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International Summer School on Aggregation Operators

AGOP 2017: Aggregation Functions in Theory and in Practice pp 211–222Cite as

Generating Recommendations in GDM with an Allocation of Information Granularity

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 581))

Abstract

A Group decision making process is carried out when human beings jointly make an election from a possible collection of alternatives. Here, a question of importance is to avoid winners and losers, in the sense that the choice is not any more attributable to any single individual, but all group members contribute to the decision. For this reason, the agreement or consensus achieved among all the individuals should be as high as possible. In this contribution, a feedback mechanism is presented in order to increase the consensus achieved among the decision makers involved in this kind of problems. It is based on granular computing, which is utilized here to provide the necessary flexibility to increase the consensus. The feedback mechanism is able to deal with heterogeneous contexts, that is, contexts in which the decision makers have importance degrees considering their capacity or talent to handle the problem.

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Acknowledement

The authors would like to acknowledge FEDER financial support from the Projects TIN2013-40658-P and TIN2016-75850-P.

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Authors and Affiliations

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

    Francisco Javier Cabrerizo & Enrique Herrera-Viedma

  2. Universidad Internacional de La Rioja (UNIR), Logroño, Spain

    Juan Antonio Morente-Molinera

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

    Sergio Alonso

  4. Department of Computer Science and Engineering, University of Cádiz, Cádiz, Spain

    Ignacio Javier Pérez & Raquel Ureña

Authors
  1. Francisco Javier Cabrerizo
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  2. Juan Antonio Morente-Molinera
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  3. Sergio Alonso
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  4. Ignacio Javier Pérez
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  5. Raquel Ureña
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  6. Enrique Herrera-Viedma
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Corresponding author

Correspondence to Francisco Javier Cabrerizo .

Editor information

Editors and Affiliations

  1. School of Informatics, University of Skövde, Skövde, Sweden

    Vicenç Torra

  2. Department of Mathematics and Descriptive Geometry, Slovak University of Technology, Bratislava, Slovakia

    Radko Mesiar

  3. KERMIT Research Unit Knowledge-based Systems, Ghent University, Ghent, Belgium

    Bernard De Baets

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Cabrerizo, F.J., Morente-Molinera, J.A., Alonso, S., Pérez, I.J., Ureña, R., Herrera-Viedma, E. (2018). Generating Recommendations in GDM with an Allocation of Information Granularity. In: Torra, V., Mesiar, R., Baets, B. (eds) Aggregation Functions in Theory and in Practice. AGOP 2017. Advances in Intelligent Systems and Computing, vol 581. Springer, Cham. https://doi.org/10.1007/978-3-319-59306-7_22

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  • DOI: https://doi.org/10.1007/978-3-319-59306-7_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59305-0

  • Online ISBN: 978-3-319-59306-7

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