A joint feedback strategy for consensus in large-scale group decision making under social network. (September 2020)

• Record Type: Journal Article
• Authors: Gai, Tiantian; Cao, Mingshuo; Cao, Qingwei; Wu, Jian; Yu, Gaofeng; Zhou, Mi
• Is Part Of: Computers & industrial engineering. Volume 147(2020)
• Publication Date: 2020-09
• Subjects: Group decision making -- Consensus -- Social network -- Joint adjusting strategy -- Harmony degree; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2020.106626
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A novel framework of joint feedback is proposed to help multiple non-consensus decision makers reach agreement. The application of social network analysis in large-scale group decision making is explored. Two optimization models of different feedback behaviors is built with the aim of maximum harmony degree. Abstract: Nowadays, large-scale group decision making (LSGDM) has become a hot topic and brought new challenges for the decision makers. This article proposes a framework of joint feedback strategy to help large-scale group decision makers to reach an agreement by combing social network context and feedback behavior. Firstly, the social network of large-scale group decision makers is explored to study the trust relationship, and it is used to assign weights associated to decision makers. And the recommendation advice can be generated by trust relationship using as reliable resource to aggregate group opinions to a collective one. Secondly, the recommendation advice is embedded to the feedback mechanism in LSGDM, and a joint feedback strategy is proposed based on harmony degree to help the multiple non-consensus decision makers modify their preferences to improve the efficiency of consensus achievement. In detail, this article builds two optimization models with the aim of maximum harmony degree: (1) one is with consistent feedback behaviour; (2) the other is with different feedback behaviour. At last, a numerical and a comparison analysis are provided to show the validity of joint feedback strategy with different feedback behaviors.