Simultaneous Elicitation of Scoring Rule and Agent Preferences for Robust Winner Determination - Archive ouverte HAL Access content directly
Conference Papers Year :

Simultaneous Elicitation of Scoring Rule and Agent Preferences for Robust Winner Determination

(1, 2) , (1, 2) , (3, 4)
1
2
3
4

Abstract

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.
Fichier principal
Vignette du fichier
paper_15 (4).pdf (353.54 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03384433 , version 1 (18-10-2021)

Identifiers

Cite

Beatrice Napolitano, Olivier Cailloux, Paolo Viappiani. Simultaneous Elicitation of Scoring Rule and Agent Preferences for Robust Winner Determination. Algorithmic Decision Theory (ADT 2021), Nov 2021, Toulouse, France. pp.51-67, ⟨10.1007/978-3-030-87756-9_4⟩. ⟨hal-03384433⟩
113 View
90 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More