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

作     者：You, Xinli Hou, Fujun 

作者机构：Shanxi Univ Sch Econ & Management Taiyuan 030006 Peoples R China Beijing Inst Technol Sch Management & Econ Beijing 100081 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS》 (IEEE Trans. Syst. Man Cybern. Syst.)

年 卷 期：2024年第54卷第6期

页      面：3357-3368页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Linguistics Social networking (online) Reliability Entropy Indexes Optimization methods Decision making Bounded confidence consensus distribution linguistic preference relation (DLPR) optimization model social network group decision making (SNGDM) 

摘      要：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.