Common Voting Rules as Maximum Likelihood Estimators
Abstract
Voting is a very general method of preference aggregation. A voting rule takes as input every voter's vote (typically, a ranking of the alternatives), and produces as output either just the winning alternative or a ranking of the alternatives. One potential view of voting is the following. There exists a 'correct' outcome (winner/ranking), and each voter's vote corresponds to a noisy perception of this correct outcome. If we are given the noise model, then for any vector of votes, we can
Cite
Text
Conitzer and Sandholm. "Common Voting Rules as Maximum Likelihood Estimators." Conference on Uncertainty in Artificial Intelligence, 2005.Markdown
[Conitzer and Sandholm. "Common Voting Rules as Maximum Likelihood Estimators." Conference on Uncertainty in Artificial Intelligence, 2005.](https://mlanthology.org/uai/2005/conitzer2005uai-common/)BibTeX
@inproceedings{conitzer2005uai-common,
title = {{Common Voting Rules as Maximum Likelihood Estimators}},
author = {Conitzer, Vincent and Sandholm, Tuomas},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2005},
pages = {145-152},
url = {https://mlanthology.org/uai/2005/conitzer2005uai-common/}
}