  Economists often estimate a subset of their model parameters outside the
model and let the decision-makers inside the model treat these point estimates
as-if they are correct. This practice ignores model ambiguity and opens the
door for model misspecification and post-decision disappointment. We develop a
framework to explore and evaluate decision rules that explicitly account for
the uncertainty in the first step estimation and assess their performance in a
decision-theoretic setting. We show how to operationalize our analysis by
studying a stochastic dynamic investment model where the decision-maker takes
ambiguity about the model's transition dynamics directly into account.
