Rawlsian Fairness in Online Bipartite Matching: Two-Sided, Group, and Individual
Authors: Seyed Esmaeili, Sharmila Duppala, Davidson Cheng, Vedant Nanda, Aravind Srinivasan, John P. Dickerson
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically test our algorithms on a real-world dataset. In this section, we verify the performance of our algorithm and our theoretical lower bounds for the KIID and group fairness setting using algorithm TSGFKIID (Section 5.1). |
| Researcher Affiliation | Academia | 1 University of Maryland, College Park 2 Colorado College |
| Pseudocode | Yes | Algorithm 1: PPDR( xv) Algorithm 2: TSGFKIID(α, β, γ) Algorithm 3: TSGFKAD(α, β, γ) |
| Open Source Code | Yes | Code to reproduce our experiments is available in the blinded format ; we will release that code in deblinded form upon acceptance. https://github.com/anonymousUser634534/TSGF |
| Open Datasets | Yes | We run our experiments over the widely used New York City (NYC) yellow cabs dataset (Sekuli c, Long, and Demˇsar 2021; Nanda et al. 2020; Xu and Xu 2020; Alonso-Mora, Wallar, and Rus 2017) which contains records of taxi trips in the NYC area from 2013. |
| Dataset Splits | No | The paper describes using the NYC dataset and running trials but does not specify explicit train/validation/test splits or their percentages/counts for reproducibility. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU/CPU models, memory, or cloud resources used for the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., programming language versions, library versions, or solver versions). |
| Experiment Setup | Yes | We pick out the trips between 7pm and 8pm on January 31, 2013, which is a rush hour with 10,814 drivers and 35,109 trips. We set driver patience u to 3. Following (Xu and Xu 2020), we uniformly sample rider patience v from {1, 2}. Specifically, we randomly assign 70% of the riders and drivers to be advantaged and the rest to be disadvantaged. The value of pe for e = (u, v) depends on whether the vertices belong to the advantaged or disadvantaged group. Specifically, pe = 0.6 if both vertices are advantaged, pe = 0.3 if both are disadvantaged, and pe = 0.1 for other cases. We run TSGFKIID at the scale of |U| = 49, |V | = 172 for 100 trials. |