Using indifference information in Robust Ordinal Regression

Juergen Branke, Salvatore Corrente, Salvatore Greco, Walter J. Gutjahr

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we propose an extension to Robust Ordinal Regression allowing it to take into account also preference information from questions about indifference between real and fictitious alternatives. In particular, we allow the decision maker to suggest a new alternative that is different from the existing alternatives, but equally preferable. As shown by several experiments in psychology of the decisions, choosing between alternatives is different from matching two alternatives since the two aspects involve two different reasoning strategies. Consequently,by including this type of preference information one can represent more faithfully the DM’s preferences. Such information about indifference should narrow down the set of compatible value functions much more quickly than standard pairwise comparisons, and a first simple example at least indicates that this intuition seems to be correct.
Original languageEnglish
Title of host publicationEvolutionary multi-criterion optimization
Subtitle of host publication8th International Conference, EMO 2015, Guimarães, Portugal, March 29-April 1, 2015. Proceedings, Part II
EditorsAntónio Gaspar-Cunha, Carlos Henggeler Antunes, Carlos Coello Coello
Place of PublicationSwitzerland
PublisherSpringer International Publishing
ISBN (Print)978-3-319-15891-4
Publication statusPublished - 18 Mar 2015

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


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