A new article in the Columbia Law Review titled “Reasons for Interpretation” by Francisco J. Urbina (see here) brought to my mind my 2016 paper “Probabilistic Interpretation“. For his part, Professor Urbina presents a “systematic analysis of the different kinds of reasons usually canvassed to defend theories of interpretation” in constitutional law. My paper, by contrast, presents a simple probabilistic model of legal interpretation. Specifically, in place of a semantic or philosophical theory of interpretation, I model the problem of interpretation probabilistically as a “best-choice secretary problem” (see, for example, a formal mathematical statement of the problem below) in which a problem-solving judge strives to select the best interpretation of a given rule from a finite set of n possible interpretations of such rule.