Uncertainty in Artificial Intelligence
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Common Voting Rules as Maximum Likelihood Estimators
Vincent Conitzer, Tuomas Sandholm
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
Keywords:
Pages: 145-152
PS Link:
PDF Link: /papers/05/p145-conitzer.pdf
BibTex:
@INPROCEEDINGS{Conitzer05,
AUTHOR = "Vincent Conitzer and Tuomas Sandholm",
TITLE = "Common Voting Rules as Maximum Likelihood Estimators",
BOOKTITLE = "Proceedings of the Twenty-First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-05)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2005",
PAGES = "145--152"
}


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