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A general hierarchical graph model for conflict resolution with application to greenhouse gas emission disputes between USA and China

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  • He, Shawei
  • Marc Kilgour, D.
  • Hipel, Keith W.

Abstract

The general hierarchical graph model, a significant expansion of the graph model for conflict resolution methodology, is designed to analyze interrelated conflicts with hierarchical structures. In a general hierarchical graph model, there are common decision makers, who take part in all related subconflicts, and local decision makers, who participate in only one subconflict. In this paper, preference structures for decision makers in a hierarchical graph model are established, and theorems are developed that elucidate the relationship between stabilities in the overall (hierarchical) model and stabilities in the component submodels. To illustrate, the hierarchical graph model is applied to greenhouse gas emission disputes between USA and China, where local decision makers in the USA are the two parties in Congress, and local decision makers in China are state-owned energy companies. The stability results suggest potential strategic resolutions of bilateral disputes, and how parties can attain them.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:257:y:2017:i:3:p:919-932
    DOI: 10.1016/j.ejor.2016.08.014
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    Cited by:

    1. 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.
    2. Leandro Chaves Rêgo & France E. G. Oliveira, 2020. "Higher-order Sequential Stabilities in the Graph Model for Conflict Resolution for Bilateral Conflicts," Group Decision and Negotiation, Springer, vol. 29(4), pages 601-626, August.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Samaniego Rascón, Danyela & Ferreira, Almerindo D. & Gameiro da Silva, Manuel, 2017. "Cumulative and momentary skin exposures to solar radiation in central receiver solar systems," Energy, Elsevier, vol. 137(C), pages 336-349.
    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. 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.
    10. 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.

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