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Learning juror competence: a generalized Condorcet Jury Theorem

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  • Jan-Willem Romeijn

    (University of Groningen, The Netherlands, j.w.romeijn@rug.nl)

  • David Atkinson

    (University of Groningen, The Netherlands)

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    Abstract

    This article presents a generalization of the Condorcet Jury Theorem. All results to date assume a fixed value for the competence of jurors, or alternatively, a fixed probability distribution over the possible competences of jurors. In this article, we develop the idea that we can learn the competence of the jurors by the jury vote. We assume a uniform prior probability assignment over the competence parameter, and we adapt this assignment in the light of the jury vote. We then compute the posterior probability, conditional on the jury vote, of the hypothesis voted over. We thereby retain the central results of Condorcet, but we also show that the posterior probability depends on the size of the jury as well as on the absolute margin of the majority.

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    Bibliographic Info

    Article provided by in its journal Politics, Philosophy & Economics.

    Volume (Year): 10 (2011)
    Issue (Month): 3 (August)
    Pages: 237-262

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    Handle: RePEc:sae:pophec:v:10:y:2011:i:3:p:237-262

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    Related research

    Keywords: Condorcet Jury Theorem; juror competence; Bayesian inference; Condorcet formula; majority voting;

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