A Two-Stage Consensus Model For Large Group Decision Making In The Context Of Social Networks

With the improvement of the democratic system and the increasing complexity of social issues,more and more people are involved in the decision-making of important public events.The increase in the number of decision makers has significantly increased the complexity of decision-making issues,and the intricate social relationships among decision makers have also had a significant impact on the decision-making process.In this context,it is of great significance to efficiently solve large scale group decisionmaking problems by properly portraying the social relationships and using them to help decision makers reach a consensus.This thesis focuses on large-scale group decision-making in the context of social networks and proposes a two-stage consensus model with spectral clustering algorithm.First,build a hybrid trust relationship network to dynamically portray the social relationships,and cluster decision makers based on this.Spectral clustering algorithm is introduced to simplify the problems and improve the efficiency of coordination among decision makers.On this basis,a two-stage feedback mechanism is designed to help decision-makers reach a consensus.In order to reach a consensus without violating the will of decision makers,this thesis constructs nonlinear programming models with the goal of maximizing the consensus degree and minimizing the adjustment cost respectively,so as to provide adjustment suggestions for sub-groups and individuals.Rank the alternatives based on the consensus of decision makers and the best one is selected then.Finally,the feasibility and effectiveness of the consensus model proposed in this thesis are verified through an illustrative example.In this thesis,the influence of social relationships on large-scale group decisionmaking problems is fully considered.Through the two-stage feedback mechanism,both the efficiency of consensus-reaching and the independence of decision-makers are taken into consideration,providing a scientific and efficient solution for large-scale group decision-making problems.