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    Group Decision Making: Consensus Approaches Based on Soft Consensus Measures

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    Author's copy of chapter that coincides with published in content. (128.2Kb)
    Date
    2017-01-14
    Author
    Cabrerizo, Francisco Javier;
    Pérez, Ignacio Javier;
    Chiclana, Francisco;
    Herrera-Viedma, Enrique
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    Abstract
    A group decision making situation involves multiple decision makers communicating with others to reach a decision. In such a situation, the most important issue is to obtain a decision that is best acceptable by the decision makers, and, therefore, consensus has attained a great attention and it is a major goal of group decision making situations. To measure the closeness among the opinions given by the decision makers, different approaches have been proposed. At the beginning, consensus was meant to be a unanimous and full agreement. However, because this situation is often not reachable in practice, the use of a softer consensus, which assesses the level of agreement in a more flexible way and reflects the large spectrum of possible partial agreements, is a more reasonable approach. Soft consensus approaches better reflects a real human perception of the essence of consensus and, therefore, they have been widely used. The purpose of this contribution is to review the different consensus approaches based on soft consensus measures that have been proposed.
    Description
    Citation : Cabrerizo, F.J., Perez, I.J., Chiclana, F. and Herrera-Viedma, E. (2017) Group Decision Making: Consensus Approaches based on Soft Consensus Measures. In: Vicenc Torra et al. (Eds.): Fuzzy sets, rough sets, multisets and clustering. Springer International Publishing Switzerland, pp. 307--321.
    URI
    http://hdl.handle.net/2086/13202
    DOI
    http://dx.doi.org/10.1007/978-3-319-47557-8_18
    ISBN : 9783319475561
    ISSN : 1860-949X
    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 [2968]

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