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Multi-Granular Large Scale Group Decision-Making Method with a New Consensus Measure Based on Clustering of Alternatives in Modifiable Scenarios

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Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence (IEA/AIE 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13343))

Abstract

Because a decision-making process in the real world involves more people and it is more heterogeneous nowadays, this paper develops an innovative large scale multi-granular decision-making method that is intended to be used in situations where many experts are involved, who do not have to provide their opinions about the alternatives at the same time. The proposed method has three main characteristics. Firstly, experts provide their judgments using the numerical labels set of their choice. Secondly, experts can join the decision-making process in any of its rounds. Thirdly, this method performs a clustering that sorts the experts according to their judgments, obtaining how many experts belong to the same group and the consensus among them. These two parameters allow the weight of the group to be adjusted. Finally, the method uses inter-group consensus measures to verify that the groups agree with the decision made.

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Acknowledgments

This work was supported by FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades / Proyecto B-TIC-590-UGR20, by the Andalusian Government through the project P20_00673, and by the project PID2019-103880RB-I00 funded by MCIN / AEI / 10.13039/501100011033.

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Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, Andalusian Research Institute of Data Science and Computational Intelligence, DaSCI, University of Granada, 18071, Granada, Spain

    José Ramón Trillo, Ignacio Javier Pérez, Enrique Herrera-Viedma, Juan Antonio Morente-Molinera & Francisco Javier Cabrerizo

Authors
  1. José Ramón Trillo
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  2. Ignacio Javier Pérez
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  3. Enrique Herrera-Viedma
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  4. Juan Antonio Morente-Molinera
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  5. Francisco Javier Cabrerizo
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Corresponding author

Correspondence to Francisco Javier Cabrerizo .

Editor information

Editors and Affiliations

  1. i-SOMET, Inc., Morioka-shi, Iwate, Japan

    Hamido Fujita

  2. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China

    Philippe Fournier-Viger

  3. Texas State University, San Marcos, TX, USA

    Moonis Ali

  4. Shanghai University of Finance and Economics, Shanghai, China

    Yinglin Wang

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Trillo, J.R., Pérez, I.J., Herrera-Viedma, E., Morente-Molinera, J.A., Cabrerizo, F.J. (2022). Multi-Granular Large Scale Group Decision-Making Method with a New Consensus Measure Based on Clustering of Alternatives in Modifiable Scenarios. In: Fujita, H., Fournier-Viger, P., Ali, M., Wang, Y. (eds) Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence. IEA/AIE 2022. Lecture Notes in Computer Science(), vol 13343. Springer, Cham. https://doi.org/10.1007/978-3-031-08530-7_63

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  • DOI: https://doi.org/10.1007/978-3-031-08530-7_63

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08529-1

  • Online ISBN: 978-3-031-08530-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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