Voting rules as statistical estimators

Marcus Pivato

MPRA Paper from University Library of Munich, Germany

Abstract: We adopt an `epistemic' interpretation of social decisions: there is an objectively correct choice, each voter receives a `noisy signal' of the correct choice, and the social objective is to aggregate these signals to make the best possible guess about the correct choice. One epistemic method is to fix a probability model and compute the maximum likelihood estimator (MLE), maximum a posteriori estimator (MAP) or expected utility maximizer (EUM), given the data provided by the voters. We first show that an abstract voting rule can be interpreted as MLE or MAP if and only if it is a scoring rule. We then specialize to the case of distance-based voting rules, in particular, the use of the median rule in judgement aggregation. Finally, we show how several common `quasiutilitarian' voting rules can be interpreted as EUM.

Keywords: voting; maximum likelihood estimator; maximum a priori estimator; expected utility maximizer; statistics; epistemic democracy; Condorcet jury theorem; scoring rule (search for similar items in EconPapers)
JEL-codes: C44 D70 D81 (search for similar items in EconPapers)
Date: 2011-04-13
New Economics Papers: this item is included in nep-cdm and nep-pol
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Journal Article: Voting rules as statistical estimators (2013)
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