Reducing Leximin Fairness to Utilitarian Optimization
Authors: Eden Hartman, Yonatan Aumann, Avinatan Hassidim, Erel Segal-Halevi
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 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. |
| Researcher Affiliation | Collaboration | 1Bar-Ilan University, Israel 2Google, Israel 3Ariel University, Israel |
| Pseudocode | Yes | Algorithm 1: Main Loop |
| Open Source Code | No | The paper does not provide any specific links to source code repositories or explicit statements about code availability for the methodology described. It mentions an extended version on arXiv, but this is for the paper itself, not source code. |
| Open Datasets | No | The paper discusses theoretical problems like 'stochastic indivisible allocations', 'giveaway lotteries', and 'participatory budgeting lotteries' as applications of its general framework, but it does not utilize or provide access information for any specific public datasets. |
| Dataset Splits | No | The paper describes a theoretical framework and its applications to social choice problems. It does not conduct experiments involving datasets, and therefore, no dataset splits are discussed. |
| Hardware Specification | No | The paper presents a theoretical reduction scheme and does not describe any experiments that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper focuses on theoretical algorithms and mathematical programming, discussing solvers for various problems. However, it does not specify any software dependencies, libraries, or their version numbers that would be used for implementation or experimentation. |
| Experiment Setup | No | The paper presents a theoretical framework and mathematical proofs. It does not describe any experimental setup, hyperparameters, or training configurations for models or algorithms. |