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A graph model for conflict resolution with inconsistent preferences among large-scale participants

Author

Listed:
  • Ming Tang

    (Sichuan University)

  • Huchang Liao

    (Sichuan University)

Abstract

As a flexible and powerful method to resolve strategy conflicts, the graph model for conflict resolution has drawn much attention. In the graph model for conflict resolution, decision-makers need to provide their preference information for all possible scenarios. Most existing studies assumed that decision-makers adopt quantitative representation formats. However, in some real-life situations, decision-makers may tend to use qualitative assessments due to their cognitive expression habits. In addition, stakeholders involved in a graph model can be a group that is composed of a large number of participants. How to manage these participants’ inconsistent preference assessments is also a debatable issue. To fit these gaps, in this study, we propose a graph model for conflict resolution with linguistic preferences, and this model allows participants to use inconsistent assessments. To do this, we first construct a linguistic preference structure, with the necessary concepts being defined. Then, four stability definitions for both a two-decision-maker scenario and an n-decision-maker scenario are introduced. To illustrate the usefulness of the proposed model, an illustrative example regarding the Huawei conflict is provided.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:fuzodm:v:21:y:2022:i:3:d:10.1007_s10700-021-09373-w
    DOI: 10.1007/s10700-021-09373-w
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. Herrera, F. & Herrera-Viedma, E., 2000. "Choice functions and mechanisms for linguistic preference relations," European Journal of Operational Research, Elsevier, vol. 120(1), pages 144-161, January.
    5. M. Abul Bashar & Keith W. Hipel & D. Marc Kilgour & Amer Obeidi, 2018. "Interval fuzzy preferences in the graph model for conflict resolution," Fuzzy Optimization and Decision Making, Springer, vol. 17(3), pages 287-315, September.
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    Cited by:

    1. 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.

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