Imprecise probabilities aren’t a radically new theory. They are merely a slight modification of existing models of belief for situations of ambiguity. Often your credences will be precise enough, and your available actions will be such that you act more or less as if you were a strict Bayesian. One might analogize imprecise probabilities as the “Theory of Relativity” to the strict Bayesian “Newtonian Mechanics”: all but indistinguishable in all but the most extreme situations. This analogy goes deeper: in both cases, the theories are “empirically indistinguishable” in normal circumstances, but they both differ radically in some conceptual respects. Namely, the role of absolute space in Newtonian mechanics/GR; how to model ignorance in the strict/imprecise probabilist case. Howson (2012) makes a similar analogy between modelling belief and models in science. Both involve some requirement to be somewhat faithful to the target system, but in each case faithfulness must be weighed up against various theoretical virtues like simplicity, computational tractability and so on. Likewise Hosni (2014) argues that what model of belief is appropriate is somewhat dependent on context. There is of course an important disanalogy in that models of belief are supposed to be normative as well as descriptive, whereas models in science typically only have to play a descriptive role. Walley (1991) discusses a similar view but is generally sceptical of such an interpretation.