Credibility-Supervised Dynamic Clustering and Consensus Model Involving Minority Opinions Handling in Social-Network Large-Scale Decision Making

Abstract

Existing studies on social-network large-scale decision making (SN-LSDM) have overlooked two vital roles of the information credibility of decision makers (DMs): 1) Information credibility directly determines the reliability of clustering results and final decision output; 2) Consideration of minority opinions may hold the truth but may also be risky, while the credibility analysis of minority opinions can help avoid risks. To this end, this paper proposes credibility-supervised dynamic clustering and consensus model involving minority opinion processing. Firstly, DMs' information is analyzed and preprocessed to obtain DMs' credibility evaluation. Subsequently, DMs' credibility is used as one of the clustering criteria to supervise and guide the trust relationships and the clustering of large-scale group, which assists in deriving the reliable clustering result. This clustering is dynamic because the clustering result may alter as the DMs' information changes in consensus. Afterwards, an SN-LSDM consensus model with minority opinions settlement is developed. A credibility-based mechanism for identification, DMs' discussion and weight adjustment of minority opinion subgroup is constructed, including it trust risk measurement method and trust risk level-based weight adjustment. Then, a risk index is defined in feedback adjustment mechanism to adjust DMs' information, and a decision plan with a higher consensus level is obtained. Finally, the feasibility and effectiveness of the proposed model are verified through the case study of “916” Lu County earthquake relief. Discussions and comparisons are conducted by simulation experiments to explore the capabilities and advantages of our proposal.