Group decision-making based on heterogeneous preference relations with self-confidence

Liu, Wenqi; Dong, Yucheng; Chiclana, Francisco; Cabrerizo, Francisco Javier; Herrera‐Viedma, Enrique

doi:10.1007/s10700-016-9254-8

Cited by 172 publications

(86 citation statements)

References 43 publications

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“…In some MADM problems, a group of decision makers might be involved, and they could provide incomplete attribute weights information (e.g., [17,19,33] …”

Section: Discussionmentioning

confidence: 99%

Strategic weight manipulation in multiple attribute decision making

Dong

Liu

Liang

et al. 2018

Omega

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201

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“…In some MADM problems, a group of decision makers might be involved, and they could provide incomplete attribute weights information (e.g., [17,19,33] …”

Section: Discussionmentioning

confidence: 99%

Strategic weight manipulation in multiple attribute decision making

Dong

Liu

Liang

et al. 2018

Omega

Self Cite

201

View full text Add to dashboard Cite

“…Definition A matrix

\overset{P}{true} = {false(p_{i j}, l_{i j} false)}_{n \times n}

is called an FPR‐SC if its elements have two components: the first component

p_{i j} \in [0, 1]

represents the preference degree of the alternative

x_{i}

over

x_{j}

, and the second element

l_{i j} \in S^{S L}

denotes the self‐confidence level associated with the first component

p_{i j}

. The following conditions are assumed:

p_{i j} + p_{j i} = 1

p_{i i} = 0.5

l_{i j} = l_{j i}

, and

l_{i i} = s_{g}

\forall i, j \in N

.…”

Section: Preliminariesmentioning

confidence: 99%

Analysis of self-confidence indices-based additive consistency for fuzzy preference relations with self-confidence and its application in group decision making

Liu

Montes

et al. 2018

Int J Intell Syst

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Preference relations have been widely used in group decision‐making (GDM) problems. Recently, a new kind of preference relations called fuzzy preference relations with self‐confidence (FPRs‐SC) has been introduced, which allow experts to express multiple self‐confidence levels when providing their preferences. This paper focuses on the analysis of additive consistency for FPRs‐SC and its application in GDM problems. To do that, some operational laws for FPRs‐SC are proposed. Subsequently, an additive consistency index that considers both the fuzzy preference values and self‐confidence is presented to measure the consistency level of an FPR‐SC. Moreover, an iterative algorithm that adjusts both the fuzzy preference values and self‐confidence levels is proposed to repair the inconsistency of FPRs‐SC. When an acceptable additive consistency level for FPRs‐SC is achieved, the collective FPR‐SC can be computed. We aggregate the individual FPRs‐SC using a self‐confidence indices‐based induced ordered weighted averaging operator. The inherent rule for aggregation is to give more importance to the most self‐confident experts. In addition, a self‐confidence score function for FPRs‐SC is designed to obtain the best alternative in GDM with FPRs‐SC. Finally, the feasibility and validity of the research are demonstrated with an illustrative example and some comparative analyses.

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“…, m} (m ≥ 2), and then, the goal is to get a consensus resolution in accordance with the most of the preferences expressed by the experts [7,33,34,36,39,56]. Fuzzy preference relations are widely utilized in the literature to depict the expert preferences [15,30,32,39,40,43].…”

Section: Gdm Problem Frameworkmentioning

confidence: 99%

An analysis of consensus approaches based on different concepts of coincidence

Moral

Tapia

Chiclana

et al. 2018

IFS

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Soft consensus is a relevant topic in group decision making problems. Soft consensus measures are utilized to reflect the different agreement degrees between the experts leading the consensus reaching process. This may determine the final decision and the time needed to reach it. The concept of coincidence has led to two main approaches to calculating the soft consensus measures, namely, concordance among expert preferences and concordance among individual solutions. In the first approach the coincidence is obtained by evaluating the similarity among the expert preferences, while in the second one the concordance is derived from the measurement of the similarity among the solutions proposed by these experts. This paper performs a comparative study of consensus approaches based on both coincidence approaches. We obtain significant differences between both approaches by comparing several distance functions for measuring expert preferences and a consensus measure over the set of alternatives for measuring the solutions provided by experts. To do so, we use the nonparametric Wilcoxon signed-ranks test. Finally, these outcomes are analyzed using Friedman mean ranks in order to obtain a quantitative classification of the considered measurements according to the convergence criterion considered in the consensus reaching process.

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Group decision-making based on heterogeneous preference relations with self-confidence

Cited by 172 publications

References 43 publications

Strategic weight manipulation in multiple attribute decision making

Strategic weight manipulation in multiple attribute decision making

Analysis of self-confidence indices-based additive consistency for fuzzy preference relations with self-confidence and its application in group decision making

An analysis of consensus approaches based on different concepts of coincidence

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