An axiomatic model of non bayesian updating

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The model can accommodate updating biases analogous to those observed by psychologists. Date: 2006 References: Add references at Cit Ec Citations View citations in Econ Papers (25) Track citations by RSS feed Downloads: (external link) (application/pdf) Access to full text is restricted to subscribers.

Related works: Working Paper: An Axiomatic Model of Non-Bayesian Updating (2005) Working Paper: An Axiomatic Model of Non-Bayesian Updating (2005) This item may be available elsewhere in Econ Papers: Search for items with the same title.

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We show that any learning rule that satisfies general axioms of label neutrality, independence of irrelevance alternatives, history neglect, monotonicity, and unanimity will result in bounded rational updates that result in learning in strongly connected networks.A representation theorem generalizes (the dynamic version of) Anscombe-Aumann’s theorem so that both the prior and the way in which it is updated are subjective.The model can generate updating biases analogous to those observed by psychologists.Gul and Pesendorfer's theory of temptation and self-control is a key building block.The main result is a representation theorem that generalizes (the dynamic version of) Anscombe-Aumann's theorem so that both the prior and the way in which it is updated are subjective.