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..
Fair Division Through Information Withholding
Authors: Hadi Hosseini, Sujoy Sikdar, Rohit Vaish, Hejun Wang, Lirong Xia2014-2021
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that HEF-k allocations with a small k often exist, even when envy-free allocations do not (Figure 1). We also compare several known algorithms for computing EF1 allocations on synthetic and real-world preference data, and find that the round-robin algorithm and a recent algorithm of Barman, Krishnamurthy, and Vaish (2018) withhold close-to-optimal amount of information, often hiding no more than three goods (Section 5). |
| Researcher Affiliation | Academia | Hadi Hosseini,1 Rochester Institute of Technology, 2Washington University St. Louis, 3Rensselaer Polytechnic Institute EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | ALGORITHM 1: Greedy Approximation Algorithm for HEF-k-VERIFICATION |
| Open Source Code | No | No explicit statement or link providing access to the source code for the methodology described in this paper. |
| Open Datasets | Yes | For experiments with real-world data, we use the data from the popular fair division website Spliddit (Goldman and Procaccia 2014). |
| Dataset Splits | No | The paper describes generating synthetic data and using real-world data, but does not provide specific training/validation/test dataset splits (e.g., percentages or sample counts) for reproducibility. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts) are provided for the experimental setup. The paper only mentions running experiments. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9) are provided for reproducibility of the experiments. |
| Experiment Setup | No | The paper describes the setup for generating synthetic data (e.g., varying n and m, Bernoulli distribution parameter) and the metrics used (regret, hidden goods), but it does not provide specific experimental setup details like hyperparameters or training configurations for the algorithms themselves. |