The Effect of Learning on Ambiguity Attitudes

This paper studies the effect of learning information on people’s attitudes toward ambiguity. We propose a method to separate ambiguity attitudes from subjective probabilities and to decompose ambiguity attitudes into two components. Under models like prospect theory that represent ambiguity through...

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Bibliographic Details
Published in:IDEAS Working Paper Series from RePEc 2018-05, Vol.64 (5), p.2181-2198
Main Authors: Baillon, Aurélien, Bleichrodt, Han, Keskin, Umut, l’Haridon, Olivier, Li, Chen
Format: Article
Language:English
Subjects:
Ambiguity
Attitudes
Consumer attitudes
Economics and Finance
Expected utility
Humanities and Social Sciences
Initial public offerings
Learning
neo-additive weighting
Pessimism
Psychological aspects
Psychological research
Risk aversion
Stock exchanges
updating
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Summary:This paper studies the effect of learning information on people’s attitudes toward ambiguity. We propose a method to separate ambiguity attitudes from subjective probabilities and to decompose ambiguity attitudes into two components. Under models like prospect theory that represent ambiguity through nonadditive decision weights, these components reflect pessimism and likelihood insensitivity. Under multiple priors models, they reflect ambiguity aversion and perceived ambiguity. We apply our method in an experiment where we elicit the ask prices of options with payoffs depending on the returns of initial public offerings (IPOs) on the New York Stock Exchange. IPOs are a natural context to study the effect of learning, as prior information about their returns is unavailable. Subjects perceived substantial ambiguity and they were insensitive to likelihood information. We observed only little pessimism and ambiguity aversion. Subjective probabilities were well calibrated and close to the true frequencies. Subjects’ behavior moved toward expected utility with more information, but substantial deviations remained even in the maximum information condition. Data and the online appendix are available at https://doi.org/10.1287/mnsc.2016.2700 This paper was accepted by Manel Baucells, decision analysis.
ISSN:0025-1909
1526-5501
DOI:10.1287/mnsc.2016.2700