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Ranking Methods Based on Dominance Measures Accounting for Imprecision

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Algorithmic Decision Theory (ADT 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5783))

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Abstract

The additive multi-attribute utility model is widely used in multicriteria decision-making. However, it is often not easy to elicit precise values for the scaling weights representing the relative importance of criteria. In a group decision-making context a very widespread approach is to derive incomplete information, such as weight intervals or ordinal information rather than precise weights from a negotiation process. Different approaches have been proposed to deal with such situations. We advance two approaches based on dominance measures accounting for imprecise weights and compare them with other existing approaches using Monte Carlo simulation.

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Author information

Authors and Affiliations

  1. Department of Artificial Intelligence, Technical University of Madrid (UPM), 28660, Boadilla del Monte, Madrid, Spain

    Alfonso Mateos, Antonio Jiménez & José F. Blanco

Authors
  1. Alfonso Mateos
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  2. Antonio Jiménez
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  3. José F. Blanco
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Editor information

Editors and Affiliations

  1. Department of Pure and Applied Mathematics, University of Padova, Via Trieste 63, 35121, Padova, Italy

    Francesca Rossi

  2. LAMSADE-CNRS, University of Paris Dauphine, Place du Maréchal De Lattre de Tassigny, 75775, Paris Cedex 16, France

    Alexis Tsoukias

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© 2009 Springer-Verlag Berlin Heidelberg

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Mateos, A., Jiménez, A., Blanco, J.F. (2009). Ranking Methods Based on Dominance Measures Accounting for Imprecision. In: Rossi, F., Tsoukias, A. (eds) Algorithmic Decision Theory. ADT 2009. Lecture Notes in Computer Science(), vol 5783. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04428-1_29

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  • DOI: https://doi.org/10.1007/978-3-642-04428-1_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04427-4

  • Online ISBN: 978-3-642-04428-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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