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 [1].
Strategyproof Mechanisms for Group-Fair Facility Location Problems
Authors: Houyu Zhou, Minming Li, Hau Chan
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the facility location problems where agents are located on a real line and divided into groups based on criteria such as ethnicity or age. Our aim is to design mechanisms to locate a facility to approximately minimize the costs of groups of agents to the facility fairly while eliciting the agents locations truthfully. We first explore various well-motivated group fairness cost objectives for the problems and show that many natural objectives have an unbounded approximation ratio. We then consider minimizing the maximum total group cost and minimizing the average group cost objectives. For these objectives, we show that existing classical mechanisms (e.g., median) and new group-based mechanisms provide bounded approximation ratios, where the group-based mechanisms can achieve better ratios. We also provide lower bounds for both objectives. |
| Researcher Affiliation | Academia | Houyu Zhou1 , Miniming Li1 , Hau Chan2 1City University of Hong Kong 2University of Nebraska-Lincoln EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes mechanisms in prose but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It only links to the full version of the paper on arXiv: "Due to the space limit, most of the proofs are in the full version of the paper (https://arxiv.org/abs/2107.05175)." |
| Open Datasets | No | This is a theoretical paper and does not use datasets for training or evaluation. |
| Dataset Splits | No | This is a theoretical paper and does not discuss dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments that would require specific hardware details. No hardware specifications are mentioned. |
| 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. |