Matching with Dynamic Ordinal Preferences

Authors: Hadi Hosseini, Kate Larson, Robin Cohen

AAAI 2015 | Conference PDF | Archive PDF | Plain Text | 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 h5hossei@uwaterloo.ca Kate Larson Cheriton School of Computer Science University of Waterloo Waterloo, ON, Canada klarson@uwaterloo.ca Robin Cohen Cheriton School of Computer Science University of Waterloo Waterloo, ON, Canada rcohen@uwaterloo.ca
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.