Fair Procedures for Fair Stable Marriage Outcomes
Authors: Nikolaos Tziavelis, Ioannis Giannakopoulos, Rune Quist Johansen, Katerina Doka, Nectarios Koziris, Panagiotis Karras7269-7276
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments with diverse simulated markets show that: (a) extant heuristics fail to yield high equity; (b) the best solution found by the GS algorithm can be very far from optimal equity; and (c) our procedures stand out in both efficiency and equity, even when compared to a non-procedurally fair approximation scheme. |
| Researcher Affiliation | Academia | 1Northeastern University, 2National Technical University of Athens, 3Aalborg University, 4Aarhus University |
| Pseudocode | Yes | Algorithm 1 Late Discontent Suspension; Algorithm 2 Early Discontent Suspension (Force Increase) |
| Open Source Code | Yes | All algorithms were implemented in Java1 and tested on an Intel Xeon 2GHz CPU with 8GB RAM. 1Available at https://github.com/ntzia/stable-marriage. |
| Open Datasets | No | The paper describes generating synthetic datasets from distributions ('Uniform(U)', 'Gaussian(G)', 'asymmetric distributions') but does not refer to any pre-existing publicly available dataset with concrete access information (link, DOI, citation). |
| Dataset Splits | No | The paper states it evaluates 'for 50 instances per distribution' but does not specify training, validation, or test dataset splits in terms of percentages or absolute counts, nor does it refer to standard predefined splits. |
| Hardware Specification | Yes | All algorithms were implemented in Java1 and tested on an Intel Xeon 2GHz CPU with 8GB RAM. |
| Software Dependencies | No | The paper mentions 'Java' as the implementation language but does not provide a specific version number for Java or any other software libraries. |
| Experiment Setup | Yes | Power Balance runs for 4n iterations before enforcing termination. We set the hot set of each Discrete distribution to include 40% of the agents, and the polarity of each Gaussian to 40% of n. |