A Feedback Mechanism With Unknown Bounded Confidence-Based Optimization Model for Consensus Reaching in Social Network Group Decision Making

Various feedback mechanisms focus on bounded confidence in the consensus reaching process (CRP) for group decision making (GDM) problems. However, confidence level from DMs' subjective cognition can lead to over-confidence, and thus to have negative effect on CRP. With this idea in mind, this article proposes an objective way to determine bounded confidence levels. In this article, the distribution linguistic preference relation (DLPR) is used to describe decision makers' (DMs') preferences on alternatives. A consensus reaching model with DLPRs in social network GDM (SNGDM) with bounded confidence effect is constructed. In the proposed consensus approach, the objective bounded confidence level is obtained from individual professional performance and social performance, i.e., knowledge degree based on consistency index and entropy measure of DLPRs, and the reliability degree based on trust degree received from other DMs. Then, the acceptable advices based on a bounded confidence-based optimization approach is provided for the identified DMs. Finally, a numerical example and comparative simulation analysis are provided to justify its feasibility and superiority.