A new resolution of the Judy Benjamin problem

Van Fraassen's Judy Benjamin problem has generally been taken to show that not all rational changes of belief can be modelled in a probabilistic framework if the available update rules are restricted to Bayes's rule and Jeffrey's generalization thereof. But alternative rules based on distance functions between probability assignments that allegedly can handle the problem seem to have counterintuitive consequences. Taking our cue from a recent proposal by Bradley, we argue that Jeffrey's rule can solve the Judy Benjamin problem after all. Moreover, we show that the specific instance of Jeffrey's rule that solves the Judy Benjamin problem can be underpinned by a particular distance function. Finally, we extend the set of distance functions to ones that take into account the varying degrees to which propositions may be epistemically entrenched.