggested Citation

  • Sabino, Emerson Rodrigues & Rêgo, Leandro Chaves, 2023. "Optimism pessimism stability in the graph model for conflict resolution for multilateral conflicts," European Journal of Operational Research, Elsevier, vol. 309(2), pages 671-682.
    • Handle: RePEc:eee:ejores:v:309:y:2023:i:2:p:671-682
    DOI: 10.1016/j.ejor.2023.01.038
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    References listed on IDEAS

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    1. Ming Tang & Huchang Liao, 2022. "A graph model for conflict resolution with inconsistent preferences among large-scale participants," Fuzzy Optimization and Decision Making, Springer, vol. 21(3), pages 455-478, September.
    2. Yu Han & Haiyan Xu & Ginger Y. Ke, 2020. "Construction and application of hyper-inverse conflict models based on the sequential stability," EURO Journal on Decision Processes, Springer;EURO - The Association of European Operational Research Societies, vol. 8(3), pages 237-259, November.
    3. Helena Gaspars-Wieloch, 2018. "The Impact of the Structure of the Payoff Matrix on the Final Decision made Under Uncertainty," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 35(01), pages 1-27, February.
    4. Sean B. Walker & Keith W. Hipel, 2017. "Strategy, Complexity and Cooperation: The Sino-American Climate Regime," Group Decision and Negotiation, Springer, vol. 26(5), pages 997-1027, September.
    5. Rêgo, Leandro Chaves & Kilgour, D. Marc, 2022. "Choice stabilities in the graph model for conflict resolution," European Journal of Operational Research, Elsevier, vol. 301(3), pages 1064-1071.
    6. Luai Hamouda & D. Marc Kilgour & Keith W. Hipel, 2004. "Strength of Preference in the Graph Model for Conflict Resolution," Group Decision and Negotiation, Springer, vol. 13(5), pages 449-462, September.
    7. Keith W. Hipel & Liping Fang & D. Marc Kilgour, 2020. "The Graph Model for Conflict Resolution: Reflections on Three Decades of Development," Group Decision and Negotiation, Springer, vol. 29(1), pages 11-60, February.
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    Cited by:

    1. Sabino, Emerson Rodrigues & Rêgo, Leandro Chaves, 2024. "Minimax regret stability in the graph model for conflict resolution," European Journal of Operational Research, Elsevier, vol. 314(3), pages 1087-1097.

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