Learning from ambiguous and misspecified models

Massimo Marinacci, Filippo Massari

Research output: Contribution to journalArticle

2 Citations (Scopus)
3 Downloads (Pure)

Abstract

We model inter-temporal ambiguity as the scenario in which a Bayesian learner holds more than one prior distribution over a set of models and provide sufficient conditions for ambiguity to fade away because of learning. Our conditions apply to most learning environments: iid and non-iid model-classes, well-specified and misspecified model-classes/prior support pairs. We show that ambiguity fades away if the empirical evidence supports a set of models with identical predictions, a condition much weaker than learning the truth.

Original languageEnglish
Pages (from-to)144-149
Number of pages6
JournalJournal of Mathematical Economics
Volume84
Early online date14 Aug 2019
DOIs
Publication statusPublished - Oct 2019
Externally publishedYes

Keywords

  • Ambiguity
  • Learning
  • Misspecified learning
  • Robust statistical decisions

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