Diletta Topazio’s research while affiliated with LUISS Guido Carli, Libera Università Internazionale degli Studi Sociali and other places

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Publications (1)


Stage 1 user interface for the Risk treatment
Stage 2 user interface for the Risk treatment
Kernel density of the individual Certainty Equivalent. Grey: density under ambiguity. Black: density under compound risk. Dashed line: density under risk. Confidence intervals at a 5% significance level drawn in light grey. TR0 (TR1) [TR2] correspond to the Ambiguity (Compound Risk) [Risk] treatments, respectively
Histogram of the distribution of the curvature of the value function (ρ) in the Risk treatment. Only 1 out of 88 estimations is not statistically different from 0 at the 5% level
Subject-by-subject estimated parameters for the curvature of the value function (ρ) and the curvature of the probability weighting function (γ) under the naïve (Panel a) and sophisticated (Panel b) model. TR0 (TR1) correspond to the Ambiguity (Compound Risk) treatments, respectively

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An experiment on outcome uncertainty
  • Article
  • Full-text available

April 2025

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18 Reads

Journal of Risk and Uncertainty

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Carmen Herrero

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Diletta Topazio

We report the evidence of a multi-stage lab experiment on individual decision making under ambiguity, where the latter is characterized by the (partial or) absence of information on some monetary values in the support of the lottery distributions. This complements the standard treatment of uncertainty where decision makers know the monetary prizes, but probabilities are uncertain. We use both a structural and a non-structural approach when analyzing subjects’ behavior under risk, compound risk, and outcome ambiguity. Our main finding is that subjects are risk-averse and ambiguity lovers in that they evaluate more optimistically uncertain payoffs under ambiguity compared to compound risk. We also study how subjects evaluate scenarios with uncertain outcomes: 60% of choices are consistent with the Expected Utility paradigm, while 40% of them are better described by a heuristic we label as “naïve,” in which the order of integration of Expected Utility is reversed (that is, they first form a point estimate of the uncertain payoffs, and then they evaluate the lotteries’ expected utility). Finally, we also find that risk and ambiguity aversion are positively correlated.

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