Truth-Tracking via Approval Voting: Size Matters
Allouche, Tahar; Lang, Jérôme; Yger, Florian (2022), Truth-Tracking via Approval Voting: Size Matters, Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22), AAAI Press : Palo Alto (USA), p. 4768-4775. 10.1609/aaai.v36i5.20403
Type
Communication / ConférenceExternal document link
https://hal.archives-ouvertes.fr/hal-03861664Date
2022Conference title
elsevierConference date
2022Conference city
Palo Alto (USA)Conference country
CANADABook title
Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22)Publisher
AAAI Press
Published in
Palo Alto (USA)
ISBN
978-1-57735-876-3
Pages
4768-4775
Publication identifier
Metadata
Show full item recordAuthor(s)
Allouche, TaharLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Lang, Jérôme
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Yger, Florian
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Abstract (EN)
Epistemic social choice aims at unveiling a hidden ground truth given votes, which are interpreted as noisy signals about it. We consider here a simple setting where votes consist of approval ballots: each voter approves a set of alternatives which they believe can possibly be the ground truth. Based on the intuitive idea that more reliable votes contain fewer alternatives, we define several noise models that are approval voting variants of the Mallows model. The likelihood-maximizing alternative is then characterized as the winner of a weighted approval rule, where the weight of a ballot decreases with its cardinality. We have conducted an experiment on three image annotation datasets; they conclude that rules based on our noise model outperform standard approval voting; the best performance is obtained by a variant of the Condorcet noise model.Subjects / Keywords
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