Proportionality in Approval-Based Participatory Budgeting

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

Publikation: Beitrag in Buch/KonferenzbandBeitrag in Konferenzband

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.

OriginalspracheEnglisch
Titel des SammelwerksProceedings of the 37th AAAI Conference on Artificial Intelligence
Untertitel des SammelwerksAAAI-23 Technical Tracks 5
Herausgeber*innenBrian Williams, Yiling Chen, Jennifer Neville
ErscheinungsortWashington, DC
VerlagAAAI Press
Seiten5524-5531
Seitenumfang8
ISBN (elektronisch)9781577358800
DOIs
PublikationsstatusVeröffentlicht - 27 Juni 2023
Extern publiziertJa
Veranstaltung37th AAAI Conference on Artificial Intelligence, AAAI 2023 - Washington, USA/Vereinigte Staaten
Dauer: 7 Feb. 202314 Feb. 2023

Publikationsreihe

ReiheProceedings of the AAAI Conference on Artificial Intelligence
Nummer5
Band37
ISSN2159-5399

Konferenz

Konferenz37th AAAI Conference on Artificial Intelligence, AAAI 2023
Land/GebietUSA/Vereinigte Staaten
OrtWashington
Zeitraum7/02/2314/02/23

Bibliographische Notiz

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

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