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Building Additive Utilities in the Presence of Non-Monotonic Preferences

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Advances in Multicriteria Analysis

Part of the book series: Nonconvex Optimization and Its Applications ((NOIA,volume 5))

Abstract

Ordinal regression is used for modelling the problem of multiattribute choice in the presence of non-monotonic preferences. The main model presented in this paper estimates an additive utility function with non-monotonic partial utilities by analysing some ordinal inter-alternative information provided by the decision maker. Then the model is extended to fit in special conditions where intensity-of-preference information is available or concavity of the utility model is required.

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References

  • Despotis, D.K., D. Yannacopoulos, C. Zopounidis (1990) A review of the UTA multicriteria method and some improvements, Foundations of Computing and Decision Sciences, 15, 2, 63 - 76.

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Author information

Authors and Affiliations

  1. Dept. of Informatics, University of Piraeus, 80 Karaoli & Dimitrioy St., 18534, Piraeus, Greece

    D. K. Despotis

  2. Technical University of Crete DSS Lab., University Campus, 73100, Chania, Greece

    C. Zopounidis

Authors
  1. D. K. Despotis
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  2. C. Zopounidis
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Editor information

Editors and Affiliations

  1. Dept. of Industrial and Systems Engineering, University of Florida, 303 Weil Hall, 32611, Gainesville, FL, USA

    Panos M. Pardalos

  2. DSS Laboratory, Dept. of Production Engineering and Management, Technical University of Crete, University Campus, 73100, Chania, Greece

    Yannis Siskos  & Constantin Zopounidis  & 

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© 1995 Springer Science+Business Media Dordrecht

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Despotis, D.K., Zopounidis, C. (1995). Building Additive Utilities in the Presence of Non-Monotonic Preferences. In: Pardalos, P.M., Siskos, Y., Zopounidis, C. (eds) Advances in Multicriteria Analysis. Nonconvex Optimization and Its Applications, vol 5. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-2383-0_7

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  • DOI: https://doi.org/10.1007/978-1-4757-2383-0_7

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-4748-2

  • Online ISBN: 978-1-4757-2383-0

  • eBook Packages: Springer Book Archive

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