Consensus convergence in large-group social network environment: Coordination between trust relationship and opinion similarity

摘要

Group decision-making (GDM) in large-group social network environment (LGSNE) has attracted considerable attention in the field of decision science. Social relationships exist among decision makers, and individual decisions are often influenced by others they are connected with. Opinions among large-scale decision-makers can easily be controversial and conflicting. Reaching consensus is necessary, but it requires the adjustment of some individual opinions. Due to differences in self-interest and perception, some decision-makers are noncooperative with regard to adjusting their opinions to promote consensus. This may delay consensus convergence and ultimately affect decision quality. This study proposes a two-dimensional consensus convergence model considering noncooperative behaviors. We first describe the characteristics of GDM problems in LGSNE. Two measurement attributes - trust relationship and opinion similarity - are identified as important factors throughout the decision-making process. Then, we propose a hierarchical clustering method based on the trust- similarity measure. A weight-determining method for clusters is presented that considers the internal and external features of a cluster. Based on these, a two-dimensional consensus convergence process is designed to reduce opinion differences and manage noncooperative behaviors. Finally, a numerical experiment is used to illustrate the feasibility and efficacy of the proposed model, and comparative analysis reveals its features and advantages. (C) 2021 Elsevier B.V. All rights reserved.