Group decision making and consensus under fuzzy preferences and fuzzy majority

Kacprzyk, Janusz; Fedrizzi, Mario; Nurmi, Hannu

doi:10.1016/0165-0114(92)90107-f

Cited by 418 publications

(146 citation statements)

References 23 publications

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“…In this methodology, each individual e k is required to provide his/her pairwise comparison between the alternatives for constructing the fuzzy preference matrix P k , a degree of support to each alternative from most decision makers is obtained by aggregation before final decision is made and a degree of consensus is computed. Two lines of reasoning are available in this methodology to solve the pairwise group decision making problem: a direct approach and an indirect approach [43] [47]. Marimin et al extended this methodology including the direct approach and the indirect approach by allowing the decision makers to express their preference relations in linguistic labels rather than in numerical values [61].…”

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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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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show abstract

“…The use of aggregation functions to merge inputs into a single output has been extensively analysed in literature [11,28,33,43]. In the Decision Making context, the use of aggregation functions to derive the degree of agreement among a group of experts has been justified (see for example [11,29,31,43,47]).…”

Section: A New Consensus Measure: Correlation Consensus Degreementioning

confidence: 99%

“…In the Decision Making context, the use of aggregation functions to derive the degree of agreement among a group of experts has been justified (see for example [11,29,31,43,47]). Recall that the main aim of considering aggregation functions is to produce an overall output that can be considered representative of the aggregated values by incorporating desirable properties.…”

Section: A New Consensus Measure: Correlation Consensus Degreementioning

confidence: 99%

A new measure of consensus with reciprocal preference relations: The correlation consensus degree

González-Arteaga

Calle

Chiclana

2016

Knowledge-Based Systems

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The achievement of a 'consensual' solution in a group decision making problem depends on experts' ideas, principles, knowledge, experience, etc. The measurement of consensus has been widely studied from the point of view of different research areas, and consequently different consensus measures have been formulated, although a common characteristic of most of them is that they are driven by the implementation of either distance or similarity functions. In the present work though, and within the framework of experts' opinions modelled via reciprocal preference relations, a different approach to the measurement of consensus based on the Pearson correlation coefficient is studied. The new correlation consensus degree measures the concordance between the intensities of preference for pairs of alternatives as expressed by the experts. Although a detailed study of the formal properties of the new correlation consensus degree shows that it verifies important properties that are common either to distance or to similarity functions between intensities of preferences, it is also proved that it is different to traditional consensus measures. In order to emphasise novelty, two applications of the proposed methodology are also included. The first one is used to illustrate the computation process and discussion of the results, while the second one covers a real life application that makes use of data from Clinical Decision-Making.

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“…To avoid this situation, it is advisable that experts carry out a consensus process, where the experts discuss and modify their preferences gradually to achieve a sufficient agreement before applying the selection process. For this reason, GDM problems are usually faced by applying a consensus process and a selection process before a final solution can be given [18], [19].…”

Section: A Gdm Problemmentioning

confidence: 99%

Challenges and open questions in soft consensus models

Cabrerizo

Chiclana

Urena

et al. 2013

2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS)

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Abstract-In group decision making problems, given the importance of obtaining an accepted solution by the whole group, the consensus has attained a great attention and it is virtually a major goal of these problems. Consensus, as traditionally meant to be a full and unanimous agreement, is often not reachable in practice. A more realistic approach is to use softer consensus measures, which assess the consensus degree in a more flexible way, and therefore reflect the large spectrum of possible partial agreements, guiding the consensus process until widespread agreement is achieved among experts. In particular, the interpretation of the consensus based on the concept of fuzzy majority has been used in the most of the consensus models proposed in the literature, as it is more human-consistent and suitable for reflecting human perceptions of the meaning of consensus. However, there are still some open questions to be addressed. In this paper, we are going to highlight some issues in order to focus researcher's attention on new problems that arise when using consensus models based on soft consensus measures in real-world applications.

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Group decision making and consensus under fuzzy preferences and fuzzy majority

Cited by 418 publications

References 23 publications

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

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

A new measure of consensus with reciprocal preference relations: The correlation consensus degree

Challenges and open questions in soft consensus models

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