Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning

Abstract

We use the "map of elections" approach of Szufa et al. (AAMAS 2020) to analyze several well-known vote distributions. For each of them, we give an explicit formula or an efficient algorithm for computing its frequency matrix, which captures the probability that a given candidate appears in a given position in a sampled vote. We use these matrices to draw the "skeleton map" of distributions, evaluate its robustness, and analyze its properties. We further develop a general and unified framework for learning the distribution of real-world preferences using the frequency matrices of established vote distributions.

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Cite

Text

Boehmer et al. "Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning." Neural Information Processing Systems, 2022.

Markdown

[Boehmer et al. "Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning." Neural Information Processing Systems, 2022.](https://mlanthology.org/neurips/2022/boehmer2022neurips-expected/)

BibTeX

@inproceedings{boehmer2022neurips-expected,
  title     = {{Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning}},
  author    = {Boehmer, Niclas and Bredereck, Robert and Elkind, Edith and Faliszewski, Piotr and Szufa, Stanisław},
  booktitle = {Neural Information Processing Systems},
  year      = {2022},
  url       = {https://mlanthology.org/neurips/2022/boehmer2022neurips-expected/}
}