  There are things we know, things we know we don't know, and then there are
things we don't know we don't know. In this paper we address the latter two
issues in a Bayesian framework, introducing the notion of doubt to quantify the
degree of (dis)belief in a model given observational data in the absence of
explicit alternative models. We demonstrate how a properly calibrated doubt can
lead to model discovery when the true model is unknown.
