Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Facility’s Perspective to Fair Facility Location Problems
Authors: Chenhao Wang, Xiaoying Wu, Minming Li, Hau Chan5734-5741
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We assess the availability, existence, approximability, and the quality (price of fairness) of fair solutions, where the quality measures the system efficiency loss under a fair allocation compared to the one that maximizes the social welfare. further, we show that one can find a Pareto-optimal solution in polynomial time. In Section 3, we first prove that the problem of deter-mining the existence of a Prop allocation is NP-complete, using a similar reduction in (Bouveret et al. 2017) for the FDC. We show that for any instance, there exists a valid n+m 1 2m umax-Prop allocation where umax is highest value of any facility for any item. On the other hand, the existence of a valid ( n 12 umax ϵ)-Prop allocation is not guaranteed for any ϵ > 0, even if there are m = 3 facilities. |
| Researcher Affiliation | Academia | Chenhao Wang,1 Xiaoying Wu,2,3 Minming Li,4 Hau Chan1 1 University of Nebraska-Lincoln 2 AMSS, Chinese Academy of Sciences 3 University of Chinese Academy of Sciences 4 City University of Hong Kong EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Pareto-optimal valid allocation |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on a dataset. |
| Dataset Splits | No | The paper is theoretical and does not use dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe hardware specifications for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |