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Understanding Distance Measures Among Elections

Niclas Boehmer, Piotr Faliszewski, Rolf Niedermeier, Stanislaw Szufa, Tomasz Was
IJCAI 2022 pp. 102-108
doi:10.24963/IJCAI.2022/15 /ijcai/2022/boehmer2022ijcai-understanding/

Abstract

Motivated by putting empirical work based on (synthetic) election data on a more solid mathematical basis, we analyze six distances among elections, including, e.g., the challenging-to-compute but very precise swap distance and the distance used to form the so-called map of elections. Among the six, the latter seems to strike the best balance between its computational complexity and expressiveness.

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Cite

Text

Boehmer et al. "Understanding Distance Measures Among Elections." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/15

Markdown

[Boehmer et al. "Understanding Distance Measures Among Elections." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/boehmer2022ijcai-understanding/) doi:10.24963/IJCAI.2022/15

BibTeX

@inproceedings{boehmer2022ijcai-understanding,
  title     = {{Understanding Distance Measures Among Elections}},
  author    = {Boehmer, Niclas and Faliszewski, Piotr and Niedermeier, Rolf and Szufa, Stanislaw and Was, Tomasz},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {102-108},
  doi       = {10.24963/IJCAI.2022/15},
  url       = {https://mlanthology.org/ijcai/2022/boehmer2022ijcai-understanding/}
}