NeurIPS 2022accept10 citations
Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning
Niclas Boehmer, Robert Bredereck, Edith Elkind, Piotr Faliszewski, Stanisław Szufa
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.
Mallows modelvisualizing experimental resultsvote distributionssingle-peaked elections
BibTeX
@inproceedings{
boehmer2022expected,
title={Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning},
author={Niclas Boehmer and Robert Bredereck and Edith Elkind and Piotr Faliszewski and Stanis{\l}aw Szufa},
booktitle={Advances in Neural Information Processing Systems},
editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
year={2022},
url={https://openreview.net/forum?id=X3RuacCx1R}
}