Truth-Revealing Participatory Budgeting

TL;DR

This paper introduces an epistemic framework for participatory budgeting where project quality is uncertain and voters have noisy information, analyzing how different rules perform in selecting high-quality projects and their incentive compatibility.

Contribution

It develops a novel epistemic model for participatory budgeting with uncertain project quality and evaluates rule performance and strategic incentives within this framework.

Findings

Common PB rules approximate the optimal set as project costs become uniform.

When projects have unit cost, these rules identify the best set with high probability.

Strategic voting incentives are limited to very restrictive conditions.

Abstract

Participatory Budgeting (PB) is commonly studied from an axiomatic perspective, where the aim is to design procedurally fair and economically efficient rules for voters with full information regarding their preferences. In contrast, we take an epistemic perspective and consider a framework where PB projects have different levels of underlying quality, indicating how well the project will take effect, which cannot be directly observed before implementation. Agents with noisy information cast votes to aggregate their information, and aim to elect a high-quality set of projects. We evaluate the performance of common PB rules by measuring the expected utility of their outcomes, compared to the optimal set of projects. We find that the quality of approximation improves as the range of project costs shrinks. When projects have unit cost, these common rules can identify the ``best'' set with probability converging to 1. We also study whether strategic agents have incentives to honestly convey their information in the vote. We find that it happens only under very restrictive conditions. We also run numerical experiments to examine the performance of different rules empirically and support our theoretical findings.