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Aggregation of Preferences on Criteria Importance Expressed on Various Subsets by Several Decision Makers

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Scalable Uncertainty Management (SUM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10564))

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Abstract

We are interested in the aggregation of preference information provided by several decision makers regarding the relative importance of criteria. When a large number of decision makers are involved, they have specific areas of expertise and express their preferences on possibly different subsets of criteria. The standard aggregation methods do not apply to this situation where the preference information of the decision makers have different support. We consider four possible types of preference information provided by the decision makers: binary preference relations comparing the relative importance of criteria, quaternary relations comparing differences of relative importance between pairs of criteria, classification of the difference of importance between any two criteria in predefined categories, and numerical values of the criteria weights. In the three first cases, we use an extension of the relational analysis.

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Acknowledgments

This work has been supported by the European project FP7-SEC-2013-607697, PREDICT “PREparing the Domino effect In crisis siTuations”.

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Authors and Affiliations

  1. Thales Research and Technology, Palaiseau, France

    Christophe Labreuche

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  1. Christophe Labreuche
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Correspondence to Christophe Labreuche .

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Editors and Affiliations

  1. University of Granada, Granada, Spain

    Serafín Moral

  2. University of Rennes I, Lannion, France

    Olivier Pivert

  3. University of Granada, Granada, Spain

    Daniel Sánchez

  4. University of Granada, Granada, Spain

    Nicolás Marín

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Labreuche, C. (2017). Aggregation of Preferences on Criteria Importance Expressed on Various Subsets by Several Decision Makers. In: Moral, S., Pivert, O., Sánchez, D., Marín, N. (eds) Scalable Uncertainty Management. SUM 2017. Lecture Notes in Computer Science(), vol 10564. Springer, Cham. https://doi.org/10.1007/978-3-319-67582-4_27

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  • DOI: https://doi.org/10.1007/978-3-319-67582-4_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67581-7

  • Online ISBN: 978-3-319-67582-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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