In this post, we will explain what “Bayesian voting” and “probabilistic verdicts” are. Simply put, under Bayesian voting a judge would assign a numerical score reflecting his relative degree of belief in what the proper outcome of an issue or case should be. Ideally, the score would be anywhere in the range of 0 to 1:
The higher the score, the greater the judge’s confidence level or degree of belief. A score below 0.5, for example, would mean that the party with the burden of persuasion is not expected to prevail. A score above 0.5, by contrast, indicates that the party is expected to prevail, while a score of 0.5 means the judge is undecided about which party should prevail. (For more information about Bayesian voting and probabilistic verdicts, check out my 2011 paper The Turing Test and the Legal Process.) In our next few posts, we will compare and contrast issue-voting versus outcome-voting and then consider the possibility of “herd behavior” and “strategic behavior” by judges.