Large-scale group consensus decision making in social networks considering adverse selection in an asymmetric information environment

In consensus-based large scale group decision making (LSGDM) problems, some experts often exhibit adverse selection behavior due to asymmetry in information availability. This may lead to results deviating from the optimum, weakening decision making fairness and reducing consensus efficiency. For this reason, this paper proposes a large group consensus decision making method based on managing adverse selection behavior in an asymmetric information environment. Firstly, the directed Louvain algorithm is introduced to achieve the decision making subgroup division based on the directed social network. On this basis, considering the different qualifications and research fields of experts, a new weight allocation method is proposed based on the authority of experts. Next, focusing on the consensus-reaching process, a mechanism for identifying and managing adverse selection behaviors is proposed. A hierarchical recognition framework is designed for behavior identification, incorporating behavioral patterns and underlying motivations. A multidimensional dynamic adjustment strategy based on weight and preference is introduced for behavior management, then a comprehensive large-group consensus decision making method based on adverse selection behavior management is developed. Finally, the feasibility and effectiveness of the proposed method are verified using case studies and parameter discussions.