Common Voting Rules as Maximum Likelihood Estimators

TL;DR

This paper explores how common voting rules can be interpreted as maximum likelihood estimators under certain noise models, linking voting theory with statistical estimation.

Contribution

It establishes a novel connection between voting rules and maximum likelihood estimation, providing a new perspective on the foundations of voting methods.

Findings

Voting rules can be viewed as ML estimators under specific noise models.

This interpretation offers insights into the robustness and optimality of voting rules.

The framework unifies various voting rules under a common statistical paradigm.

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