A review of soft consensus models in a fuzzy environment

Liu, Xinyue; Cabrerizo, Francisco Javier; Kacprzyk, Janusz; Pedrycz, Witold

doi:10.1016/j.inffus.2013.04.002

Cited by 561 publications

(254 citation statements)

References 58 publications

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“…Given a reciprocal preference relation on a set of alternatives, the concept of non-dominance degree introduced by Orlovsky [51] has been extensively used to rank the alternatives [10,23,38,61,63,65,66]. In the following, and in order to improve the understanding of the proposed correlation consensus degree, the consistency of the correlation consensus degree with Orlovsky's non-dominance degree is proved.…”

Section: Consistency Under Maximum Correlation Consensus Degreementioning

confidence: 99%

“…On the one hand, establishing and characterising different methodologies to measure consensus have been addressed from a Social Choice perspective [1,3,13]. On the other hand, within the Decision Making Theory framework, modelling group decision making problems in order to reach a higher level of cohesiveness has been managed successfully [15,32,34,38,39,65]. Outside of these main areas, it is possible to find other methodologies that use the idea of consensus in different ways to the aforementioned ones, with [41,46] being representative examples of these methodologies.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Consistency Under Maximum Correlation Consensus Degreementioning

confidence: 99%

Section: Introductionmentioning

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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“…], it is preferable to include mechanism to check the agreement among the decision makers before obtaining a solution ensuring that enough agreement have been achieved. Those mechanisms are widely known as consensus processes [13] and they consist on iterative negotiations where the decision makers agree to change their testimonies following the advice given by a moderator, which calculates the agreement or consensus degree at each iteration [6]. If enough agreement have been reached the consensus process stops and the aggregation and the exploitation is carried out.…”

Section: Consensusmentioning

confidence: 99%

GDM-VieweR: A New Tool in R to Visualize the Evolution of Fuzzy Consensus Processes

Ureña

Cabrerizo

Chiclana

et al. 2015

Communications in Computer and Information Science

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Abstract. With the incorporation of web 2.0 frameworks the complexity of decision making situations has exponentially increased, involving in many cases many experts, and a huge number of different alternatives. In the literature we can find a great deal of methodologies to assist multi-person decision making. However these classical approaches are not prepared to deal with such a huge complexity and there is a lack of tools that support the decision processes providing some graphical information. Therefore the main objective of this contribution is to present an open source tool developed in R to provide a quick insight of the evolution of the decision making by means of meaningful graphical representations. Thanks to the modular architecture of this solution this tool can be easily adapted to work with various Group decision making methodologies.

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“…In [18] a review of consensus models in a fuzzy environment can be found. There is, nowadays, a wide range of areas of application for these methods, from managerial to medical or engineering [6,8,10,30].…”

Section: Introductionmentioning

confidence: 99%

A consensus model for Delphi processes with linguistic terms and its application to chronic pain in neonates definition

Agell

Ganzewinkel

Sánchez

et al. 2015

Applied Soft Computing

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This paper proposes a new model of consensus based on linguistic terms to be implemented in Delphi processes. The model of consensus involves qualitative reasoning techniques and is based on the concept of entropy. The proposed model has the ability to reach consensus automatically without the need for either a moderator or a final interaction among panelists. In addition, it permits panelists to answer with different levels of precision depending on their knowledge on each question. The model defined has been used to establish the relevant features for the definition of a type of chronic disease. A real-case application conducted in the Department of Neonatology of Máxima Medical Center in The Netherlands is presented. This application considers the opinions of stakeholders of neonate healthcare in order to reach a final consensual definition of chronic pain in neonates.

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A review of soft consensus models in a fuzzy environment

Cited by 561 publications

References 58 publications

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

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

GDM-VieweR: A New Tool in R to Visualize the Evolution of Fuzzy Consensus Processes

A consensus model for Delphi processes with linguistic terms and its application to chronic pain in neonates definition

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