Ambiguity, Robust Statistics, and Raiffa's Critique
19 Pages Posted: 3 Jun 2019 Last revised: 30 Jun 2020
Date Written: June 30, 2020
I show that ambiguity-averse decision functionals matched with the multiple-prior learning model are more robust to model misspecification than the standard expected utility with Bayesian learning. However, these criteria may fail to deliver robust decisions because the multiple-prior learning model inherits the same fragility of Bayesian learning. There are misspecified learning problems in which an ambiguity-averse DM optimally chooses a sequence of ambiguous acts over a sequence of risky acts that would deliver a strictly higher average utility.
Keywords: ambiguity, misspecified learning, robust statistics, multiple-prior learning
JEL Classification: D81, D83, C11
Suggested Citation: Suggested Citation