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Measuring and Disentangling Ambiguity and Confidence in the Lab

Department of Economics and Finance, Luiss Guido Carli, 00198 Rome, Italy
Department of Economics, Bocconi University, 20136 Milano, Italy
Author to whom correspondence should be addressed.
Games 2019, 10(1), 9;
Received: 7 December 2018 / Revised: 28 January 2019 / Accepted: 28 January 2019 / Published: 18 February 2019
(This article belongs to the Special Issue The Empirics of Behaviour under Risk and Ambiguity)
PDF [600 KB, uploaded 18 February 2019]


In this paper we present a novel experimental procedure aimed at better understanding the interaction between confidence and ambiguity attitudes in individual decision making. Different ambiguity settings not only can be determined by the lack of information in possible scenarios completely “external” to the decision-maker, but can also be a consequence of the decision maker’s ignorance about her own characteristics or performance and, thus, deals with confidence. We design a multistage experiment where subjects face different sources of ambiguity and where we are able to control for self-assessed levels of competence. By means of a Principal Component Analysis, we obtain a set of measures of “internal” and “external” ambiguity aversion. Our regressions show that the two measures are significantly correlated at the subject level, that the subjects’ “internal” ambiguity aversion increases in performance in the high-competence task and that “external” ambiguity aversion moderately increases in earnings. Self-selection does not play any role. View Full-Text
Keywords: ambiguity; confidence; competence; precision; self-selection ambiguity; confidence; competence; precision; self-selection
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Di Cagno, D.; Grieco, D. Measuring and Disentangling Ambiguity and Confidence in the Lab. Games 2019, 10, 9.

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