Journal Article

Bayesian learning with multiple priors and nonvanishing ambiguity

Author

  • Alexander Zimper
Publication Date

The existing models of Bayesian learning with multiple priors by Marinacci and by Epstein and Schneider formalize the intuitive notion that ambiguity should vanish through statistical learning in an one-urn environment. Moreover, the multiple priors decision maker of these models will eventually learn the “truth.” To accommodate nonvanishing violations of Savage’s sure-thing principle, as reported in Nicholls et al., we construct and analyze a model of Bayesian learning with multiple priors for which ambiguity does not necessarily vanish in an one-urn environment

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Info

JEL Classification
C11, D81

Key Words

  • ambiguity
  • misspecified priors

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