Multi-winner Approval Voting Goes EpistemicDownload PDF

Published: 20 May 2022, Last Modified: 22 Oct 2023UAI 2022 PosterReaders: Everyone
Keywords: computational social choice, epistemic voting, multi-winner voting
TL;DR: We design and experiment a maximum likelihood estimation framework for truth-tracking with multi-winner approval voting.
Abstract: Epistemic voting interprets votes as noisy signals about a ground truth. We consider contexts where the truth consists of a set of objective winners, knowing a lower and upper bound on its cardinality. A prototypical problem for this setting is the aggregation of multi-label annotations with prior knowledge on the size of the ground truth. We posit noise models, for which we define rules that output a set of winners corresponding to local maxima of the data likelihood function. We report on experiments on multi-label annotations (which we collected).
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