Approval-Based Committee Voting under Uncertainty

TL;DR

This paper investigates approval-based committee voting under voter uncertainty, analyzing four models of uncertain preferences and algorithms for selecting committees that maximize the probability of satisfying fairness axioms.

Contribution

It introduces four models of uncertain approval preferences and studies algorithms for selecting committees that optimize fairness criteria under these models.

Findings

Developed algorithms for committee selection under uncertainty

Analyzed probability of satisfying axioms like justified representation

Compared different models of voter preference uncertainty

Abstract

We study approval-based committee voting in which a target number of candidates are selected based on voters' approval preferences over candidates. In contrast to most of the work, we consider the setting where voters express uncertain approval preferences and explore four different types of uncertain approval preference models. For each model, we study the problems such as computing a committee with the highest probability of satisfying axioms such as justified representation.