Inducing probability distributions on the set of value functions by Subjective Stochastic Ordinal Regression

Salvatore Corrente, Salvatore Greco, Miłosz Kadziński, Roman Słowiński

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Abstract

Ordinal regression methods of Multiple Criteria Decision Aiding (MCDA) take into account one, several, or all value functions compatible with the indirect preference information provided by the Decision Maker (DM). When dealing with multiple criteria ranking problems, typically, this information is a series of holistic and certain judgments having the form of pairwise comparisons of some reference alternatives, indicating that alternative a is certainly either preferred to or indifferent with alternative b . In some decision situations, it might be useful, however, to additionally account for uncertain pairwise comparisons interpreted in the following way: although the preference of a over b is not certain, it is more credible than preference of b over a . To handle certain and uncertain preference information, we propose a new approach that builds a probability distribution over the space of all value functions compatible with the DM’s certain holistic judgments. A didactic example shows the applicability of the proposed approach.
Original languageEnglish
Pages (from-to)26-36
JournalKnowledge-Based Systems
Volume112
Early online date29 Aug 2016
DOIs
Publication statusPublished - 1 Nov 2016

Keywords

  • Multiple criteria decision aiding
  • Ordinal regression
  • Stochastic multiobjective acceptability analysis
  • Multi-attribute value function
  • Uncertain preference information
  • Probability distribution

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