Equity Promotion in Online Resource Allocation
Authors: Pan Xu, Yifan Xu9962-9970
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our model and algorithms on publicly available COVID-19 vaccination datasets maintained by the Minnesota Department of Health. Experimental results confirm our theoretical predictions and demonstrate the power of our policies in navigating the distribution of limited resources toward the preset target ratios when compared against heuristics |
| Researcher Affiliation | Academia | Pan Xu1, Yifan Xu2 1 Department of Computer Science, New Jersey Institute of Technology, Newark, USA 2 Key Lab of CNII, MOE, Southeast University, Nanjing, China |
| Pseudocode | Yes | Algorithm 1: An LP-based sampling (SAMP). |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | Yes | We use the publicly available COVID-19 vaccination datasets that are maintained by the Minnesota Department of Health4. 4https://mn.gov/covid19/vaccine/data/index.jsp. |
| Dataset Splits | No | The paper describes the construction of the input instance from the datasets but does not explicitly provide details about training, validation, or test dataset splits, percentages, or sample counts for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory, or cloud computing resources) used to run the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies or versions (e.g., programming languages, libraries, frameworks, or solvers with version numbers) used for the experiments. |
| Experiment Setup | Yes | We test different settings when the supply scarcity ρ takes values in {1, 1.5, 2, 2.5, 3} while the minimum serving capacity is fixed at b = 1. For each setting, we run all algorithms for 100 times and take the average as the final performance. |