We have seen arguments above that IP does have some advantage over precise probabilism, in the capacity to represent suspending judgement, the difference between weight and balance of evidence and so on. So we must go at least this far up the uncertainty escalator. But for the sake of practicality we need not go any further, even though there are hierarchical Bayes models that would give us a well-defined theory of higher-order models of belief. This is, ultimately, a pragmatic argument. Actual human belief states are probably immensely complicated neurological patterns with all the attendant complexity, interactivity, reflexivity and vagueness. We are modelling belief, so it is about choosing a model at the right level of complexity. If you are working out the trajectory of a cannonball on earth, you can safely ignore the gravitational influence of the moon on the cannonball. Likewise, there will be contexts where simple models of belief are appropriate: perhaps your belief state is just a set of sentences of a language, or perhaps just a single probability function. If, however, you are modelling the tides, then the gravitational influence of the moon needs to be involved: the model needs to be more complex. This suggests that an adequate model of belief under severe uncertainty may need to move beyond the single probability paradigm. But a pragmatic argument says that we should only move as far as we need to. So while you need to model the moon to get the tides right, you can get away without having Venus in your model. This relates to the contextual nature of appropriateness for models of belief mentioned earlier. If one were attempting to provide a complete formal characterisation of the ontology of belief, then these regress worries would be significantly harder to avoid.