Project-Fair and Truthful Mechanisms for Budget Aggregation

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.

Cite

Text

Freeman and Schmidt-Kraepelin. "Project-Fair and Truthful Mechanisms for Budget Aggregation." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I9.28828

Markdown

[Freeman and Schmidt-Kraepelin. "Project-Fair and Truthful Mechanisms for Budget Aggregation." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/freeman2024aaai-project/) doi:10.1609/AAAI.V38I9.28828

BibTeX

@inproceedings{freeman2024aaai-project,
  title     = {{Project-Fair and Truthful Mechanisms for Budget Aggregation}},
  author    = {Freeman, Rupert and Schmidt-Kraepelin, Ulrike},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2024},
  pages     = {9704-9712},
  doi       = {10.1609/AAAI.V38I9.28828},
  url       = {https://mlanthology.org/aaai/2024/freeman2024aaai-project/}
}