Ambiguity, Robust Statistics, and Raiffa's Critique

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

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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
UEA Research Groups:	Faculty of Social Sciences > Research Groups > Behavioural Economics Faculty of Social Sciences > Research Groups > Economic Theory
Related URLs:	https://papers.ssrn.com/sol3/papers.cfm?...
Depositing User:	LivePure Connector
Date Deposited:	23 Jul 2020 23:49
Last Modified:	20 Sep 2021 00:33
URI:	https://ueaeprints.uea.ac.uk/id/eprint/76261
DOI:

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