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Heterogeneous Opinion Dynamics Considering Consensus Evolution in Social Network Group Decision-Making

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  • Tong Wu

    (Nanjing University of Aeronautics and Astronautics
    Ministry of Industrial and Information Technology)

Abstract

Social network group decision-making (SNGDM) is a new type of group decision-making (GDM) paradigm that has emerged in recent years. The traditional consensus feedback adjustment model for GDM is difficult to adapt to the characteristics of SNGDM, with a large number of participants, unrestricted by time and space, and unfixed decision-making rules. Opinion dynamics is an important tool for predicting the evolution of group opinions based on established opinion evolution rules, relying solely on the initial opinions of participants. The combination of opinion dynamics and SNGDM is natural. However, this combination still faces many problems, such as the current opinion dynamics models having difficulty handling the common heterogeneous preferences in GDM, and little consideration being given to the interaction between social relationships and opinion evolution. This paper studies heterogeneous opinion dynamics phenomena considering consensus evolution in SNGDM. We process heterogeneous preferences based on the measurement of distance and similarity, improve Friedkin and Johnsen model considering the stubbornness of the decision-makers with respect to their own latest opinions dynamically, and mainly focus on the interaction between opinion evolution and social relationships. A case study on enterprise team risk management is given to illustrate the effectiveness of the proposed method. Through comparative analysis, we find that when the group is in a connected network with consistent goals, the interaction between opinion evolution and social relationships can achieve consensus faster than in other situations.

Suggested Citation

  • Tong Wu, 2024. "Heterogeneous Opinion Dynamics Considering Consensus Evolution in Social Network Group Decision-Making," Group Decision and Negotiation, Springer, vol. 33(1), pages 159-194, February.
    • Handle: RePEc:spr:grdene:v:33:y:2024:i:1:d:10.1007_s10726-023-09858-6
    DOI: 10.1007/s10726-023-09858-6
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