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Non-Bayesian updating: A theoretical framework

By Larry G. Epstein, Jawwad Noor and Alvaro Sandroni

Abstract

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.Non-Bayesian updating, temptation and self-control, overreaction, underreaction, learning, law of small numbers

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  1. (1994). Infinite-Dimensional Analysis.
  2. (2008). Non-Bayesian updating: a theoretical framework 229 Mullainathan, Sendhil (2000), “Thinking through categories.” Unpublished paper, Department of Economics, MIT. [195] Noor,

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