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dc.contributor.authorLang, Jérôme*
dc.contributor.authorSkowron, Piotr*
dc.date.accessioned2017-04-03T15:21:39Z
dc.date.available2017-04-03T15:21:39Z
dc.date.issued2016
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/16477
dc.language.isoenen
dc.subjectsocial choiceen
dc.subjectproportional representationen
dc.subjectapportionmenten
dc.subjectapproximationen
dc.subject.ddc003en
dc.titleMulti-Attribute Proportional Representationen
dc.typeCommunication / Conférence
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 fit. We look for a set that fits as much as possible the desired distributions on all attributes. Examples of applications include choosing members of a representative committee, where candidates are described by attributes such as sex, 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. With a single attribute the problem boils down to the apportionment problem for party-list proportional representation systems (in such case the value of the single attribute is the political affiliation of a candidate). We study some properties of the associated subset selection rules, and address their computation.en
dc.identifier.citationpages530-536en
dc.relation.ispartoftitleProceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI 2016)en
dc.relation.ispartofeditorSchuurmans, Dale
dc.relation.ispartofeditorWellman, Michael
dc.relation.ispartofpublnameAAAI Pressen
dc.relation.ispartofpublcityPalo Alto (USA)en
dc.relation.ispartofdate2016-12
dc.relation.ispartofpages4406en
dc.subject.ddclabelRecherche opérationnelleen
dc.relation.ispartofisbn978-1-57735-760-5en
dc.relation.conftitle30th AAAI Conference on Artificial Intelligence (AAAI 2016)en
dc.relation.confdate2016-02
dc.relation.confcityPhoenix, Arizonaen
dc.relation.confcountryUnited Statesen
dc.relation.forthcomingnonen
dc.description.ssrncandidatenonen
dc.description.halcandidateouien
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewednonen
dc.relation.Isversionofjnlpeerreviewednonen
dc.date.updated2017-04-03T15:09:25Z
hal.person.labIds989*
hal.person.labIds98120*
hal.identifierhal-01500907*


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