Ambiguity, Robust Statistics, and Raiffa's Critique

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

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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: 20 Sep 2021 00:33

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