A Consensus Model Based on Social Network Analysis in Large-Scale Group Decision Making: Mining and Managing Trust Risk Behaviors

In traditional social network analysis-based large-scale group decision making (LSGDM), decision makers (DMs) often trust others based on social prestige/status without sufficient information to understand others’ credibility. Notably, this ignores the inherent risks of trust, resulting in the LSGDM process being subject to trust risk behaviors (blind trust/lack of trust), which ultimately causes unreasonable aggregation weights allocation and unreliable decision results. Therefore, this article develops a trust risk analysis-based social network LSGDM model to mine and manage trust risk behaviors, reasonably assign aggregation weights and reach consensus. In this model, DMs are divided into different communities by a clustering method. The performances of DMs in each community are observed during the decision-making process and their credibility is evaluated. Community’s credibility is modeled by a multicriteria credibility assessment matrix (MCAM). Combined with external network structure information, community’s synthetical trust assessment matrix (STAM) is constructed. Furthermore, group’s MCAM and STAM are obtained by information collection from all communities. The aggregation weights of communities and DMs are dynamically derived from STAMs of group and communities, respectively. Afterward, a trust risk behaviors mining and detection method is presented to mine communities’ different behaviors and determine the trust risk degree of the group. When the group is deemed too risky, the trust risk behaviors management mechanism allows to compensate or punish the weights of communities with different behaviors, and give recommendations for information modification. Sensitivity analysis and comparative analysis, as well as a case study, verify the feasibility and superiority of the proposed methods.