Ambiguity, Robust Statistics, and Raiffa's Critique

19 Pages Posted: 3 Jun 2019 Last revised: 30 Jun 2020

See all articles by Filippo Massari

Filippo Massari

Bocconi University - Department of Decision Sciences; University of East Anglia

Date Written: June 30, 2020

Abstract

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

Massari, Filippo, Ambiguity, Robust Statistics, and Raiffa's Critique (June 30, 2020). Available at SSRN: https://ssrn.com/abstract=3388410 or http://dx.doi.org/10.2139/ssrn.3388410

Filippo Massari (Contact Author)

Bocconi University - Department of Decision Sciences ( email )

Via Roentgen 1
Milan, 20136
Italy

University of East Anglia ( email )

Norwich Research Park
Norwich, Norfolk NR4 7TJ
United Kingdom

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
7
Abstract Views
162
PlumX Metrics