The Semi-Random Satisfaction of Voting Axioms

Abstract

We initiate the work towards a comprehensive picture of the worst average-case satisfaction of voting axioms in semi-random models, to provide a finer and more realistic foundation for comparing voting rules. We adopt the semi-random model and formulation in [Xia 2020], where an adversary chooses arbitrarily correlated ``ground truth'' preferences for the agents, on top of which random noises are added. We focus on characterizing the semi-random satisfaction of two well-studied voting axioms: Condorcet criterion and participation. We prove that for any fixed number of alternatives, when the number of voters $n$ is sufficiently large, the semi-random satisfaction of the Condorcet criterion under a wide range of voting rules is $1$, $1-\exp(-\Theta(n))$, $\Theta(n^{-0.5})$, $ \exp(-\Theta(n))$, or being $\Theta(1)$ and $1-\Theta(1)$ at the same time; and the semi-random satisfaction of participation is $1-\Theta(n^{-0.5})$. Our results address open questions by Berg and Lepelley in 1994, and also confirm the following high-level message: the Condorcet criterion is a bigger concern than participation under realistic models.

Cite

Text

Xia. "The Semi-Random Satisfaction of Voting Axioms." Neural Information Processing Systems, 2021.

Markdown

[Xia. "The Semi-Random Satisfaction of Voting Axioms." Neural Information Processing Systems, 2021.](https://mlanthology.org/neurips/2021/xia2021neurips-semirandom/)

BibTeX

@inproceedings{xia2021neurips-semirandom,
  title     = {{The Semi-Random Satisfaction of Voting Axioms}},
  author    = {Xia, Lirong},
  booktitle = {Neural Information Processing Systems},
  year      = {2021},
  url       = {https://mlanthology.org/neurips/2021/xia2021neurips-semirandom/}
}