Fair Division Through Information Withholding
Authors: Hadi Hosseini, Sujoy Sikdar, Rohit Vaish, Hejun Wang, Lirong Xia2014-2021
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | 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 hho@cs.rit.edu, sujoy@wustl.edu, {vaishr2, wangj38}@rpi.edu, xial@cs.rpi.edu |
| 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. |