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.
Learning from ambiguous and misspecified models / Marinacci M.; Massari F.. - In: JOURNAL OF MATHEMATICAL ECONOMICS. - ISSN 0304-4068. - ELETTRONICO. - 84:(2019), pp. 144-149. [10.1016/j.jmateco.2019.07.012]
Learning from ambiguous and misspecified models
Marinacci M.;
2019
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.