A ‘soft’ measure of consensus in the setting of partial (fuzzy) preferences

Kacprzyk, Janusz; Fedrizzi, Mario

doi:10.1016/0377-2217(88)90152-x

Cited by 368 publications

(115 citation statements)

References 32 publications

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“…Indeed, unanimity is not necessary in most real life situations. A second meaning of the concept of consensus refers to the judgement arrived at by 'most of' those concerned (http://www.merriam-webster.com), which has led to the definition and use of a new concept of consensus degree referred to as 'soft' consensus degree [16,18,26,30,31,33,40,50].…”

Section: Introductionmentioning

confidence: 99%

A statistical comparative study of different similarity measures of consensus in group decision making

Chiclana

Tapia

Moral

et al. 2013

Information Sciences

310

179

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Section: Introductionmentioning

confidence: 99%

A statistical comparative study of different similarity measures of consensus in group decision making

Chiclana

Tapia

Moral

et al. 2013

Information Sciences

310

179

View full text Add to dashboard Cite

“…The obtained fuzzy s/Q-core can be interpreted as a fuzzy set of alternatives that are not strongly defeated by Q (say, most) individuals. In order to alleviate some "rigidness" of the conventional concept of consensus, i.e., full consensus occurs only when all the individual experts agree as to all the issues, a degree of consensus model was proposed by Kacprzyk [44], then advanced by Fedrizzi and Kacprzyk [27], and Kacprzyk and Fedrizzi [45]. By introducing linguistic quantifiers into group decision making, in the new degree of consensus proposed in [47][46], full consensus may occur when most of the individual experts agree as to almost all (of the relevant) issues (alternatives, options).…”

Section: B Methodologies Of Group Decision Making Under Fuzzinessmentioning

confidence: 99%

Automated Group Decision Support Systems Under Uncertainty: Trends and Future Research

John

Zhou²,

Garibaldi

et al. 2008

IJCIR

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Abstract:In the real world, group decision making is one of the most significant and omnipresent human decision making activities. The central problem of group decision making is to develop "fair" methods for aggregating individual alternatives (options, variants, etc.) to yield a consensus decision that is most acceptable to the group as a whole. It has become a subject of intensive research due to its practical and academic significance. But group decision making is innately uncertain, so a promising framework of decision making in groups is to consider uncertainty measures in the processes of model construction, so as to achieve optimal solutions in terms of best degrees of consensus of final decisions. This paper aims to provide an in-depth overview of group decision making incorporating uncertainty from the perspective of fuzzy sets (including type-1 and type-2 fuzzy sets). Some potential new research directions on fuzzy group decision making are suggested.

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“…Furthermore, on the one hand, the chances for reaching such a full agreement are rather low, and on the other hand, unanimity is not necessary or desirable in most real life situations. A second meaning of the concept of consensus refers to the judgement arrived at by 'most of' those concerned, which has led to the definition and use of a new concept of consensus degree referred to as 'soft' consensus degree [20,26,31,[34][35][36][37][38][39][40].…”

Section: Consensus Reaching Processmentioning

confidence: 99%

Type-1 OWA methodology to consensus reaching processes in multi-granular linguistic contexts

Mata

Pérez

Zhou

et al. 2014

Knowledge-Based Systems

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A crucial step in group decision making (GDM) processes is the aggregation of individual opinions with the aim of achieving a "fair" representation of each individual within the group. In multi-granular linguistic contexts where linguistic term sets with common domain but different granularity and/or semantic are used, the methodology widely applied until now requires, prior to the aggregation step, the application of a unification process. The reason for this unification process is the lack of appropriate aggregation operators for directly aggregating uncertain information represented by means of fuzzy sets. With the recent development of the Type-1 Ordered Weighted Averaging (T1OWA) operator, which is able to aggregate fuzzy sets, alternative approaches to multi-granular linguistic GDM problems are possible. Unlike consensus models based on unification processes, this paper presents a new T1OWA based consensus methodology that can directly manage linguistic term sets with different cardinality and/or semantic without the need to perform any transformation to unify the information. Furthermore, the linguistic information could be assumed to be balanced or unbalanced in its mathematical representation, and therefore the new T1OWA approach to consensus is more general in its application than previous consensus reaching processes with muti-granular linguistic information. To test the goodness of the new consensus reaching approach, a comparative study between the T1OWA based consensus model and the unification based consensus model is carried out using six randomly generated GDM problems with balanced multi-granular information. When distance between fuzzy sets used in the T1OWA based approach is defined as the corresponding distance between their centroids, a higher final level of consensus is achieved in four out of the six cases although no significant differences were found between both consensus approaches.

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A ‘soft’ measure of consensus in the setting of partial (fuzzy) preferences

Cited by 368 publications

References 32 publications

A statistical comparative study of different similarity measures of consensus in group decision making

A statistical comparative study of different similarity measures of consensus in group decision making

Automated Group Decision Support Systems Under Uncertainty: Trends and Future Research

Type-1 OWA methodology to consensus reaching processes in multi-granular linguistic contexts

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