A two-stage consensus model for large-scale group decision-making considering dynamic social networks

Abstract

Clustering and the feedback mechanism in a consensus reaching process (CRP) have been regarded as two critical components when solving the large-scale group decision-making (LSGDM) problem. However, the necessity of treating decision makers (DMs) as a dynamic social network (in terms of their trust relationships) in the DM clustering has not been fully investigated. Also, with people's increased democratic awareness, it appears essential to consider the individual's willingness to cooperate when designing the feedback mechanism in the CRP. This study will focus on solving the LSGDM problem considering dynamic social networks and propose a novel two-stage consensus model. First, a hybrid trust network is built to dynamically represent social relationships among DMs (i.e., the hybrid trust network evolves with the CRP). Spectral clustering algorithm is then used to cluster DMs into more trusted sub-groups, thus improving the efficiency of coordinating DMs. Second, a two-stage feedback mechanism is designed to help DMs reach a consensus in a more human-centric manner. To reach a consensus without seriously violating the willingness of DMs, we strike a balance between maximizing the consensus degree and minimizing the adjustment cost and further develop two nonlinear programming models to provide modification suggestions for sub-groups and individuals, respectively. The originality of the proposed consensus model can be summarized as clustering the dynamic hybrid trust network integrated with balancing the decision-making efficiency against the willingness of DMs to cooperate. The feasibility and effectiveness of the developed model are demonstrated through the detailed application study.