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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Guaranteeing Maximin Shares: Some Agents Left Behind
Authors: Hadi Hosseini, Andrew Searns
IJCAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 17,074 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,538 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we provide empirical experiments using synthetic data. ... 6 Empirical Evaluations The polynomial algorithm of Theorem 4 guarantees ( 23 , 1)-MMS only for n < 9. We evaluate a variant of this algorithm that does not rely on the strong normalization assumption (details relegated to the full version). |
| Researcher Affiliation | Academia | Hadi Hosseini1 and Andrew Searns2 1Pennsylvania State University 2Rochester Institute of Technology EMAIL, EMAIL |
| Pseudocode | No | The paper describes algorithms in prose and refers to figures, but it does not contain a formally labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide a direct link or explicit statement about the availability of its source code. |
| Open Datasets | No | We focus on ordered instances as the most difficult instances in achieving MMS [Bouveret and Lemaˆıtre, 2016] and generate 1,000 instances for each combination of n and m. Instances are sampled uniformly at random, ordered, and scaled such that vi(M) = n for all agents i N. ... We provide empirical experiments using synthetic data. |
| Dataset Splits | No | The paper mentions generating synthetic data and evaluating its algorithm but does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific hardware specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes the algorithms and empirical evaluations but does not provide specific experimental setup details such as hyperparameters or system-level training settings. |