  Economists often estimate economic models on data and use the point estimates
as a stand-in for the truth when studying the model's implications for optimal
decision-making. This practice ignores model ambiguity, exposes the decision
problem to misspecification, and ultimately leads to post-decision
disappointment. Using statistical decision theory, we develop a framework to
explore, evaluate, and optimize robust decision rules that explicitly account
for estimation uncertainty. We show how to operationalize our analysis by
studying robust decisions in a stochastic dynamic investment model in which a
decision-maker directly accounts for uncertainty in the model's transition
dynamics.
