The Distortion of Binomial Voting Defies Expectation
Abstract
In computational social choice, the distortion of a voting rule quantifies the degree to which the rule overcomes limited preference information to select a socially desirable outcome. This concept has been investigated extensively, but only through a worst-case lens. Instead, we study the expected distortion of voting rules with respect to an underlying distribution over voter utilities. Our main contribution is the design and analysis of a novel and intuitive rule, binomial voting, which provides strong distribution-independent guarantees for both expected distortion and expected welfare.
Cite
Text
Gonczarowski et al. "The Distortion of Binomial Voting Defies Expectation." Neural Information Processing Systems, 2023.Markdown
[Gonczarowski et al. "The Distortion of Binomial Voting Defies Expectation." Neural Information Processing Systems, 2023.](https://mlanthology.org/neurips/2023/gonczarowski2023neurips-distortion/)BibTeX
@inproceedings{gonczarowski2023neurips-distortion,
title = {{The Distortion of Binomial Voting Defies Expectation}},
author = {Gonczarowski, Yannai A. and Kehne, Gregory and Procaccia, Ariel D and Schiffer, Ben and Zhang, Shirley},
booktitle = {Neural Information Processing Systems},
year = {2023},
url = {https://mlanthology.org/neurips/2023/gonczarowski2023neurips-distortion/}
}