Equitable Allocations of Indivisible Goods
Authors: Rupert Freeman, Sujoy Sikdar, Rohit Vaish, Lirong Xia
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | 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. |