Reaching a minimum adjustment consensus in social network group decision-making

Cheng, Dong; Cheng, Faxin; Zhou, Zhili; Wu, Yong

doi:10.1016/j.inffus.2020.01.004

Cited by 109 publications

(25 citation statements)

References 51 publications

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“…After all rounds of discussion, the DM weights (ut) j are obtained at each stage in virtue of Eqs. (12) and (13). The results are shown in Table 1.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…These phenomena have led to the traditional LSGDM model urgently needs to consider the real life situation and practical significance [4]. LSGDM based on SNA has been a quite popular research topic during the last decade [5][6][7][8][9][10][11][12][13]. Recently, Wu et al [14] proposed a two-stage solution based on trust networks information modeling, which not only reduce the complexity for LSGDM problems, but also improve the reliability of the decision results as well.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Relationship Network Analysis Based on Louvain Algorithm for Large-Scale Group Decision Making

Li¹,

Qin²,

Jiang³

et al. 2021

IJCIS

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In most existing large-scale group decision making (LSGDM) problems, the relationships between decision makers (DMs) are usually ignored or regarded as static. However, in many cases, the results of LSGDM are dynamically influenced by the relationship between group members. To address this issue, a dynamic relationship network analysis method based on Louvain algorithm is proposed in this paper. First, each DM could be considered as a node to construct a relationship network, which dynamically change the individual opinion by the definition of correction index to eliminate subjective factors. Second, the node centrality and subgroup cohesion are defined and the Louvain algorithm is used to divide DMs into several subgroups to measure the importance of each node and subgroup. Then, the termination conditions of the discussion are determined by measuring the consensus and stability of the group decision information. Moreover, stage weight function is defined to assign weights to discussions at different stages and obtain the final results. An illustrative example is provided to prove the feasibility of the proposed model. Sensitivity analysis is given to show the stability of correction index and stage weight function. Finally, the comparative analysis is performed to illustrate its feasibility and effectiveness of the method.

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“…After all rounds of discussion, the DM weights (ut) j are obtained at each stage in virtue of Eqs. (12) and (13). The results are shown in Table 1.…”

Section: Problem Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Relationship Network Analysis Based on Louvain Algorithm for Large-Scale Group Decision Making

Li¹,

Qin²,

Jiang³

et al. 2021

IJCIS

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show abstract

“…• Modeling social relationships in GDM, by integrating and exploiting relationship information e.g. trust between experts, in GDM problems and using them to guide CRPs [15], [103], [104]. • Large-scale GDM problems [85], [105], in which decision groups become larger, more diverse, and additional difficulties arise such as the necessity of clustering a large group into subgroups or identifying strategic manipulative behaviors that may bias the CRP [16], [104], [106], [107], among others.…”

Section: A Multi-criteria Decision-making (Mcdm)mentioning

confidence: 99%

Revisiting Fuzzy and Linguistic Decision Making: Scenarios and Challenges for Making Wiser Decisions in a Better Way

Liu

Palomares

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

168

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This paper provides a brief tour through the main fuzzy and linguistic decision-making trends, studies, methodologies and models developed in the last 50 years. Fuzzy and linguistic decision-making approaches allow to address complex real-world decision problems where humans exhibit vagueness, imprecision and/or use natural language to assess decision alternatives, criteria, etc. The aim of this paper is threefold. Firstly, the main fuzzy set theory and computing with words based representation paradigms of decision information, with their different levels of expressive richness and complexity, are reviewed. Secondly, three core decision-making frameworks are examined:(1) multi-criteria decision-making, (2) group consensus-driven decision-making, and (3) multi-person multi-criteria decisionmaking. Thirdly, the paper discusses new complex decisionmaking frameworks that have emerged in recent years, where decisions are guided by the "wisdom of the crowd": their associated challenges are highlighted and considerations on much needed key guidelines for future research in the field are provided.

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“…For an LSGDM problem, to manage disagreements among DMs based on the social network, it is necessary to construct a complete network. Scholars have studied this topic from different perspectives (Cheng et al, 2020; Liu et al, 2019; T. Wu et al, 2019). However, they did not consider overlapping networks.…”

Section: A Trust Propagation Process Based On the Ogmmentioning

confidence: 99%

“…Scholars have studied this topic from different perspectives (Cheng et al, 2020;Liu et al, 2019;T. Wu et al, 2019).…”

Section: A Trust Propagation Process Based On the Ogmmentioning

confidence: 99%

An overlap graph model for large‐scale group decision making with social trust information considering the multiple roles of experts

2020

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Social network analysis is an efficient tool to investigate the relationships of decision‐makers in large‐scale group decision making (LSGDM). Existing social network‐based LSGDM studies generally assumed that each decision‐maker has a single role or belongs to only one subgroup. The assumption that a decision‐maker has multiple roles or belongs to multiple subgroups is rarely taken into consideration. In this regard, this study proposes an overlap graph model (OGM) in which decision‐makers can participate in the decision‐making process in multiple roles to solve LSGDM problems with social trust information. In the OGM, decision‐makers are firstly divided into two types: multiple‐role decision‐makers and single‐role decision‐makers. Since it is unpractical for a decision‐maker to evaluate all others in a LSGDM problem, we then investigate how to construct a complete social trust network based on an Agent mechanism. A two‐stage consensus reaching process is proposed to reduce the discrepancies among decision‐makers: The first stage is for single‐role decision‐makers within a subgroup while the second stage is for Agents and multiple‐role decision‐makers. Finally, an illustrative example regarding selecting treatment plans for critical patients in COVID‐19 is provided to test the applicability and rationality of the proposed model.

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Reaching a minimum adjustment consensus in social network group decision-making

Cited by 109 publications

References 51 publications

Dynamic Relationship Network Analysis Based on Louvain Algorithm for Large-Scale Group Decision Making

Dynamic Relationship Network Analysis Based on Louvain Algorithm for Large-Scale Group Decision Making

Revisiting Fuzzy and Linguistic Decision Making: Scenarios and Challenges for Making Wiser Decisions in a Better Way

An overlap graph model for large‐scale group decision making with social trust information considering the multiple roles of experts

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