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A hierarchical consensus reaching process for group decision making with noncooperative behaviors

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  • Tang, Ming
  • Liao, Huchang
  • Mi, Xiaomei
  • Lev, Benjamin
  • Pedrycz, Witold

Abstract

With the development of technological and societal paradigms, we witness a trend where a large number of experts participate in decision-making processes, and large-scale group decision making has become a much researched topic. A large-scale group decision-making problem usually involves many experts with various backgrounds and experiences. In these cases, an effective consensus reaching process is essential to guarantee the support of all experts, especially in large-scale group decision-making settings. This study proposes a hierarchical consensus model that allows the number of adjusted opinions to vary depending on the specific level of consensus in each iterative round. Furthermore, this study also introduces a method to detect and manage noncooperative behaviors by means of the hierarchical consensus model. The minimum spanning tree clustering algorithm is used to classify experts. A weight determination method combining the size of the subgroup and the sum of squared errors is developed for subgroups. Finally, an illustrative example is provided to demonstrate the practicality of the proposed model.

Suggested Citation

  • Tang, Ming & Liao, Huchang & Mi, Xiaomei & Lev, Benjamin & Pedrycz, Witold, 2021. "A hierarchical consensus reaching process for group decision making with noncooperative behaviors," European Journal of Operational Research, Elsevier, vol. 293(2), pages 632-642.
    • Handle: RePEc:eee:ejores:v:293:y:2021:i:2:p:632-642
    DOI: 10.1016/j.ejor.2020.12.028
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    References listed on IDEAS

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    1. Tang, Ming & Liao, Huchang & Xu, Jiuping & Streimikiene, Dalia & Zheng, Xiaosong, 2020. "Adaptive consensus reaching process with hybrid strategies for large-scale group decision making," European Journal of Operational Research, Elsevier, vol. 282(3), pages 957-971.
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    Cited by:

    1. Dombi, József & Jónás, Tamás, 2024. "Consensus measures based on a fuzzy concept," European Journal of Operational Research, Elsevier, vol. 315(2), pages 642-653.
    2. Tong, Huagang & Zhu, Jianjun, 2023. "A parallel approach with the strategy-proof mechanism for large-scale group decision making: An application in industrial internet," European Journal of Operational Research, Elsevier, vol. 311(1), pages 173-195.
    3. Liu, Hu-Chen & Wang, Jing-Hui & Zhang, Ling & Zhang, Qi-Zhen, 2022. "New success likelihood index model for large group human reliability analysis considering noncooperative behaviors and social network," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    4. 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.
    5. Zhou, Jian-Lan & Yu, Ze-Tai & Xiao, Ren-Bin, 2022. "A large-scale group Success Likelihood Index Method to estimate human error probabilities in the railway driving process," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    6. Guo, Weiwei & Gong, Zaiwu & Zhang, Wei-Guo & Xu, Yanxin, 2023. "Minimum cost consensus modeling under dynamic feedback regulation mechanism considering consensus principle and tolerance level," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1279-1295.
    7. Gong, Zaiwu & Guo, Weiwei & Słowiński, Roman, 2021. "Transaction and interaction behavior-based consensus model and its application to optimal carbon emission reduction," Omega, Elsevier, vol. 104(C).

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