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A Market-Inspired Bidding Scheme for Peer Review Paper Assignment

dc.contributor.authorMeir, Reshef
hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorLang, Jérôme
hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorLesca, Julien
dc.contributor.authorMattei, Nicholas
dc.contributor.authorKaminsky, Natan
dc.date.accessioned2022-02-22T11:48:05Z
dc.date.available2022-02-22T11:48:05Z
dc.date.issued2021
dc.identifier.urihttps://basepub.dauphine.psl.eu/handle/123456789/22703
dc.language.isoenen
dc.subjectMechanism Designen
dc.subjectSocial Choiceen
dc.subjectVotingen
dc.subject.ddc004en
dc.titleA Market-Inspired Bidding Scheme for Peer Review Paper Assignmenten
dc.typeCommunication / Conférence
dc.description.abstractenWe propose a market-inspired bidding scheme for the assignment of paper reviews in large academic conferences. We provide an analysis of the incentives of reviewers during the bidding phase, when reviewers have both private costs and some information about the demand for each paper; and their goal is to obtain the best possible k papers for a predetermined k. We show that by assigning 'budgets' to reviewers and a 'price' for every paper that is (roughly) proportional to its demand, the best response of a reviewer is to bid sincerely, i.e., on her most favorite papers, and match the budget even when it is not enforced. This game-theoretic analysis is based on a simple, prototypical assignment algorithm. We show via extensive simulations on bidding data from real conferences, that our bidding scheme would substantially improve both the bid distribution and the resulting assignment.en
dc.identifier.citationpages4776-4784en
dc.relation.ispartoftitle35th AAAI Conference on Artificial Intelligenceen
dc.relation.ispartofpublnameAAAI Pressen
dc.relation.ispartofpublcityPalo Alto (USA)en
dc.subject.ddclabelInformatique généraleen
dc.relation.ispartofisbn978-1-57735-866-4en
dc.relation.conftitle35th AAAI Conference on Artificial Intelligenceen
dc.relation.confdate2021-02
dc.relation.forthcomingnonen
dc.description.ssrncandidatenon
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewednonen
dc.date.updated2022-02-22T11:46:31Z
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