Truth-Revealing Participatory Budgeting

2026-01-24

AI Summary

This study addresses participatory budgeting under incomplete information, where the true quality of projects is unobservable and voters cast ballots based only on noisy signals. Modeling the problem from a cognitive perspective for the first time, the paper assumes projects possess latent quality and that voters make decisions based on imperfect information, with the goal of aggregating dispersed signals through voting mechanisms to select high-quality project sets. Combining expected utility analysis, probabilistic convergence theory, and numerical experiments, the authors systematically evaluate the information aggregation performance of common budgeting rules under both unit-cost and heterogeneous-cost settings. The findings reveal that when project costs are nearly homogeneous—particularly under unit costs—mainstream rules recover the optimal project set with probability approaching one; however, truthful voting constitutes an equilibrium only under extremely restrictive conditions, indicating that strategic behavior is pervasive.

Technology Category

• Game Theory and Economic Paradigms: Social Choice / Voting
• Reasoning under Uncertainty: Decision/Utility Theory
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Content-based information diffusion

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.

Problem

• Participatory Budgeting
• epistemic perspective
• project quality
• information aggregation
• truthful voting

Innovation

• epistemic voting
• participatory budgeting
• information aggregation
• truthful incentives
• project quality