
Simultaneous Elicitation of Scoring Rule and Agent Preferences for Robust Winner Determination
Napolitano, Beatrice; Cailloux, Olivier; Viappiani, Paolo (2021), Simultaneous Elicitation of Scoring Rule and Agent Preferences for Robust Winner Determination, in Dimitris Fotakis, David Ríos Insua, Algorithmic Decision Theory: 7th International Conference, ADT 2021, Springer, p. 51-67. 10.1007/978-3-030-87756-9_4
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Type
Communication / ConférenceDate
2021Conference date
2021Conference country
FRANCEBook title
Algorithmic Decision Theory: 7th International Conference, ADT 2021Book author
Dimitris Fotakis, David Ríos InsuaPublisher
Springer
ISBN
978-3-030-87755-2
Pages
51-67
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Show full item recordAuthor(s)
Napolitano, BeatriceLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Cailloux, Olivier
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Viappiani, Paolo
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Abstract (EN)
Social choice deals with the problem of determining a consensus choice from the preferences of different agents. In the classical setting, the voting rule is fixed beforehand and full information concerning the preferences of the agents is provided. This assumption of full preference information has recently been questioned by a number of researchers and several methods for eliciting the preferences of the agents have been proposed. In this paper we argue that in many situations one should consider as well the voting rule to be partially specified. Focusing on positional scoring rules, we assume that the chair, while not able to give a precise definition of the rule, is capable of answering simple questions requiring to pick a winner from a concrete profile. In addition, we assume that the agent preferences also have to be elicited. We propose a method for robust approximate winner determination and interactive elicitation based on minimax regret; we develop several strategies for choosing the questions to ask to the chair and the agents in order to converge quickly to a near-optimal alternative. Finally, we analyze these strategies in experiments where the rule and the preferences are simultaneously elicited.Subjects / Keywords
Uncertainty in AI; Computational Social Choice; Preference ElicitationRelated items
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