A large-scale consensus model to manage non-cooperative behaviors in group decision making: A perspective based on historical data. (15th March 2023)

• Record Type: Journal Article
• Authors: Xiong, Kai; Dong, Yucheng; Zha, Quanbo
• Is Part Of: Expert systems with applications. Volume 214(2023)
• Publication Date: 2023-03-15
• Subjects: Large-scale group decision making -- Consensus reaching process -- Non-cooperative behaviors -- K-means clustering -- Historical data; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.119163
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A novel large-scale consensus reaching process framework is proposed. Integrate decision makers' historical preferences into cluster analysis. Simulation experiments are designed to show the validity of the framework. Abstract: In the large-scale group decision making (LSGDM) problems, it is common that some decision makers might refuse to modify their preferences to achieve a consensus or even form an alliance to exhibit non-cooperative behaviors for their own interests. These non-cooperative behaviors might bias or hinder the consensus reaching process (CRP). In this paper, we propose a large-scale consensus model to manage non-cooperative behaviors based on the clustering method using the historical preference data of decision makers. In the proposed framework, all the historical preference data of decision makers are used for clustering to catch their features, by which the decision makers with the three defined non-cooperative behaviors will be more centralized. Then, the clusters with non-cooperative behaviors will be detected better through their preference adjustments. On this basis, the penalty strategy of the decision makers in clusters with non-cooperative behaviors would be more effective. Simulation experiments and comparison studies are presented to demonstrate the validity of the proposed consensus framework against traditional frameworks for coping with non-cooperative behaviors.