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Voting rules as statistical estimators

  • Pivato, Marcus

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.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 30292.

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Date of creation: 13 Apr 2011
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Handle: RePEc:pra:mprapa:30292
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  12. Pivato, Marcus, 2011. "Variable-population voting rules," MPRA Paper 31896, University Library of Munich, Germany.
  13. Truchon, Michel & Gordon, Stephen, 2009. "Statistical comparison of aggregation rules for votes," Mathematical Social Sciences, Elsevier, vol. 57(2), pages 199-212, March.
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  17. Serguei Kaniovski, 2010. "Aggregation of correlated votes and Condorcet’s Jury Theorem," Theory and Decision, Springer, vol. 69(3), pages 453-468, September.
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  19. P. Mongin., 1997. "The paradox of the Bayesian experts and state-dependent utility theory," THEMA Working Papers 97-15, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
  20. Nehring, Klaus & Pivato, Marcus & Puppe, Clemens, 2011. "Condorcet admissibility: Indeterminacy and path-dependence under majority voting on interconnected decisions," MPRA Paper 32434, University Library of Munich, Germany.
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