Ambiguity, Robust Statistics, and Raiffa's Critique

Massari, Filippo (2020) Ambiguity, Robust Statistics, and Raiffa's Critique.

Full text not available from this repository. (Request a copy)

Abstract

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.

Item Type: Article
Faculty \ School: Faculty of Social Sciences > School of Economics
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 23 Jul 2020 23:49
Last Modified: 23 Oct 2020 00:02
URI: https://ueaeprints.uea.ac.uk/id/eprint/76261
DOI:

Actions (login required)

View Item View Item