Large Group Decision-Making Method Based on Social Network Analysis: Integrating Evaluation Information and Trust Relationships

In the context of large group decision-making (LGDM), the opinions of individuals can influence each other due to their trust relationships. So, trust relationships should be deemed as just as important as evaluation information, and they should be considered jointly throughout the LGDM. This study first transforms the trust relationships between decision-makers into an information type, labeled as compromise information, whose form is the same as the evaluation information. The compromise information is utilized to incorporate trust relationships into various stages of the decision-making process, including clustering, weight determination, consensus reaching, and alternative selection. In the expert clustering and weight determination processes, more criteria and factors are considered by considering the compromise information. In the consensus reaching process, an optimization model is built to adjust the evaluation information of clusters to simultaneously guarantee a substantial increase in the global consensus level and minimize the adjustment cost. The compromise information also serves as a reference to limit the range of the adjusted information. An objective method to determine the consensus threshold is proposed. The proposed method is validated through an application example and comparisons, demonstrating its rationality and effectiveness. Simulation results indicate that the proposed consensus reaching method converges regardless of the number of experts, alternatives, and criteria. The proposed method integrates evaluation information and trust relationships into the LGDM process, thereby improving the rationality and scientificity of the decision results.