Consensus reaching in social network DeGroot Model: The roles of the Self-confidence and node degree

Ding, Zhuo; Xia, Chen; Dong, Yucheng; Herrera, Francisco

doi:10.1016/j.ins.2019.02.028

Cited by 98 publications

(38 citation statements)

References 47 publications

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“…However, it is obvious that under different confidence levels, the speeds of weight change should be different. Ding et al (2019) gave a weight determination model that the weight changes non-linearly with the self-confidence level. However, if the experts' weights are calculated in this way, only the evaluations provided by the experts with high self-confidence levels will be considered, while the weights of other experts are very low and the influence of their opinions on the decision-making results will be ignored.…”

Section: Weight Determination With Endo-confidencementioning

confidence: 99%

“…Thus, considering the effect of confidence on the decision-making results is essential. Consensus models with self-confidence have been presented and Ding et al (2019) alleged that the self-confidence level indeed affect the consensus reaching process. According to Liu et al (2017) and Liu et al (2018) proposed an iteration-based consensus with self-confidence multiplicative preference relations.…”

Section: Introductionmentioning

confidence: 99%

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Large-scale consensus with endo-confidence under probabilistic linguistic circumstance and its application

Zhang

et al. 2021

Economic Research-Ekonomska Istraživanja

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In real decision-making problems, decision makers (DMs) usually select the most potential project from several ones. However, they unconsciously show different confidence levels in decisionmaking process because they come from various backgrounds and have different experiences, etc., which affects the decision results. Moreover, the probabilistic linguistic term set, which not only includes the linguistic expressions used by DMs in their daily life but also contains the probability for each linguistic term, can well portray the real perceptions of DMs for the projects. Furthermore, large-scale consensus has gradually been a popular way to effectively solve complex decision-making problems. To sum up, in this paper, we are dedicated to constructing a largescale consensus model considering the confidence levels of DMs under probabilistic linguistic circumstance. Firstly, the endo-confidence is defined and measured by DM's probabilistic linguistic information. Then, the DMs are clustered according to the similarities of both evaluation information and the endo-confidence levels. Both evaluation of the non-consensus cluster and evaluation integrated by the clusters with higher endo-confidence level than this non-consensus cluster are used as the reference to adjust its evaluation information. Then, a case study and the comparative analysis are carried out. Finally, some conclusions and future work are given.

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Section: Weight Determination With Endo-confidencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Large-scale consensus with endo-confidence under probabilistic linguistic circumstance and its application

Zhang

et al. 2021

Economic Research-Ekonomska Istraživanja

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“…In multicriteria decision-making (MCDM) problems, we need to choose the best alternative/alternatives according to several determined criteria from a set of alternatives [1][2][3]. Different approaches have been developed from different perspectives, where preference relations (PRs) are one of the commonly used technologies.…”

Section: Introductionmentioning

confidence: 99%

“…However, there are still some important issues that need to be further studied. (1) The concept of additive/multiplicatively consistent HFPRs. As HFPRs, additive/multiplicatively consistent HFPRs develop in considering one FPR derived from HFPRs, which may lead to information loss [50]; develops in considering ordered FPRs derived from normalized HFPRs may distort the preference information [50,51]; and develops in considering all possible FPRs in HFPRs seems too restrictive [31].…”

Section: Introductionmentioning

confidence: 99%

Approaches for multicriteria decision-making based on the hesitant fuzzy best–worst method

Niu

Chen

et al. 2021

Complex Intell. Syst.

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Hesitant fuzzy preference relations (HFPRs) have been widely applied in multicriteria decision-making (MCDM) for their ability to efficiently express hesitant information. To address the situation where HFPRs are necessary, this paper develops several decision-making models integrating HFPRs with the best worst method (BWM). First, consistency measures from the perspectives of additive/multiplicative consistent hesitant fuzzy best worst preference relations (HFBWPRs) are introduced. Second, several decision-making models are developed in view of the proposed additive/multiplicatively consistent HFBWPRs. The main characteristic of the constructed models is that they consider all the values included in the HFBWPRs and consider the same and different compromise limit constraints. Third, an absolute programming model is developed to obtain the decision-makers’ objective weights utilizing the information of optimal priority weight vectors and provides the calculation of decision-makers’ comprehensive weights. Finally, a framework of the MCDM procedure based on hesitant fuzzy BWM is introduced, and an illustrative example in conjunction with comparative analysis is provided to demonstrate the feasibility and efficiency of the proposed models.

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“…In the literature, a wide variety of opinion dynamics approaches have been proposed, among which, we can highlight the following: voter model [10,11], Sznajd model [12] majority-rule model [13,14], DeGroot model [15], Deffuant model [16], Hegselmann-Krause (HK) model [17], OCR model [18], continuous opinions and discrete action (CODA) models [19]. In addition to the above general opinion dynamics models, some scholars have attempted to extend the HK model [20,21], discuss the strategy to support the consensus reaching [22] and the opinion control [23].…”

mentioning

confidence: 99%

Consensus Reaching With Time Constraints and Minimum Adjustments in Group With Bounded Confidence Effects

Liang

Dong

Ding

et al. 2020

IEEE Trans. Fuzzy Syst.

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In the bounded confidence model it is widely known that individuals rely on the opinions of their close friends or people with similar interests. Meanwhile, the decision maker always hopes that the opinions of individuals can reach a consensus in a required time. Therefore, with this idea in mind, this paper develops a consensus reaching model with time constraints and minimum adjustments in a group with bounded confidence effects. In the proposed consensus approach, the minimum adjustments rule is used to modify the initial opinions of individuals with bounded confidence, which can further influence the opinion evolutions of individuals to reach a consensus in a required time. The properties of the model are studied, and detailed numerical examples and comparative simulation analysis are provided to justify its feasibility. Index Terms-Consensus, time constraints, bounded confidence, minimum adjustments, opinion dynamics, group decision making I. INTRODUCTION onsensus reaching is an important research problem that involves in the preference evolution and aggregation of a group of individuals [1-3], and it is present in a wide range of application areas [4-6], such as

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Consensus reaching in social network DeGroot Model: The roles of the Self-confidence and node degree

Cited by 98 publications

References 47 publications

Large-scale consensus with endo-confidence under probabilistic linguistic circumstance and its application

Large-scale consensus with endo-confidence under probabilistic linguistic circumstance and its application

Approaches for multicriteria decision-making based on the hesitant fuzzy best–worst method

Consensus Reaching With Time Constraints and Minimum Adjustments in Group With Bounded Confidence Effects

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