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.

Keywords: Alpha-MaxMin model; Ambiguity; Decision-making under uncertainty; Multiple prior models.

Figures

Fig. 1

First ambiguity question: winning for one of two ball colors. Notes: This figure shows the first round in the ambiguity question sequence with two ball colors. The respondent can win a prize of $15 if a purple ball is drawn from the box of his preference. Box K contains 50 purple and 50 orange balls, offering 50% initial known probability of winning. Box U also contains purple and orange balls, but with the proportions unknown. If the respondent selects “Box K” or “Box U”, a second question round follows, similar to the one shown above. If the response is “Box K”, in the second round the probability of winning for Box K is decreased (fewer purple balls). Vice versa, when the respondent selects “Box U”, in the second round Box K offers a higher probability of winning (more purple balls). Selecting the “Indifferent” button takes the respondent to the next ambiguity sequence, shown in Fig. 2

Fig. 2

Second ambiguity question: winning for one of ten ball colors. Notes: This figure shows the first round in the second ambiguity question sequence, with 10 ball colors. Here the respondent wins if a purple ball, 1 out of 10 colors, is drawn from the box of his preference. Box K contains 10 balls of each color and offers a 10% probability of winning. Box U also contains balls with 10 different colors, but with the proportions unknown. If the respondent selects “Box K” or “Box U”, a second question round follows, similar to the one shown above. If the response is “Box K”, in the second round the probability of winning for Box K is decreased (fewer purple balls). Vice versa, when the respondent selects “Box U”, in the second round Box K offers a higher probability of winning (more purple balls). Selecting the “Indifferent” button takes the respondent to the next ambiguity sequence: wining for nine out of ten ball colors