A minimal extension of Bayesian decision theory

Abstract

Savage denied that Bayesian decision theory applies in large worlds. This paper proposes a minimal extension of Bayesian decision theory to a large-world context that evaluates an event \(E\) by assigning it a number \(\pi (E)\) that reduces to an orthodox probability for a class of measurable events. The Hurwicz criterion evaluates \(\pi (E)\) as a weighted arithmetic mean of its upper and lower probabilities, which we derive from the measurable supersets and subsets of \(E\). The ambiguity aversion reported in experiments on the Ellsberg paradox is then explained by assigning a larger weight to the lower probability of winning than to the upper probability. However, arguments are given here that would make anything but equal weights irrational when using the Hurwicz criterion. The paper continues by embedding the Hurwicz criterion in an extension of expected utility theory that we call expectant utility.