Abstract
Participatory budgeting engages the public in the process of allocating public money to different types of projects. PB designs differ in how voters are asked to express their preferences over candidate projects and how these preferences are aggregated to determine which projects to fund. This paper studies two fundamental questions in PB design. Which voting format and aggregation method to use, and how to evaluate the outcomes of these design decisions? We conduct an extensive empirical study in which 1 800 participants vote in four participatory budgeting elections in a controlled setting to evaluate the practical effects of the choice of voting format and aggregation rule. We find that k-approval leads to the best user experience. With respect to the aggregation rule, greedy aggregation leads to outcomes that are highly sensitive to the input format used and the fraction of the population that participates. The method of equal shares, in contrast, leads to outcomes that are not sensitive to the type of voting format used, and these outcomes are remarkably stable even when the majority of the population does not participate in the election. These results carry valuable insights for PB practitioners and social choice researchers.
| Original language | English |
|---|---|
| Title of host publication | AAAI-23 Technical Tracks 5 |
| Editors | Brian Williams, Yiling Chen, Jennifer Neville |
| Publisher | AAAI press |
| Pages | 5633-5640 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781577358800 |
| DOIs | |
| State | Published - 27 Jun 2023 |
| Event | 37th AAAI Conference on Artificial Intelligence, AAAI 2023 - Washington, United States Duration: 7 Feb 2023 → 14 Feb 2023 |
Publication series
| Name | Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023 |
|---|---|
| Volume | 37 |
Conference
| Conference | 37th AAAI Conference on Artificial Intelligence, AAAI 2023 |
|---|---|
| Country/Territory | United States |
| City | Washington |
| Period | 7/02/23 → 14/02/23 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 16 Peace, Justice and Strong Institutions
ASJC Scopus subject areas
- Artificial Intelligence
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