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Learning from ambiguous urns

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Abstract

We provide conditions under which ambiguity fades away in sampling with replacement from the same “ambiguous” urn.

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Author information

Authors and Affiliations

  1. Dipartimento di Statistica e Matematica Applicata, Universitá di Torino, Piazza Arbarello 8, 10122, Torino, Italy

    Massimo Marinacci

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  1. Massimo Marinacci
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Corresponding author

Correspondence to Massimo Marinacci.

Additional information

We wish to thank Paolo Ghirardato, Marco Scarsini, an anonymous referee, and especially Larry Epstein for helpful comments. The financial support of MIUR is gratefully acknowledged.

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Marinacci, M. Learning from ambiguous urns. Statistical Papers 43, 143–151 (2002). https://doi.org/10.1007/s00362-001-0092-5

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  • DOI: https://doi.org/10.1007/s00362-001-0092-5

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