Common Voting Rules as Maximum Likelihood Estimators
This provides a theoretical justification for existing voting methods, which is incremental but clarifies their statistical foundations.
The paper tackles the problem of interpreting voting rules as maximum likelihood estimators under noise models, showing that many common voting rules can be derived from specific noise assumptions.
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