# Project-Fair and Truthful Mechanisms for Budget Aggregation

arXiv: https://arxiv.org/abs/2309.02613 · HTML: https://mlsanity.com/paper/2309.02613/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2023.

## Built on by (most cited first, 2 of 2)

- [Learning Unanimously Acceptable Lotteries via Queries](https://mlsanity.com/paper/2604.17505/) (2026)
- [The End Justifies the Mean: A Linear Ranking Rule for Proportional Sequential Decisions](https://mlsanity.com/paper/2605.12717/) (2026)

## Abstract (arXiv)

We study the budget aggregation problem in which a set of strategic voters must split a finite divisible resource (such as money or time) among a set of competing projects. Our goal is twofold: We seek truthful mechanisms that provide fairness guarantees to the projects. For the first objective, we focus on the class of moving phantom mechanisms [Freeman et al., 2021], which are -- to this day -- essentially the only known truthful mechanisms in this setting. For project fairness, we consider the mean division as a fair baseline, and bound the maximum difference between the funding received by any project and this baseline. We propose a novel and simple moving phantom mechanism that provides optimal project fairness guarantees. As a corollary of our results, we show that our new mechanism minimizes the ℓ1 distance to the mean (a measure suggested by Caragiannis et al. [2022]) for three projects and gives the first non-trivial bounds on this quantity for more than three projects.