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dc.contributor.authorBellosta, Marie-Jo*
dc.contributor.authorKornman, Sylvie*
dc.contributor.authorVanderpooten, Daniel*
dc.date.accessioned2011-01-28T11:51:00Z
dc.date.available2011-01-28T11:51:00Z
dc.date.issued2011
dc.identifier.issn0004-3702
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/5579
dc.language.isoenen
dc.subjectPreference-based auctions
dc.subjectAutomated agents
dc.subjectMulti-attribute English reverse auctions
dc.subjectMultiple criteria decision analysis
dc.subjectNon-transitive and partial preferences
dc.subject.ddc006.3en
dc.titlePreference-based English reverse auctions
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenThis paper studies English reverse auctions within a unified framework for preference-based English reverse auctions. In this context, and particularly for electronic auctions, representing and handling the buyer's preferences, so as to enable him/her to obtain the best possible outcome, is a major issue. Existing auction mechanisms, which are based on single or multi-attribute utility functions, are only able to represent transitive and complete preferences. It is well known, however, in the preference modeling literature that more general preference structures, allowing intransitivity and incomparability, are more appropriate to capture preferences. On the other hand, we must also consider properties on the evolution and, above all, on the outcome of any auction executed by an auction mechanism. These properties, as well as properties of non-dominance and fair competition defined for multiple criteria auctions, impose restrictions on the preference relation. This leaves room for interesting preference models to be implemented within English reverse auction mechanisms.
dc.relation.isversionofjnlnameArtificial Intelligence
dc.relation.isversionofjnlvol175
dc.relation.isversionofjnlissue7-8
dc.relation.isversionofjnldate2011
dc.relation.isversionofjnlpages1449-1467
dc.relation.isversionofdoihttp://dx.doi.org/10.1016/j.artint.2010.11.015
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherElsevier
dc.subject.ddclabelIntelligence artificielleen
dc.description.ssrncandidatenon
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.relation.Isversionofjnlpeerreviewedoui
dc.date.updated2016-10-06T09:41:01Z
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