An axiomatic model of non bayesian updating
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This paper models an agent in a three-period setting who does not update according to Bayes ’ Rule, and who is self-aware and anticipates her updating behavior when formulating plans.
An axiomatic model of non bayesian updating video
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.
The model can accommodate updating biases analogous to those observed by psychologists.
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In this paper we focus on imaging rules that can be described by the following procedure: (1) Identify every state with some real valued vector of characteristics, and accordingly identify every probabilistic belief with an expected vector of characteristics; (2) For every initial belief and every piece of information, choose the revised belief which is compatible with this information and for which the expected vector of characteristics has minimal Euclidean distance to the expected vector of characteristics of the initial belief.
This class of rules thus satisfies an intuitive notion of minimal belief revision.
Gul and Pesendorfer's theory of temptation and self-control is a key building block.