A Linguistic Cloud-Based Consensus Framework with Three Behavior Classifications Under Trust-Interest Relations

Weiqiao Liu, Jianjun Zhu (), Peide Liu, Peng Wang and Wen Song
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Weiqiao Liu: Nanjing University of Aeronautics and Astronautics
Jianjun Zhu: Nanjing University of Aeronautics and Astronautics
Peide Liu: Shandong University of Finance and Economics
Peng Wang: Shandong University of Finance and Economics
Wen Song: Nanjing University of Science and Technology

Group Decision and Negotiation, 2023, vol. 32, issue 6, No 9, 1497-1533

Abstract: Abstract In the consensus reaching process (CRP), experts are not infinitely compromising when adjusting opinions, and the “compromise limit” is used to reflect the limited compromise behaviors in the process of expert opinion adjustment. In complex and multiple social relations, experts tend to exhibit different behaviors according to their compromise limits. This paper aims to develop a novel CRP framework to promote a consensus that categorizes and manages experts based on their compromise limits. Firstly, the trust-interest network is defined to represent the multiple relations among experts, and the expert weights are calculated by considering the impact of interest manipulation on trust relations. Secondly, a novel cloud model-based minimum cost consensus model is established, which considers the mutual acceptance between the experts and the group, as well as the changes in the ranges of experts’ hesitation and the collective acceptance. Thirdly, three behavior classifications are defined based on individual compromise limits and group acceptance ranges: cooperative behavior, hesitating non-cooperative behavior, and strong non-cooperative behavior, and a CRP optimization model is constructed to manage the three behaviors. Finally, a numerical example is given to illustrate the validity and superiority of the proposed model.

Keywords: Normal cloud model; Compromise limits; Behavior classifications; Minimum consensus cost; Trust-interest network (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10726-023-09851-z

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