  We show that quantum state discrimination sits neatly in the framework of
Bayesian experimental design. In this setting, the two main branches of quantum
state discrimination (minimal error and maximal confidence) simply correspond
to two different utility functions. This view allows straightforward extensions
and mixing of different discrimination tasks by examining the utility
functions, and to describe multi-objective discrimination tasks. In addition,
the probability of success and the total confidence are resource monotones
quantifying the usefulness of measurements. We give general conditions under
which utility functions lead to resource monotones in the resource theory of
quantum measurement.
