Non-Bayesian updating: a theoretical framework

This paper models an agent in a multi-period setting who does not update according to Bayes Rule, and who is self-aware and anticipates her updating behavior when formulating plans. Choice-theoretic axiomatic foundations are provided to capture updating biases that reect excessive weight given to either prior beliefs, or alternatively, to observed data. A counterpart of the exchangeable learning Bayesian model is also described