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..
Maximin Share Allocations on Cycles
Authors: Zbigniew Lonc, Miroslaw Truszczynski
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present cases when maximin share allocations of goods on cycles exist and provide results on allocations guaranteeing each agent a certain portion of her maximin share. We also study algorithms for computing maximin share allocations of goods on cycles. |
| Researcher Affiliation | Academia | 1 Warsaw University of Technology, Poland 2 University of Kentucky, USA |
| Pseudocode | No | The paper describes algorithms in prose but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention providing open-source code for its methodology. |
| Open Datasets | No | The paper is theoretical and does not use or reference any datasets for training purposes. The figures provide illustrative examples, not empirical data. |
| Dataset Splits | No | The paper is theoretical and does not describe any dataset splits for validation. |
| Hardware Specification | No | The paper does not specify any hardware used for experiments as it is a theoretical work. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |