ML Anthology

Fair Stable Matchings Under Correlated Preferences (Student Abstract)

Angelina Brilliantova, Hadi Hosseini
AAAI 2021 pp. 15763-15764
doi:10.1609/AAAI.V35I18.17878 /aaai/2021/brilliantova2021aaai-fair/

Abstract

Stable matching models are widely used in market design, school admission, and donor organ exchange. The classic Deferred Acceptance (DA) algorithm guarantees a stable matching that is optimal for one side (say men) and pessimal for the other (say women). A sex-equal stable matching aims at providing a fair solution to this problem. We demonstrate that under a class of correlated preferences, the DA algorithm either returns a sex-equal solution or has a very low sex-equality cost.

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Cite

Text

Brilliantova and Hosseini. "Fair Stable Matchings Under Correlated Preferences (Student Abstract)." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I18.17878

Markdown

[Brilliantova and Hosseini. "Fair Stable Matchings Under Correlated Preferences (Student Abstract)." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/brilliantova2021aaai-fair/) doi:10.1609/AAAI.V35I18.17878

BibTeX

@inproceedings{brilliantova2021aaai-fair,
  title     = {{Fair Stable Matchings Under Correlated Preferences (Student Abstract)}},
  author    = {Brilliantova, Angelina and Hosseini, Hadi},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2021},
  pages     = {15763-15764},
  doi       = {10.1609/AAAI.V35I18.17878},
  url       = {https://mlanthology.org/aaai/2021/brilliantova2021aaai-fair/}
}