Representing incomplete preference information by probability distributions

Risto Lahdelma*, Pekka Salminen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationAIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
EditorsV. Devedzic
PublisherACTA Press
Number of pages8
ISBN (Print)9780889866317
Publication statusPublished - 2007
MoE publication typeA4 Article in a conference publication
EventIASTED International Conference on Artificial Intelligence and Applications - Innsbruck, Austria
Duration: 12 Feb 200714 Feb 2007


ConferenceIASTED International Conference on Artificial Intelligence and Applications


  • Decision support
  • Knowledge representation
  • Multicriteria analysis
  • Preference information

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