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Inverse Preference Optimization in the Graph Model for Conflict Resolution based on the Genetic Algorithm

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  • Liangyan Tao

    (Nanjing University of Aeronautics and Astronautics)

  • Xuebi Su

    (Nanjing University of Aeronautics and Astronautics)

  • Saad Ahmed Javed

    (Nanjing University of Information Science and Technology)

Abstract

The Inverse GMCR (Graph Model for Conflict Resolution) produces rankings of possible states (preference relation profiles) that will make the desired resolution of a conflict stable. However, there are usually numerous preference relation profiles making it difficult for a third party to choose an appropriate preference relation to design its mediation strategy. Moreover, the cost or effort of changing preference relations over states has rarely been studied in Inverse GMCR. The current study presents two inverse preference optimization models considering the cost and effort in changing preferences to address these issues. The first model aims to ascertain an optimal preference at minimum adjustment cost such that the desired equilibrium is reached. The other model is to find an optimal required preference under minimum adjustment amount, which is defined as the difference between the required preference matrix and the original preference matrix. Then, a Genetic Algorithm (GA)-based algorithm is proposed. Finally, the two proposed preference optimization methods are applied to two cases, demonstrating the effectiveness of the proposed methodology.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:grdene:v:30:y:2021:i:5:d:10.1007_s10726-021-09748-9
    DOI: 10.1007/s10726-021-09748-9
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    References listed on IDEAS

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