  This work proposes a way to align statistical modeling with decision making.
We provide a method that propagates the uncertainty in predictive modeling to
the uncertainty in operational cost, where operational cost is the amount spent
by the practitioner in solving the problem. The method allows us to explore the
range of operational costs associated with the set of reasonable statistical
models, so as to provide a useful way for practitioners to understand
uncertainty. To do this, the operational cost is cast as a regularization term
in a learning algorithm's objective function, allowing either an optimistic or
pessimistic view of possible costs, depending on the regularization parameter.
From another perspective, if we have prior knowledge about the operational
cost, for instance that it should be low, this knowledge can help to restrict
the hypothesis space, and can help with generalization. We provide a
theoretical generalization bound for this scenario. We also show that learning
with operational costs is related to robust optimization.
