A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

作     者：Rongrong Ren Luyang Su Xinyu Meng Jianfang Wang Meng Zhao 

作者机构：School of Business AdministrationNortheastern UniversityShenyang110819China School of ManagementNortheastern University at QinhuangdaoQinhuangdao066004China School of Modern LogisticsShanxi Vocational University of Engineering Science and TechnologyTaiyuan030031China Business SchoolSichuan UniversityChengdu610064China 

出 版 物：《工程与科学中的计算机建模(英文)》 (Computer Modeling in Engineering & Sciences)

年 卷 期：2024年第138卷第1期

页      面：429-458页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 0701[理学-数学] 

基　　金：The work was supported by Humanities and Social Sciences Fund of the Ministry of Education(No.22YJA630119) the National Natural Science Foundation of China(No.71971051) Natural Science Foundation of Hebei Province(No.G2021501004). 

主　　题：Large-scale group decision making social network updating trust relationship group consensus feedback mechanism 

摘      要：With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.