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Optimal Learning and Ellsberg’s Urns

Author

Listed:
  • Larry G. Epstein

    (Department of Economics, Boston University)

  • Shaolin Ji

    (Institute of Financial Studies, Shandong University)

Abstract

We consider the dynamics of learning under ambiguity when learning is costly and is chosen optimally. The setting is Ellsberg’s two-urn thought experiment modified by allowing the agent to postpone her choice between bets so that she can learn about the composition of the ambiguous urn. Signals are modeled by a diffusion process whose drift is equal to the true bias of the ambiguous urn and they are observed at a constant cost per unit time. The resulting optimal stopping problem is solved and the effect of ambiguity on the extent of learning is determined. It is shown that rejection of learning opportunities can be optimal for an ambiguity averse agent even given a small cost.

Suggested Citation

  • Larry G. Epstein & Shaolin Ji, 2017. "Optimal Learning and Ellsberg’s Urns," Boston University - Department of Economics - Working Papers Series WP2017-010, Boston University - Department of Economics.
    • Handle: RePEc:bos:wpaper:wp2017-010
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    File URL: http://www.bu.edu/econ/files/2017/09/ResponseTime-QJE-Aug23-2017.pdf
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    References listed on IDEAS

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    Cited by:

    1. Dew-Becker, Ian & Nathanson, Charles G., 2019. "Directed attention and nonparametric learning," Journal of Economic Theory, Elsevier, vol. 181(C), pages 461-496.

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    Keywords

    ambiguity; learning; partial information; optimal stopping; drift-diffusion model;
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