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Computing Pareto Optimal Committees

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016.pdf (687.8Kb)
Date
2016
Dewey
Recherche opérationnelle
Sujet
Computational Social Choice; voting; NP-hardness
Conference name
Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016
Conference date
07-2016
Conference city
New York
Conference country
United States
Book title
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9-15 July
Author
Kambhampati, Subbarao
Publisher
AAAI Press / IJCAI
Year
2016
ISBN
978-1-57735-770-4
URI
https://basepub.dauphine.fr/handle/123456789/15829
Collections
  • LAMSADE : Publications
Metadata
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Author
Aziz, Haris
209491 NICTA Canberra labs
Lang, Jérôme
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Monnot, Jérôme
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Type
Communication / Conférence
Item number of pages
60-66
Abstract (EN)
Selecting a set of alternatives based on the preferences of agents is an important problem in committee selection and beyond. Among the various criteria put forth for desirability of a committee, Pareto optimality is a minimal and important requirement.As asking agents to specify their preferences over exponentially many subsets of alternatives is practically infeasible,we assume that each agent specifies a weak order on single alternatives, from which a preference relation over subsets is derived using some preference extension.We consider four prominent extensions (responsive, leximax, best, and worst). For each of them, we consider the corresponding Pareto optimality notion, and we study the complexity of computing and verifying Pareto optimal outcomes. We also consider strategic issues: for three of the set extensions, we present linear-time, Pareto optimal and strategyproof algorithms that work even for weak preferences.

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