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    A Feedback Mechanism Based on Granular Computing to Improve Consensus in GDM

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    Author's copy of accepted paper. (466.7Kb)
    Date
    2017-07-02
    Author
    Cabrerizo, Francisco Javier;
    Chiclana, Francisco;
    Perez, Ignacio Javier;
    Mata, Francisco;
    Alons, Sergio;
    Herrera-Viedma, Enrique
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    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.
    Description
    Citation : Cabrerizo, F.J. et al. (2017) A Feedback Mechanism Based on Granular Computing to Improve Consensus in GDM. In Collan, Mikael, Kacprzyk, Janusz (Eds.) Soft computing applications for group decision-making and consensus modeling. Springer, pp. 371-390
    URI
    http://hdl.handle.net/2086/14297
    DOI
    http://dx.doi.org/10.1007/978-3-319-60207-3_22
    ISBN : 9783319602066
    9783319602073
    Research Group : Centre for Computational Intelligence
    Research Institute : Institute of Artificial Intelligence (IAI)
    Peer Reviewed : Yes
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    • School of Computer Science and Informatics [2680]

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