Analyzing consensus approaches in fuzzy group decision making: advantages and drawbacks

By F. Cabrerizo, Juan Manuel Moreno, Ignacio Javier Pérez, Enrique Herrera-Viedma. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 2010

314 citations recorded by the source corpus.

Two processes are necessary to solve group decision making problems: a consensus process and a selection process. The consensus process is necessary to obtain a final solution with a certain level of agreement between the experts, while the selection process is necessary to obtain such a final solution. Clearly, it is preferable that the set of experts reach a high degree of consensus before applying the selection process. In order to measure the degree of consensus, different approaches have been proposed. For example, we can use hard consensus measures, which vary between 0 (no consensus or partial consensus) and 1 (full consensus), or soft consensus measures, which assess the consensus degree in a more flexible way. The aim of this paper is to analyze the different consensus approaches in fuzzy group decision making problems and discuss their advantages and drawbacks. Additionally, we study the future trends.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/2478863bf415823fd33bb1c07a73e9d804f8ff6e

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Citations in the corpus

A New Consensus Model for Group Decision Making Problems With Non-Homogeneous Experts (2014, background): “Several authors have provided interesting results on GDM with the help of fuzzy sets theory [2], [3].”
Trust based consensus model for social network in an incomplete linguistic information context (2015, background): “On the other hand, another key issue in this type of decision making problem is how to reach consensus to derive the decision solution [6] [28].”
A review of soft consensus models in a fuzzy environment (2014, methodology): “Then, we use the coincidence concept to compute soft consensus measures [52] according to three different approaches [53]: 1.”
Consensus reaching in social network group decision making: Research paradigms and challenges (2018, background): “It is often based on two kinds of consensus rules: (a) Identification rule (IR) and direction rule (DR) [44,45].”
A statistical comparative study of different similarity measures of consensus in group decision making (2013, background): “The evaluation of consensus necessarily implies the computation and aggregation of the ‘distance’ representing disagreement between the opinions (preferences) of each pair of experts on each pair of alternatives [3].”
Managing the consensus in group decision making in an unbalanced fuzzy linguistic context with incomplete information (2010, background): “Normally, the consensus process is guided by a human figure called moderator [7, 22, 25, 36], who is a person that does not participate in the discussion but monitors the agreement in each moment of the consensus process and is in charge of supervising and addressing the consensus process toward suc”