Imprecise prior probabilities can be used to model beliefs when data is sparse, equivocal or vague.  They can also provide a way of representing certain kinds of indecision or uncertainty about epistemic values and inductive policies. If we use the apparatus of proper scoring rules to model a believer’s epistemic values, we can see her priors, partly, as an articulation of her epistemic values (e.g., the value she places on learning, or the value she places on having accurate beliefs).  When these values are less than fully definite, or when there is unresolved conflict among values, imprecise priors reflect this indefiniteness in interesting ways.