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The trust degree between individuals plays an important role in social network group decision-making (SNGDM). The majority of current literature assumes that the trust degree between individuals is constant. However, the trust values among decision-makers (DMs) are subject to change over time. Thus, it is necessary to identify potential dynamic trust that is compatible with the actual SNGDM to support the consensus reaching. For this purpose, this article investigates consensus building that considers dynamic trust among DMs in SNGDM with linguistic distribution assessments (LDAs). Firstly, the three-dimensional trust degree among DMs is constructed from three perspectives: the trust relationship from social networks, the confidence level of DMs, and the similarity of DMs' preferences. Secondly, an optimization model is developed with the objective of maximizing consensus to determine how trust degree is assigned to each perspective, thereby determining the weights of DMs. Then, a double feedback-based consensus mechanism incorporating both opinion evolution and trust evolution is developed. Under the guidance of consensus mechanism, an improved SNGDM approach with LDAs is presented. Finally, we demonstrate our proposed approach through a case study of sustainable supplier selection in a circular economy. Comparative analysis and sensitive analysis verify our approach's effectiveness. [ABSTRACT FROM AUTHOR]

The consensus reaching process (CRP) is a key step in group decision-making (GDM), in which reaching a satisfactory consensus often requires certain costs, such as time, money, and effort. Moreover, benefits from the CRP are often compared among decision-makers, which raises fairness issues. Given these, this paper comprehensively considers these two factors in linguistic GDM modeling. Aiming at the problem of multi-attribute group decision-making (MAGDM) under distributed linguistic assessment (DLA) information, a two-stage group consensus model that considers psychological behaviors with fairness concern is proposed. Subsequently, the feasibility and effectiveness of the proposed model are illustrated by a case of selecting emergency material warehouses related to urban flood disasters. The main innovations and contributions of this paper are as follows. (a) The psychology of individual fairness concern is integrated into the MAGDM process with DLA information. (b) The DLA-based two-stage group consensus model, including the first-stage consensus model with maximum fairness satisfaction degree and the second-stage consensus model with minimum cost, is developed. Compared with the existing consensus models for distributed linguistic MAGDM, the proposed consensus model can not only improve the consensus level on the opinions with minimum cost but also promote the fairness satisfaction degree of decision-makers. [ABSTRACT FROM AUTHOR]

In this article, an adaptive consensus model that considers individual disappointment emotion is proposed for emergency multi-attribute group decision-making (MAGDM) problems with hesitant fuzzy linguistic information. Subsequently, it is applied to choose the optimal emergency alternative(s) for the prevention and control of COVID-19 on a college campus. The main innovations and contributions of this article are as follows: (a) Individual modified perceived utility (MPU) based on disappointment theory is integrated into the determination of attribute weights and construction of consensus reaching process (CRP). (b) The MPU-based individual contribution degree and the MPU-based soft group consensus degree are developed. (c) The new emergency CRP mechanism not only considers the rewards and penalties of expert weights but also regards the adaptive updating of attribute weights. Compared with the existing emergency MAGDM models in a hesitant fuzzy linguistic environment, the proposed consensus model has some advantages in improving consensus efficiency and simulating uncertain psychological behavior. [ABSTRACT FROM AUTHOR]