Open Access

ARTICLE

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

by Rongrong Ren^1,2, Luyang Su^1,2, Xinyu Meng^1,2, Jianfang Wang³, Meng Zhao^1,2,4,*

1 School of Business Administration, Northeastern University, Shenyang, 110819, China
2 School of Management, Northeastern University at Qinhuangdao, Qinhuangdao, 066004, China
3 School of Modern Logistics, Shanxi Vocational University of Engineering Science and Technology, Taiyuan, 030031, China
4 Business School, Sichuan University, Chengdu, 610064, China

* Corresponding Author: Meng Zhao. Email:

(This article belongs to the Special Issue: Linguistic Approaches for Multiple Criteria Decision Making and Applications)

Computer Modeling in Engineering & Sciences 2024, 138(1), 429-458. https://doi.org/10.32604/cmes.2023.027310

Received 24 October 2022; Accepted 23 March 2023; Issue published 22 September 2023

Abstract

With the development of big data and social computing, large-scale group decision making (LGDM) is now merging with social networks. Using social network analysis (SNA), this study proposes an LGDM consensus model that considers the trust relationship among decision makers (DMs). In the process of consensus measurement: 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 proposed model not only allows the subgroups to be reconstructed and updated during the adjustment process, but also improves the accuracy of the adjustment by the feedback mechanism. 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.

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Keywords

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