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dc.contributor.authorLang, Jérôme
dc.contributor.authorSkowron, Piotr
dc.date.accessioned2019-07-04T07:55:38Z
dc.date.available2019-07-04T07:55:38Z
dc.date.issued2018
dc.identifier.issn0004-3702
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/19150
dc.language.isoenen
dc.subjectProportional representationen
dc.subjectDiversityen
dc.subjectMultiwinner electionsen
dc.subjectApportionmenten
dc.subjectRecommendation systemsen
dc.subjectAlgorithmsen
dc.subjectComputational complexityen
dc.subjectApproximation algorithmsen
dc.subject.ddc006.3en
dc.titleMulti-attribute proportional representationen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenWe consider the following problem in which a given number of items has to be chosen from a predefined set. Each item is described by a vector of attributes and for each attribute there is a desired distribution that the selected set should have. We look for a set that fits as much as possible the desired distributions on all attributes. An example of application is the choice of members for a representative committee, where candidates are described by attributes such as gender, age and profession, and where we look for a committee that for each attribute offers a certain representation, i.e., a single committee that contains a certain number of young and old people, certain number of men and women, certain number of people with different professions, etc. Another example of application is the selection of a common set of items to be used by a group of users, where items are labelled by attribute values. With a single attribute the problem collapses to the apportionment problem for party-list proportional representation systems (in such a case the value of the single attribute would be a political affiliation of a candidate). We study the properties of the associated subset selection rules, as well as their computational complexity.en
dc.relation.isversionofjnlnameArtificial Intelligence
dc.relation.isversionofjnlvol263en
dc.relation.isversionofjnldate2018-10
dc.relation.isversionofjnlpages74-106en
dc.relation.isversionofdoi10.1016/j.artint.2018.07.005en
dc.relation.isversionofjnlpublisherElsevieren
dc.subject.ddclabelIntelligence artificielleen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenonen
dc.description.halcandidateouien
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewedouien
dc.relation.Isversionofjnlpeerreviewedouien
dc.date.updated2019-03-28T11:44:15Z
hal.person.labIds989
hal.person.labIds124758
hal.identifierhal-02172761*


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