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Exploring the minimum cost conflict mediation path to a desired resolution within the inverse graph model framework

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  • Zhu, Yan
  • Dong, Yucheng
  • Zhang, Hengjie
  • Fang, Liping

Abstract

The existing inverse graph model for conflict resolution (GMCR) research primarily concentrates on identifying the required preferences of decisions makers (DMs) such that a desired state is an equilibrium. However, the process of transitioning from the current state to the desired equilibrium is not explored. In this paper, we propose a minimum adjustment cost model taking account of preference adjustment costs to identify the required preferences for a desired state to be an equilibrium. Subsequently, we introduce the concept of transition costs for the first time to quantify expenses involved in guiding a DM transition from one state to another and develop a minimum cost conflict mediation path model. This model aims to identify the most efficient path that minimizes the cost of transitioning from the current state to the desired equilibrium. Moreover, to accommodate the consideration of multiple desired equilibria, we extend the minimum cost conflict mediation path model to analyze and determine the optimal path for transitioning from the current state to one of the identified desired equilibria with the overall minimum cost. Furthermore, to address uncertainty surrounding transition costs, we formulate a probability maximizing conflict mediation path model that considers a limited budget available for the mediation process. Finally, a real-world dispute, the fracking conflict in the province of New Brunswick, Canada, is utilized to demonstrate the application of the proposed models.

Suggested Citation

  • Zhu, Yan & Dong, Yucheng & Zhang, Hengjie & Fang, Liping, 2025. "Exploring the minimum cost conflict mediation path to a desired resolution within the inverse graph model framework," European Journal of Operational Research, Elsevier, vol. 321(2), pages 543-564.
  • Handle: RePEc:eee:ejores:v:321:y:2025:i:2:p:543-564
    DOI: 10.1016/j.ejor.2024.10.014
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    References listed on IDEAS

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    1. Liangyan Tao & Xuebi Su & Saad Ahmed Javed, 2021. "Inverse Preference Optimization in the Graph Model for Conflict Resolution based on the Genetic Algorithm," Group Decision and Negotiation, Springer, vol. 30(5), pages 1085-1112, October.
    2. Zhao, Shinan & Xu, Haiyan & Hipel, Keith W. & Fang, Liping, 2019. "Mixed stabilities for analyzing opponents’ heterogeneous behavior within the graph model for conflict resolution," European Journal of Operational Research, Elsevier, vol. 277(2), pages 621-632.
    3. O'Brien, Nicole L. & Hipel, Keith W., 2016. "A strategic analysis of the New Brunswick, Canada fracking controversy," Energy Economics, Elsevier, vol. 55(C), pages 69-78.
    4. 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.
    5. He, Shawei & Marc Kilgour, D. & Hipel, Keith W., 2017. "A general hierarchical graph model for conflict resolution with application to greenhouse gas emission disputes between USA and China," European Journal of Operational Research, Elsevier, vol. 257(3), pages 919-932.
    6. Huang, Yuming & Ge, Bingfeng & Hipel, Keith W. & Fang, Liping & Zhao, Bin & Yang, Kewei, 2023. "Solving the inverse graph model for conflict resolution using a hybrid metaheuristic algorithm," European Journal of Operational Research, Elsevier, vol. 305(2), pages 806-819.
    7. Kadziński, Miłosz & Ciomek, Krzysztof, 2021. "Active learning strategies for interactive elicitation of assignment examples for threshold-based multiple criteria sorting," European Journal of Operational Research, Elsevier, vol. 293(2), pages 658-680.
    8. 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.
    9. Jiapeng Liu & Miłosz Kadziński & Xiuwu Liao, 2023. "Modeling Contingent Decision Behavior: A Bayesian Nonparametric Preference-Learning Approach," INFORMS Journal on Computing, INFORMS, vol. 35(4), pages 764-785, July.
    10. García-Zamora, Diego & Dutta, Bapi & Massanet, Sebastia & Riera, Juan Vicente & Martínez, Luis, 2023. "Relationship between the distance consensus and the consensus degree in comprehensive minimum cost consensus models: A polytope-based analysis," European Journal of Operational Research, Elsevier, vol. 306(2), pages 764-776.
    11. Rêgo, Leandro Chaves & Silva, Hugo Victor & Rodrigues, Carlos Diego, 2021. "Optimizing the cost of preference manipulation in the graph model for conflict resolution," Applied Mathematics and Computation, Elsevier, vol. 392(C).
    12. 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.
    13. 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.
    14. 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.
    15. Xu, Haiyan & Marc Kilgour, D. & Hipel, Keith W. & Kemkes, Graeme, 2010. "Using matrices to link conflict evolution and resolution in a graph model," European Journal of Operational Research, Elsevier, vol. 207(1), pages 318-329, November.
    16. He, Shawei, 2022. "A time sensitive graph model for conflict resolution with application to international air carbon negotiation," European Journal of Operational Research, Elsevier, vol. 302(2), pages 652-670.
    17. Gilbert, Hugo & Spanjaard, Olivier, 2017. "A double oracle approach to minmax regret optimization problems with interval data," European Journal of Operational Research, Elsevier, vol. 262(3), pages 929-943.
    18. Wang, Junjie & Hipel, Keith W. & Fang, Liping & Dang, Yaoguo, 2018. "Matrix representations of the inverse problem in the graph model for conflict resolution," European Journal of Operational Research, Elsevier, vol. 270(1), pages 282-293.
    19. Kevin W. Li & Keith W. Hipel & D. Marc Kilgour & Donald Noakes, 2005. "Integrating Uncertain Preferences into Status Quo Analysis with Applications to an Environmental Conflict," Group Decision and Negotiation, Springer, vol. 14(6), pages 461-479, November.
    20. Yu Han & Haiyan Xu & Liping Fang & Keith W. Hipel, 2022. "An Integer Programming Approach to Solving the Inverse Graph Model for Conflict Resolution with Two Decision Makers," Group Decision and Negotiation, Springer, vol. 31(1), pages 23-48, February.
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