Project-Fair and Truthful Mechanisms for Budget Aggregation
Rupert Freeman, Ulrike Schmidt-Kraepelin
Abstract
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, 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 for three projects and gives the first non-trivial bounds on this quantity for more than three projects.
BibTeX
@article{Freeman_Schmidt-Kraepelin_2024, title={Project-Fair and Truthful Mechanisms for Budget Aggregation}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/28828}, DOI={10.1609/aaai.v38i9.28828}, abstractNote={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, 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 for three projects and gives the first non-trivial bounds on this quantity for more than three projects.}, number={9}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Freeman, Rupert and Schmidt-Kraepelin, Ulrike}, year={2024}, month={Mar.}, pages={9704-9712} }