title: Estimating ambiguity preferences and perceptions in multiple prior models: Evidence from the field.
abstract: 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.
authors.affiliation: Nanyang Technological University, Nanyang Business School, Singapore, Singapore.
authors.forename: Stephen G
authors.initials: SG
authors.lastname: Dimmock
authors.affiliation: College of Management, Mahidol University, 69 Vipawadee Rangsit Road, Bangkok 10140, Thailand; Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, Netherlands.
authors.forename: Roy
authors.initials: R
authors.lastname: Kouwenberg
authors.affiliation: The Wharton School, University of Pennsylvania, NBER, Philadelphia, PA, USA.
authors.forename: Olivia S
authors.initials: OS
authors.lastname: Mitchell
authors.affiliation: Bocconi University, NETSPAR, and IGIER, Milan, Italy.
authors.forename: Kim
authors.initials: K
authors.lastname: Peijnenburg
keywords: Alpha-MaxMin model; Ambiguity; Decision-making under uncertainty; Multiple prior models