Estimating ambiguity preferences and perceptions in multiple prior models: Evidence from the field
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We develop a tractable method to estimate multiple prior models of decision-making under ambiguity. In a representative sample of the U.S. population, we measure ambiguity attitudes in the gain and loss domains. We find that ambiguity aversion is common for uncertain events of moderate to high likelihood involving gains, but ambiguity seeking prevails for low likelihoods and for losses. We show that choices made under ambiguity in the gain domain are best explained by the α-MaxMin model, with one parameter measuring ambiguity aversion (ambiguity preferences) and a second parameter quantifying the perceived degree of ambiguity (perceptions about ambiguity). The ambiguity aversion parameter α is constant and prior probability sets are asymmetric for low and high likelihood events. The data reject several other models, such as MaxMin and MaxMax, as well as symmetric probability intervals. Ambiguity aversion and the perceived degree of ambiguity are both higher for men and for the college-educated. Ambiguity aversion (but not perceived ambiguity) is also positively related to risk aversion. In the loss domain, we find evidence of reflection, implying that ambiguity aversion for gains tends to reverse into ambiguity seeking for losses. Our model’s estimates for preferences and perceptions about ambiguity can be used to analyze the economic and financial implications of such preferences.
KeywordsAmbiguity Decision-making under uncertainty Multiple prior models Alpha-MaxMin model
JEL ClassificationD81 C93 C23
The survey module fielded by the authors in the RAND American Life Panel (ALP) was approved by the Institutional Review Board of the University of Pennsylvania. The authors gratefully acknowledge financial support from Netspar, and grants to the University of Pennsylvania from the National Institute on Aging (P30 AG-012836-18), and a grant from the National Institutes of Health–National Institute of Child Health and Development Population Research Infrastructure Program (R24 HD-044964-9). Support was also provided by the Pension Research Council/Boettner Center and the Wharton Behavioral Labs at the University of Pennsylvania. We also thank the ALP teams at RAND and the University of Southern California. We are grateful to Aurelien Baillon, Peter Wakker and participants at FUR 2014 for helpful comments, and to Tania Gutsche, Arie Kapteyn, Bart Orriens, and Bas Weerman for assistance with the survey. Yong Yu provided outstanding programming assistance. The content is solely the responsibility of the authors and does not represent the official views of the National Institute of Aging, the National Institutes of Health, or any of the other institutions providing funding for this study or with which the authors are affiliated.
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