Ambiguity, Coherence and Performance

Abstract Imprecise Bayesianism has been proposed as an alternative to Standard Bayesianism, partly because of its tools for representing ambiguity. Instead of representing credences via precise probabilities, a set of probability distributions is used to model belief states. However, there are criticisms of Imprecise Bayesianism’s update rule. A recent alternative update rule is Alpha Cut, which evades some of the primary criticisms of Imprecise Bayesian updating. We compare Alpha Cut with Imprecise Bayesianism and another alternative update approach called Calibration. We find that Alpha Cut has problems with respect to ambiguity, coherence, and performance qualities, whereas there are more promising alternatives.