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.V39I13.33521

Markdown

[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.V39I13.33521

BibTeX

@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/}
}