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 reflect excessive weight given to either prior beliefs, or alternatively, to observed data. A counterpart of the exchangeable Bayesian learning model is also described.
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