Article

Eliciting ambiguity aversion in unknown and in compound lotteries: A smooth ambiguity model experimental study

Giuseppe Marco Attanasi, Christian Gollier, Aldo Montesano, and Noémie Pace

Abstract

Coherent-ambiguity aversion is defined within the (Klibanoff et al., Econometrica 73:1849–1892, 2005) smooth-ambiguity model (henceforth KMM) as the combination of choice-ambiguity and value-ambiguity aversion. Five ambiguous decision tasks are analyzed theoretically, where an individual faces two-stage lotteries with binomial, uniform, or unknown second-order probabilities. Theoretical predictions are then tested through a 10-task experiment. In (unambiguous) tasks 1–5, risk aversion is elicited through both a portfolio choice method and a BDM mechanism. In (ambiguous) tasks 6–10, choice-ambiguity aversion is elicited through the portfolio choice method, while value-ambiguity aversion comes about through the BDM mechanism. The behavior of over 75 % of classified subjects is in line with the KMM model in all tasks 6–10, independent of their degree of risk aversion. Furthermore, the percentage of coherent-ambiguity-averse subjects is lower in the binomial than in the uniform and in the unknown treatments, with only the latter difference being significant. The most part of coherent-ambiguity-loving subjects show a high risk aversion.

Keywords

Coherent-ambiguity aversion; Value-ambiguity aversion; Choice-ambiguity aversion; Smooth ambiguity model; Binomial distribution; Uniform distribution; Unknown urn;

Replaces

Giuseppe Marco Attanasi, Christian Gollier, Aldo Montesano, and Noémie Pace, Eliciting ambiguity aversion in unknown and in compound lotteries: A KMM experimental approach, TSE Working Paper, n. 12-338, September 15, 2012.

Reference

Giuseppe Marco Attanasi, Christian Gollier, Aldo Montesano, and Noémie Pace, Eliciting ambiguity aversion in unknown and in compound lotteries: A smooth ambiguity model experimental study, Theory and Decision, vol. 77, n. 4, December 2014, pp. 485–530.

Published in

Theory and Decision, vol. 77, n. 4, December 2014, pp. 485–530