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Fuzzy Logic and Information Fusion pp 135–153Cite as

Soft Consensus Models in Group Decision Making

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 339))

Abstract

In group decision making problems, when a consensual solution is required, a natural question is how to measure the closeness among experts’ opinions in order to obtain the consensus level. To do so, different approaches have been proposed. Following this research line, several authors have introduced hard consensus measures varying between 0 (no consensus or partial consensus) and 1 (full consensus or complete agreement). However, consensus as a full and unanimous agreement is far from being achieved in real situations. So, in practice, a more realistic approach is to use some softer consensus measures, which assess the consensus degree in a more flexible way reflecting better all possible partial agreements obtained through the process. The aim of this chapter is to identify and describe the different existing approaches to compute soft consensus measures in fuzzy group decision making problems. Additionally, we analyze the current models and new challenges on this field.

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Acknowledgments

This paper has been developed with the financing of FEDER funds in TIN2013-40658-P and Andalusian Excellence Project TIC-5991.

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Authors and Affiliations

  1. Department of Computer Sciences and Engineering, University of Cadiz, 11519, Puerto Real, Spain

    Ignacio Javier Perez

  2. Department of Software Engineering and Computer Systems, Distance Learning University of Spain, 28040, Madrid, Spain

    Francisco Javier Cabrerizo

  3. Department of Software Engineering, University of Granada, 18071, Granada, Spain

    Sergio Alonso

  4. Faculty of Technology, Centre for Computational Intelligence, De Montfort University, Leicester, LE1 9BH, UK

    Francisco Chiclana

  5. Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain

    Enrique Herrera-Viedma

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  1. Ignacio Javier Perez
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  2. Francisco Javier Cabrerizo
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  3. Sergio Alonso
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  4. Francisco Chiclana
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  5. Enrique Herrera-Viedma
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Correspondence to Enrique Herrera-Viedma .

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  1. University of Alcalá, Alcalá de Henares, Spain

    Tomasa Calvo Sánchez

  2. University of Balearic Islands, Palma, Spain

    Joan Torrens Sastre

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Perez, I.J., Cabrerizo, F.J., Alonso, S., Chiclana, F., Herrera-Viedma, E. (2016). Soft Consensus Models in Group Decision Making. In: Calvo Sánchez, T., Torrens Sastre, J. (eds) Fuzzy Logic and Information Fusion. Studies in Fuzziness and Soft Computing, vol 339. Springer, Cham. https://doi.org/10.1007/978-3-319-30421-2_10

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