  Inferential models (IMs) are data-dependent, probability-like structures
designed to quantify uncertainty about unknowns. As the name suggests, the
focus has been on uncertainty quantification for inference, and on establishing
a validity property that ensures the IM is reliable in a specific sense. The
present paper develops an IM framework for decision problems and, in
particular, investigates the decision-theoretic implications of the
aforementioned validity property. I show that a valid IM's assessment of an
action's quality, defined by a Choquet integral, will not be too optimistic
compared to that of an oracle. This ensures that a valid IM tends not to favor
actions that the oracle doesn't also favor, hence a valid IM is reliable for
decision-making too. In a certain special class of structured statistical
models, further connections can be made between the valid IM's favored actions
and those favored by other more familiar frameworks, from which certain
optimality conclusions can be drawn. An important step in these
decision-theoretic developments is a characterization of the valid IM's credal
set in terms of confidence distributions, which may be of independent interest.
