Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Asymptotic Fair Division: Chores Are Easier Than Goods
Authors: Pasin Manurangsi, Warut Suksompong
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that an envy-free allocation exists with high probability provided that m ≥ 2n, and moreover, m must be at least n + Θ(√n) in order for the existence to hold. On the other hand, we prove that a proportional allocation is likely to exist as long as m = ω(1), and this threshold is asymptotically tight. Our results reveal a clear contrast with the allocation of goods, where a larger number of items is necessary to ensure existence for both notions. |
| Researcher Affiliation | Collaboration | 1Google Research, Thailand 2National University of Singapore, Singapore EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Matching for Envy-Free Allocation... Algorithm 2 Matching for Proportional Allocation |
| Open Source Code | No | The paper does not contain any explicit statement about releasing code, nor does it provide a link to a code repository. It mentions a CoRR preprint [Manurangsi and Suksompong, 2025] but this is not a code release. |
| Open Datasets | No | The paper is theoretical and models disutilities as drawn independently from a non-atomic probability distribution D. It does not use or refer to any specific, named datasets. |
| Dataset Splits | No | The paper does not use any specific datasets, therefore, no dataset splits are provided. |
| Hardware Specification | No | The paper describes theoretical results and algorithms but does not report on experimental evaluations that would require specific hardware. No hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical proofs and algorithm design. It does not mention any specific software dependencies or version numbers for implementing or running experiments. |
| Experiment Setup | No | The paper presents theoretical findings and algorithms. There are no experiments described that would require an experimental setup with hyperparameters or training configurations. |