Learning under ambiguity: An experiment using initial public offerings on a stock market

This paper studies the effect of learning new information on decision under uncertainty.Using ambiguity models, we show the effect of learning on beliefs and ambiguity attitudes. We develop a new method to correct beliefs for ambiguity attitudes and decompose ambiguity attitudes into pessimism (capturing ambiguity aversion) and likelihood insensitivity. We apply our method in an experiment using initial public offerings (IPOs) on the New York Stock Exchange. IPOs provide a natural decision context in which no prior information on returns is available. We found that likelihood insensitivity decreased with information, but pessimism was unaffected. Subjects moved in the direction of expected utility with more information, but significant deviations remained. Subjective probabilities,corrected for ambiguity attitudes, were well calibrated and close to market data.