Guaranteeing Maximin Shares: Some Agents Left Behind
Authors: Hadi Hosseini, Andrew Searns
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 hadi@psu.edu, abs2157@rit.edu |
| 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. |