Common Voting Rules as Maximum Likelihood Estimators
Computer Science and Game Theory
2012-07-09 v1 Artificial Intelligence
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
@article{arxiv.1207.1368,
title = {Common Voting Rules as Maximum Likelihood Estimators},
author = {Vincent Conitzer and Tuomas Sandholm},
journal= {arXiv preprint arXiv:1207.1368},
year = {2012}
}
Comments
Appears in Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (UAI2005)