A consensus method in social network large-scale group decision making with interval information

Abstract

Large-scale group decision making (LSGDM) is considered when the number of experts involved in the decision exceeds 20. Due to the large number of people involved in LSGDM, its decision-making process is uncertain, complex, and time-consuming. Therefore, how to effectively help large groups reach consensus in a complex environment is a challenge for current research. Consensus reaching process (CRP) is an effective tool to eliminate group conflicts. Based on this, we propose a consensus reaching process based on the Louvain algorithm, social network, and bounded confidence (SNBC) model with interval numbers. First, we use interval numbers to express expert opinions and social network relationships among experts. Second, the experts are clustered using the Louvain algorithm. The weights of experts are obtained by social network analysis. Third, we use the SNBC model to design a feedback mechanism for tripartite opinions. In addition, we give a numerical example and simulation experiments to demonstrate the flexibility and effectiveness of the proposed approach. Finally, the comparative analysis shows the superiority of our method.