A Bayesian model of voting in juries

We model voting in juries as a game of incomplete information, allowing jur ors to receive a continuum of signals. We characterize the unique symmetric equilibrium of the game, and give a condition under which no asymmetric eq uilibria exist under unanimity rule. We offer a condition under which unani mity rule exhibits a bias toward convicting the innocent, regardless of the size of the jury, and give an example showing that this bias can be revers ed. We prove a "jury theorem" for our general model: As the size of the jur y increases, the probability of a mistaken judgment goes to zero for every voting rule except unanimity rule. For unanimity rule, the probability of m aking a mistake is bounded strictly above zero if and only if there do not exist arbitrarily strong signals of innocence. Our results explain the asym ptotic inefficiency of unanimity rule in finite models and establishes the possibility of asymptotic efficiency, a property that could emerge only in a continuous model. (C) 2001 Academic Press.