In standard models of Bayesian learning agents reduce their uncertainty about an eventÂ’s true probability because their consistent estimator concentrates almost surely around this probabilityÂ’s true value as the number of observations becomes large. This paper takes the empirically...

Based on the axiomatic framework of Choquet decision theory, we develop a closed-form model of Bayesian learning with ambiguous beliefs about the mean of a normal distribution. In contrast to rational models of Bayesian learning the resulting Choquet Bayesian estimator results in a long-run bias...

We explore a New Keynesian Model with diverse beliefs and study the aggregation problems in the log-linearized economy. We show the solution of these problems depend upon the belief structure. Agents' beliefs are described by individual state variables and satisfy three Rationality Axioms,...

Based on the axiomatic framework of Choquet decision theory, we develop a closed-form model of Bayesian learning with ambiguous beliefs about the mean of a normal distribution. In contrast to rational models of Bayesian learning the resulting Choquet Bayesian estimator results in a long-run bias...

This paper introduces a dynamic Bayesian game with an unknown population distribution. Players do not know the true population distribution and assess it based on their private observations using Bayes׳ rule. First, we show the existence and characterization of an equilibrium in which each...