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NeurIPS 2025

Strategic Cost Selection in Participatory Budgeting

Conference Paper Main Conference Track Artificial Intelligence · Machine Learning

Abstract

We study strategic behavior of project proposers in the context of approval-based participatory budgeting (PB). In our model we assume that the votes are fixed and known and the proposers want to set as high project prices as possible, provided that their projects get selected and the prices are not below the minimum costs of their delivery. We study the existence of pure Nash equilibria (NE) in such games, focusing on the AV/Cost, Phragmen, and Method of Equal Shares rules. We also provide an experimental study of cost selection on real-life PB election data.

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Context

Venue
Annual Conference on Neural Information Processing Systems
Archive span
1987-2025
Indexed papers
30776
Paper id
590183503725758437