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Learning Under Ambiguity

  • Larry G. Epstein
  • Martin Schneider

This paper considers learning when the distinction between risk and ambiguity matters. It first describes thought experiments, dynamic variants of those provided by Ellsberg, that highlight a sense in which the Bayesian learning model is extreme—it models agents who are implausibly ambitious about what they can learn in complicated environments. The paper then provides a generalization of the Bayesian model that accommodates the intuitive choices in the thought experiments. In particular, the model allows decision-makers' confidence about the environment to change—along with beliefs—as they learn. A portfolio choice application compares the effect of changes in confidence under ambiguity vs. changes in estimation risk under Bayesian learning. The former is shown to induce a trend towards more stock market participation and investment even when the latter does not. Copyright 2007, Wiley-Blackwell.

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Article provided by Oxford University Press in its journal The Review of Economic Studies.

Volume (Year): 74 (2007)
Issue (Month): 4 ()
Pages: 1275-1303

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Handle: RePEc:oup:restud:v:74:y:2007:i:4:p:1275-1303
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