A Feedback Mechanism Based on Granular Computing to Improve Consensus in GDM
Type of publication: Book Chapter
Year of publication: 2018
Authors: Francisco Javier Cabrerizo
Director: Francisco Chiclana, Ignacio Javier Pérez, Francisco Mata, Sergio Alonso, Enrique Herrera-Viedma
Type: Soft Computing Applications for Group Decision-making and Consensus Modeling
Editorial: Springer International Publishing
Start Page: A Feedback Mechanism Based on Granular Computing to Improve Consensus in GDM
Volumen: 357
Pagination: 371-390
ISSN number: 978-3-319-60206-6
ISBN Number: "978-3-319-60207-3"
Keywords: Consensus
Abstract: Group decision making is an important task in real world activities. It consists in obtaining the best solution to a particular problem according to the opinions given by a set of decision makers. In such a situation, an important issue is the level of consensus achieved among the decision makers before making a decision. For this reason, different feedback mechanisms, which help decision makers for reaching the highest degree of consensus possible, have been proposed in the literature. In this contribution, we present a new feedback mechanism based on granular computing to improve consensus in group decision making problems. Granular computing is a framework of designing, processing, and interpretation of information granules, which can be used to obtain a required flexibility to improve the level of consensus within the group of decision makers.
URL: https://doi.org/10.1007/978-3-319-60207-3_22
DOI: 10.1007/978-3-319-60207-3_22
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