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Proportionality in Approval-Based Participatory Budgeting

  • Markus Brill
  • , Stefan Forster
  • , Martin Lackner
  • , Jan Maly
  • , Jannik Peters

Publication: Chapter in book/Conference proceedingContribution to conference proceedings

Abstract

The ability to measure the satisfaction of (groups of) voters is a crucial prerequisite for formulating proportionality axioms in approval-based participatory budgeting elections. Two common - but very different - ways to measure the satisfaction of a voter consider (i) the number of approved projects and (ii) the total cost of approved projects, respectively. In general, it is difficult to decide which measure of satisfaction best reflects the voters' true utilities. In this paper, we study proportionality axioms with respect to large classes of approval-based satisfaction functions. We establish logical implications among our axioms and related notions from the literature, and we ask whether outcomes can be achieved that are proportional with respect to more than one satisfaction function. We show that this is impossible for the two commonly used satisfaction functions when considering proportionality notions based on extended justified representation, but achievable for a notion based on proportional justified representation. For the latter result, we introduce a strengthening of priceability and show that it is satisfied by several polynomial-time computable rules, including the Method of Equal Shares and Phragmén's sequential rule.

Original languageEnglish
Title of host publicationProceedings of the 37th AAAI Conference on Artificial Intelligence
Subtitle of host publicationAAAI-23 Technical Tracks 5
EditorsBrian Williams, Yiling Chen, Jennifer Neville
Place of PublicationWashington, DC
PublisherAAAI Press
Pages5524-5531
Number of pages8
ISBN (Electronic)9781577358800
DOIs
Publication statusPublished - 27 Jun 2023
Externally publishedYes
Event37th AAAI Conference on Artificial Intelligence, AAAI 2023 - Washington, United States
Duration: 7 Feb 202314 Feb 2023

Publication series

SeriesProceedings of the AAAI Conference on Artificial Intelligence
Number5
Volume37
ISSN2159-5399

Conference

Conference37th AAAI Conference on Artificial Intelligence, AAAI 2023
Country/TerritoryUnited States
CityWashington
Period7/02/2314/02/23

Bibliographical note

Publisher Copyright:
Copyright © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

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