Reducing Leximin Fairness to Utilitarian Optimization
Abstract
Two prominent objectives in social choice are utilitarian - maximizing the sum of agents' utilities, and leximin - maximizing the smallest agent's utility, then the second-smallest, etc. Utilitarianism is typically computationally easier to attain but is generally viewed as less fair. This paper presents a general reduction scheme that, given a utilitarian solver, produces a distribution over states (deterministic outcomes) that is leximin in expectation. Importantly, the scheme is robust in the sense that, given an approximate utilitarian solver, it produces a lottery that is approximately-leximin (in expectation) - with the same approximation factor. We apply our scheme to several social choice problems: stochastic allocations of indivisible goods, giveaway lotteries, and fair lotteries for participatory budgeting.
Cite
Text
Hartman et al. "Reducing Leximin Fairness to Utilitarian Optimization." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I13.33521Markdown
[Hartman et al. "Reducing Leximin Fairness to Utilitarian Optimization." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/hartman2025aaai-reducing/) doi:10.1609/AAAI.V39I13.33521BibTeX
@inproceedings{hartman2025aaai-reducing,
title = {{Reducing Leximin Fairness to Utilitarian Optimization}},
author = {Hartman, Eden and Aumann, Yonatan and Hassidim, Avinatan and Segal-Halevi, Erel},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2025},
pages = {13905-13914},
doi = {10.1609/AAAI.V39I13.33521},
url = {https://mlanthology.org/aaai/2025/hartman2025aaai-reducing/}
}