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..
Equitable Allocations of Indivisible Goods
Authors: Rupert Freeman, Sujoy Sikdar, Rohit Vaish, Lirong Xia
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on real-world preference data reveal that approximate envy-freeness, approximate equitability, and Pareto optimality can often be achieved simultaneously. |
| Researcher Affiliation | Collaboration | 1Microsoft Research New York City 2Rensselaer Polytechnic Institute |
| Pseudocode | No | The paper describes algorithms verbally and through theorems but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about the public availability of its source code or links to a code repository for the methodology described. |
| Open Datasets | Yes | For real-world preferences, we used data obtained from the popular fair division website Spliddit [Goldman and Procaccia, 2014]. |
| Dataset Splits | No | The paper mentions using real-world and synthetic datasets but does not specify exact training, validation, or test split percentages or sample counts. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | Yes | For synthetic data, we generated 1000 instances with n = 5, m = 20, and (strictly positive) valuations drawn i.i.d. from Dirichlet distribution. The concentration parameter for each item is set to 10 to generate normalized valuations. |