Entropy and Insufficient Reason
Anubav Vasudevan (University of Chicago)
4:10 pm, Friday, November 10th, 2017
Faculty House, Columbia University
Abstract. One well-known objection to the principle of maximum entropy is the so-called Judy Benjamin problem, first introduced by van Fraassen (1981). The problem turns on the apparently puzzling fact that, on the basis of information relating an event’s conditional probability, the maximum entropy distribution will almost always assign to the event conditionalized on a probability strictly less than that assigned to it by the uniform distribution. In this paper, I present an analysis of the Judy Benjamin problem that can help to make sense of this seemingly odd feature of maximum entropy inference. My analysis is based on the claim that, in applying the principle of maximum entropy, Judy Benjamin is not acting out of a concern to maximize uncertainty in the face of new evidence, but is rather exercising a certain brand of epistemic charity towards her informant. This charity takes the form of an assumption on the part of Judy Benjamin that her informant’s evidential report leaves out no relevant information. I will explain how this single assumption suffices to rationalize Judy Benjamin’s behavior. I will then explain how such a re-conceptualization of the motives underlying Judy Benjamin’s appeal to the principle of maximum entropy can further our understanding of the relationship between this principle and the principle of insufficient reason. I will conclude with a discussion of the foundational significance for probability theory of ergodic theorems (e.g., de Finetti’s theorem) describing the asymptotic behavior of measure preserving transformation groups. In particular, I will explain how these results, which serve as the basis of maximum entropy inference, can provide a unified conceptual framework in which to justify both a priori and a posteriori probabilistic reasoning.