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dc.contributor.authorEndriss, Ulle
dc.contributor.authorde Haan, Ronald
dc.contributor.authorLang, Jérôme
dc.contributor.authorSlavkovik, Marija
dc.date.accessioned2021-05-06T11:05:53Z
dc.date.available2021-05-06T11:05:53Z
dc.date.created2020-12-15T10:37:12Z
dc.date.issued2020
dc.PublishedThe journal of artificial intelligence research. 2020, 69 687-731.
dc.identifier.issn1076-9757
dc.identifier.urihttps://hdl.handle.net/11250/2753919
dc.description.abstractWe provide a comprehensive analysis of the computational complexity of the outcome determination problem for the most important aggregation rules proposed in the literature on logic-based judgment aggregation. Judgment aggregation is a powerful and flexible framework for studying problems of collective decision making that has attracted interest in a range of disciplines, including Legal Theory, Philosophy, Economics, Political Science, and Artificial Intelligence. The problem of computing the outcome for a given list of individual judgments to be aggregated into a single collective judgment is the most fundamental algorithmic challenge arising in this context. Our analysis applies to several different variants of the basic framework of judgment aggregation that have been discussed in the literature, as well as to a new framework that encompasses all existing such frameworks in terms of expressive power and representational succinctness.en_US
dc.language.isoengen_US
dc.publisherAI Access Foundationen_US
dc.titleThe Complexity Landscape of Outcome Determination in Judgment Aggregationen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2020 AI Access Foundation. All rights reserveden_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode2
dc.identifier.doi10.1613/jair.1.11970
dc.identifier.cristin1859883
dc.source.journalThe journal of artificial intelligence researchen_US
dc.source.4069
dc.source.pagenumber687-731en_US
dc.identifier.citationThe journal of artificial intelligence research. 2020, 69, 687–731en_US
dc.source.volume69en_US


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