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Super conflict resolution approach based on minimum loss considering altruistic behavior and fairness concern

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  • Jiang, Qin
  • Liu, Yong
  • An, Jia-qi

Abstract

Due to difference in strength, power, or status among decision makers (DMs), traditional strategic conflict may be evolved into a super conflict problem where a powerful DM can force others to approach his strategy although other DMs suffer loss. For instance, the government can mobilize relevant enterprises to engage in ecological and environmental governance. Also, DMs may conduct irrational behaviors during negotiations, such as altruistic behavior and fairness concern. To guarantee interests of DMs and sustainable development of decision system, exploring the equilibrium strategy and state that resolves such conflict problem becomes necessary. Therefore, based on theories of conflict analysis, group consensus, Nash bargaining, Berge equilibrium and fairness theory, this paper constructs a super conflict resolution model. Firstly, this paper defines a super conflict information system by introducing the concept of ‘strategy’ and DMs’ information vectors containing DMs’ evaluation values regarding conflict states. Then from the perspective of group negotiation and consensus, we divide the super conflict resolution into three stages and construct a super conflict resolution model based on minimum loss. Considering coalitions’ altruistic behavior and fairness concern, we optimize the model by defining Berge equilibrium and fairness utility function. Finally, a case study of river basin pollution treatment verifies the validity and rationality of the proposed model.

Suggested Citation

  • Jiang, Qin & Liu, Yong & An, Jia-qi, 2025. "Super conflict resolution approach based on minimum loss considering altruistic behavior and fairness concern," European Journal of Operational Research, Elsevier, vol. 325(1), pages 147-166.
    • Handle: RePEc:eee:ejores:v:325:y:2025:i:1:p:147-166
    DOI: 10.1016/j.ejor.2025.03.018
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    References listed on IDEAS

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    1. Meng, Fan-Yong & Gong, Zai-Wu & Pedrycz, Witold & Chu, Jun-Fei, 2023. "Selfish-dilemma consensus analysis for group decision making in the perspective of cooperative game theory," European Journal of Operational Research, Elsevier, vol. 308(1), pages 290-305.
    2. Du, Junliang & Liu, Sifeng & Liu, Yong, 2022. "A limited cost consensus approach with fairness concern and its application," European Journal of Operational Research, Elsevier, vol. 298(1), pages 261-275.
    3. Wu, Nannan & Xu, Yejun & Kilgour, D. Marc & Fang, Liping, 2023. "The graph model for composite decision makers and its application to a water resource conflict," European Journal of Operational Research, Elsevier, vol. 306(1), pages 308-321.
    4. Shen, Yufeng & Ma, Xueling & Kou, Gang & Rodríguez, Rosa M. & Zhan, Jianming, 2025. "Consensus methods with Nash and Kalai–Smorodinsky bargaining game for large-scale group decision-making," European Journal of Operational Research, Elsevier, vol. 321(3), pages 865-883.
    5. Cheng, Dong & Zhou, Zhili & Cheng, Faxin & Zhou, Yanfang & Xie, Yujing, 2018. "Modeling the minimum cost consensus problem in an asymmetric costs context," European Journal of Operational Research, Elsevier, vol. 270(3), pages 1122-1137.
    6. Ernst Fehr & Klaus M. Schmidt, 1999. "A Theory of Fairness, Competition, and Cooperation," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 114(3), pages 817-868.
    7. S. Yu & E. C. Ierland & H.-P. Weikard & X. Zhu, 2017. "Nash bargaining solutions for international climate agreements under different sets of bargaining weights," International Environmental Agreements: Politics, Law and Economics, Springer, vol. 17(5), pages 709-729, October.
    8. D. Marc Kilgour & Keith W. Hipel, 2005. "The Graph Model for Conflict Resolution: Past, Present, and Future," Group Decision and Negotiation, Springer, vol. 14(6), pages 441-460, November.
    9. Meng, Fan-Yong & Zhao, Deng-Yu & Gong, Zai-Wu & Chu, Jun-Fei & Pedrycz, Witold & Yuan, Zhe, 2024. "Consensus adjustment for multi-attribute group decision making based on cross-allocation," European Journal of Operational Research, Elsevier, vol. 318(1), pages 200-216.
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