Bayesian updating by judges

In their thought-provoking paper “The Votes of Other Judges,” Eric Posner and Adrian Vermeule present a compelling argument why judges on multi-member panels should engage in informal Bayesian updating when they decide issues of law or questions of interpretation. Consider, for example, the U.S. Supreme Court, which is composed of nine judges. The Supreme Court generally schedules an oral argument for each case on its docket, and each side is usually allotted 20 minutes to present their arguments to the Court. At the initial conference meeting after oral argument, the Justices meet in private and reveal to each other how they would decide the case. Suppose five of the Justices say that the ordinary meaning of the statute is clearly X, while four say that it is clearly Y. In the words of Eric Posner, “Shouldn’t all nine update their views and learn from the aggregate information contained in the votes of colleagues? Shouldn’t all entertain the possibility that the vote reveals the statute to be ambiguous?” In the alternative, what if five Justices say that the statute clearly means X, while four say that it is ambiguous as between X and Y. “Should the five obtain some information from the votes of the four, albeit not as much as in [the above case]? … And how about vice-versa–should the four update their own views, in light of the views of the five?” For our part, we think Posner and Vermeule are on the right track. The votes of each judge provide additional relevant information about the case under review. The problem, however, is not whether judges should engage in Bayesian voting, but rather how they should do so. After all, we don’t foresee judges doing formal probability calculations any time soon. (Here is a fun video tutorial explaining the finer points of Bayesian updating. Here is another.) We will address “the how question” in our next blog post.

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Hat tip: Kevin Binz, via Fewer Lacunae

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One Response to Bayesian updating by judges

  1. Pingback: Bayesian updating by judges (part 2) | prior probability

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