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..
When Do Envy-Free Allocations Exist?
Authors: Pasin Manurangsi, Warut Suksompong2109-2116
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that, surprisingly, there is in fact no universal point of transition instead, the transition is governed by the divisibility relation between m and n. On the one hand, if m is divisible by n, an envy-free allocation exists with high probability as long as m 2n. On the other hand, if m is not almost divisible by n, an envy-free allocation is unlikely to exist even when m = Θ(n log n/ log log n). |
| Researcher Affiliation | Academia | Pasin Manurangsi Department of EECS UC Berkeley Warut Suksompong Department of Computer Science University of Oxford |
| Pseudocode | Yes | Algorithm 1 Simplified Algorithm for r 3 |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper does not use a specific, named public dataset for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not specify any hardware used for running experiments. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |