The oracles of SCOTUS decided Gamble v. U.S. today (17 June), upholding the nefarious “separate sovereigns” exception by a 7-2 margin. Here are three of our previous posts about this fascinating case:
1. Be like Bayes (part 3) (30 December 2018), in which we build a simple Bayesian model to predict whether the Supreme Court will overturn the “separate sovereigns” exception to the Double Jeopardy Clause when it decides Gamble v. U.S. (Our prediction turned out to be wrong.)
2. Be like Bayes (part 2) (29 December 2018), in which we estimate the base rate or the historical frequency in which a precedent is overturned by the Supreme Court in those cases in which a party is asking the Court to take such an action.
3. Forecasting the forecasts (31 December 2018), in which we describe a method for scoring the accuracy of our Gamble v. U.S. forecast via a simple scoring method that was first proposed by Glenn Wilson Brier, an early advocate of probability forecasting and the use of probability forecasts in decision making.
To my friends–law professor colleagues and students alike–beware! All three of the above posts are somewhat technical and mathematical in nature. In short, instead of focusing on the legal arguments in Gamble v. U.S. or the “merits” of the case (we agree with Brian Leiter, Richard Posner, and other legal realists that law in close cases is indeterminate), we attempt to build a simple Bayesian forecasting model based on the number of amicus briefs submitted by third parties to the Supreme Court.