Non-bayesian Updating: a Theoretical Framework
نویسندگان
چکیده
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. Then the model is specialized axiomatically to capture updating biases that reect excessive weight given to (i) prior beliefs, or alternatively, (ii) the realized sample. Finally, the paper describes a counterpart of the exchangeable Bayesian model, where the agent tries to learn about parameters, and some answers are provided to the question what does a non-Bayesian updater learn?
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تاریخ انتشار 2003