Consensus reaching with trust evolution in social network group decision making

Zhang, Yangjingjing; Chen, Xia; Gao, Lei; Dong, Yucheng; Pedryczc, Witold

doi:10.1016/j.eswa.2021.116022

Cited by 73 publications

(8 citation statements)

References 42 publications

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“…However, the level of trust among DMs could also evolve due to the changes in the opinion similarities. In this regard, an SNDG is proposed in [170] that simultaneously makes use of the historical trust degrees and opinion similarities of DMs to construct an opinion dynamics-based endogenous feedback mechanism and a trust evolution-based exogenous feedback mechanism for consensus reaching. Other than the trust, self-confidence of DMs and node degree of network play an important role in SNDG, where their impacts have been discussed in detail in [171].…”

Section: Social Network Degroot Modelsmentioning

confidence: 99%

Classical dynamic consensus and opinion dynamics models: A survey of recent trends and methodologies

et al. 2022

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Section: Social Network Degroot Modelsmentioning

confidence: 99%

Classical dynamic consensus and opinion dynamics models: A survey of recent trends and methodologies

et al. 2022

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“…The consensus reaching process (CRP) is an effective method to eliminate conflicts in GDM (Cabrerizo et al 2014 ; Amirkhani et al 2022 ). Different types of CRP models have been of interest to the research community: CRP in social networks (Zhang et al 2020 ; Wu et al 2022 ; Zhang et al 2022 ), CRP in dynamic context (Pérez et al 2018 ), CRP with preferred representative structure (Gong et al 2018 ; Wu et al 2020 ), adaptive CRP (Rodríguez 2018 ; Tian et al 2021 ), CRP with minimum cost (Zhang et al 2020 ; Chen et al 2021 ; Gong et al 2021 ), and CRP driven by behavior/attitude (Liu et al 2019b ; Sun et al 2021 ; Cao et al 2022 ). Conflicts in SN-GDM are likely to increase as the size of the group of people involved increases, which subsequently implies that SN-GDM require higher adjustment costs to reach consensus.…”

Section: Introductionmentioning

confidence: 99%

An approach to prevent weight manipulation by minimum adjustment and maximum entropy method in social network group decision making

Sun

Chiclana

et al. 2022

Artif Intell Rev

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In social network group decision making (SN-GDM) problem, subgroup weights are mostly unknown, many approaches have been proposed to determine the subgroup weights. However, most of these methods ignore the weight manipulation behavior of subgroups. Some studies indicated that weight manipulation behavior hinders consensus efficiency. To deal with this issue, this paper proposes a theoretical framework to prevent weight manipulation in SN-GDM. Firstly, a community detection based method is used to cluster the large group. The power relations of subgroups are measured by the power index (PI), which depends on the subgroups size and cohesion. Then, a minimum adjustment feedback model with maximum entropy is proposed to prevent subgroups’ manipulation behavior. The minimum adjustment rule aims for ‘efficiency’ while the maximum entropy rule aims for ‘justice’ . The experimental results show that the proposed model can guarantee the rationality of weight distribution to reach consensus efficiently, which is achieved by maintaining a balance between ‘efficiency’ and ‘justice’ in the mechanism of assigning weights. Finally, the detailed numerical and simulation analyses are carried out to verify the validity of the proposed method.

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“…Group decision-making is widespread in real-world decision-making problems. Many studies focused on all kinds of group decision-making problems and obtained fruitful results [1][2][3]. For group decision-making under fuzzy and uncertain environment, linguistic information is common way to express evaluation information.…”

Section: Introductionmentioning

confidence: 99%

“…Among them, the most notable ones are the following: (1) The method based on 2-tuples linguistic representation model. This method based on the canonical characteristic values of the linguistic terms to deal with linguistic term sets that are symmetrically distributed and not uniformly.…”

Section: Introductionmentioning

confidence: 99%

A personalized individual semantics model for computing with linguistic intuitionistic fuzzy information and application in MCDM

Tang²,

Niu³

et al. 2022

Soft Comput

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This paper develops a personalized individual semantics (PISs) model for computing with linguistic intuitionistic fuzzy information and applies to evaluating different brands of mobile phones. First, considering that a linguistic term means different things to different decision-makers, a consistency-driven optimization model for checking the additive consistent linguistic intuitionistic fuzzy preference relations (LIFPRs) is constructed by considering the PISs of a decision maker. Besides, several optimization models is built to determine the PISs of linguistic terms in LIFPRs and obtain the acceptable additive consistent LIFPRs. Second, a new definition of Hamming distance between linguistic intuitionistic fuzzy numbers (LIFNs) is developed by considering the PISs of a decision maker, and several of its desirable properties are discussed. Then, the method of deriving the weight vectors of criteria is calculated based on the proposed distance measure. Subsequently, a framework of group decision making process with LIFPRs is offered, and the application of the proposed method is illustrated by using a multi-criteria decision-making problem about evaluating different brands of mobile phones. Finally, the comparative analysis is conducted to show the feasibility of proposed group decision-making method.

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Consensus reaching with trust evolution in social network group decision making

Cited by 73 publications

References 42 publications

Classical dynamic consensus and opinion dynamics models: A survey of recent trends and methodologies

Classical dynamic consensus and opinion dynamics models: A survey of recent trends and methodologies

An approach to prevent weight manipulation by minimum adjustment and maximum entropy method in social network group decision making

A personalized individual semantics model for computing with linguistic intuitionistic fuzzy information and application in MCDM

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