Joint work with Craig Fox and David Tannenbaum

Abstract: We propose that ambiguity aversion reflects distaste for betting on one’s relative ignorance, but only to the extent that uncertainty is seen as inherently knowable (epistemic). In contrast, uncertainty viewed as random (aleatory) can provide an attractive hedge against betting on one’s ignorance. We refer to this account as the epistemic uncertainty aversion hypothesis, which allows for the simultaneous observation of ambiguity aversion and preference for compound lotteries involving both chance and ambiguity. In preregistered experiments involving Ellsberg urns and naturalistic events we show: (1) under conditions of ignorance, ambiguity averse decision makers prefer betting on a greater balance of aleatory to epistemic uncertainty; (2) this preference for an aleatory hedge increases as subjective knowledge decreases, and can lead decision makers to choose stochastically dominated alternatives; (3) an uncertain prospect can be framed as more epistemic or aleatory to influence its overall attractiveness. These findings collectively violate several prominent models of ambiguity aversion, but can be accomodated by generic source models.

Available at SSRN

Joint work with Enrico Diecidue, Andreas Jacobs, and Thomas Langer

Abstract: This paper shows that group discussions can serve as an instrument to improve individuals’ calibration, which in turn strongly increases the accuracy of competence-weighted, statistical aggregates. We conduct an experiment in which participants estimate quantities and report their self-perceived competence for various judgment problems. In addition, they engage in group discussions with other judges on unrelated judgment tasks. We find that prior to participating in the group discussions, judges’ self-perceived competence and their estimation accuracy are poorly aligned, which causes competence weighting to perform worse than prediction markets and simple averaging. However, the information exchange facilitated by the group discussions improved judges’ calibration, raising the accuracy of competence-weighted aggregates on subsequent judgment problems to prediction market levels and beyond.

Available at SSRN