Multi-winner Approval Voting Goes Epistemic

Tahar Allouche, Jérôme Lang, Florian Yger

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).

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/multi-winner-approval-voting-goes-epistemic/code)