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].
Matching with Dynamic Ordinal Preferences
Authors: Hadi Hosseini, Kate Larson, Robin Cohen
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We formulate a generic dynamic matching problem via a sequential stochastic matching process. We design a mechanism based on random serial dictatorship (RSD) that, given any history of preferences and matching decisions, guarantees global stochastic strategyproofness while satisfying desirable local properties. |
| Researcher Affiliation | Academia | Hadi Hosseini Cheriton School of Computer Science University of Waterloo Waterloo, ON, Canada EMAIL Kate Larson Cheriton School of Computer Science University of Waterloo Waterloo, ON, Canada EMAIL Robin Cohen Cheriton School of Computer Science University of Waterloo Waterloo, ON, Canada EMAIL |
| Pseudocode | Yes | Algorithm 1: RSD with adjusted priorities (ARSD) |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit release statement) for open-source code. |
| Open Datasets | No | The paper is theoretical and does not involve the use of datasets for training. Therefore, no information about publicly available datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve the use of datasets with training, validation, or test splits. Therefore, no such information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware. No hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any software implementation with specific version numbers for dependencies. |
| Experiment Setup | No | The paper is theoretical and describes algorithm design and proofs, not an experimental setup with hyperparameters or system-level training settings. |