Three layers of uncertainty

Ilke Aydogan (), Loïc Berger, Valentina Bosetti and Ning Liu ()
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Ilke Aydogan: IESEG School of Management, Univ. Lille, CNRS, UMR 9221 - LEM - Lille Economie Management, F-59000 Lille, France; and iRisk Research Center on Risk and Uncertainty
Ning Liu: School of Economics and Management, Beihang University and Laboratory for Low-carbon Intelligent Governance, Beihang University, China

Working Papers from IESEG School of Management

Abstract: We explore decision-making under uncertainty using a framework that decomposes uncertainty into three distinct layers: (1) risk, which entails inherent randomness within a given probability model; (2) model ambiguity, which entails uncertainty about the probability model to be used; and (3) model misspecification, which entails uncertainty about the presence of the correct probability model among the set of models considered. Using a new experimental design, we isolate and measure attitudes towards each layer separately. We conduct our experiment on three di?erent subject pools and document the existence of a behavioral distinction between the three layers. In addition to providing new insights into the underlying processes behind ambiguity aversion, we provide the first empirical evidence of the role of model misspecification in decision-making under uncertainty.

Keywords: : Ambiguity aversion; model uncertainty; model misspecification; non-expected utility; reduction of compound lotteries (search for similar items in EconPapers)
JEL-codes: D81 (search for similar items in EconPapers)
Pages: 36 pages
Date: 2022-11
New Economics Papers: this item is included in nep-exp, nep-rmg and nep-upt
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Three Layers of Uncertainty (2023)
Working Paper: Three Layers of Uncertainty (2023)
Working Paper: Three layers of uncertainty (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:ies:wpaper:e202211

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