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Representing Incomplete Preference Information by Probability Distributions

Risto Lahdelma, Pekka Salminen, Representing Incomplete Preference Information by Probability Distributions. In: Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (AIA 2007), 590-597, ACTA Press, 2007.

Abstract:

Preference information in real-life multi-criteria decision aiding
(MCDA) problems is always more or less inaccurate, imprecise or uncertain. Sometimes preference information can be missing. We discuss methods for
representing different kinds of incomplete preference information through probability distributions for preference parameters and show how to treat this information in MCDA methods through simulation techniques. The techniques are suitable for different kinds of decision models, such as utility/value function models, prospect theory, reference point methods, and outranking methods.

BibTeX entry:

@INPROCEEDINGS{inpLaSa07a,
  title = {Representing Incomplete Preference Information by Probability Distributions},
  booktitle = {Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (AIA 2007)},
  author = {Lahdelma, Risto and Salminen, Pekka},
  publisher = {ACTA Press},
  pages = {590-597},
  year = {2007},
  keywords = {Knowledge representation, Decision support, Preference information, Multicriteria analysis},
}

Belongs to TUCS Research Unit(s): Algorithmics and Computational Intelligence Group (ACI)

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