Nombre
Sergio
Apellidos
Alonso
Keywords
Resumen
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.
Year of Publicaion
2018
Book Title
Soft Computing Applications for Group Decision-making and Consensus Modeling
Volume
357
Chapter
A Feedback Mechanism Based on Granular Computing to Improve Consensus in GDM
Pagination
371-390
Publisher
Springer International Publishing
ISBN
978-3-319-60206-6
ISBN Number
"978-3-319-60207-3"
URL
https://doi.org/10.1007/978-3-319-60207-3_22
DOI
10.1007/978-3-319-60207-3_22
Cabrerizo, F. J., et al. «A Feedback Mechanism Based On Granular Computing To Improve Consensus In Gdm». Soft Computing Applications For Group Decision-Making And Consensus Modeling, Springer International Publishing, 2018, pp. 371-390.
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Tipo participación
Ponente
Year of publication
2007
Conference Name
Modeling Decisions for Artificial Intelligence. MDAI 2007
Volume
4617
Edition
Lecture Notes in Computer Science
Pagination
80-91
Date Published
August 16-18th
Publisher
Springer, Berlin, Heidelberg
Place Published
Fukuoka (Japan)
ISBN Number
978-3-540-73728-5
URL
https://link.springer.com/chapter/10.1007/978-3-540-73729-2_8#aboutcontent
DOI
https://doi.org/10.1007/978-3-540-73729-2_8
Chiclana, F., et al. «Group Decision Making: From Consistency To Consensus». August 16-18th. Modeling Decisions For Artificial Intelligence. Mdai 2007, Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 2007, pp. 80-91.
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