Ilke Aydogan

We report the results of two experiments designed to better understand the mechanisms driving decision making under ambiguity. We elicit individual preferences over different sources of uncertainty, entailing different degrees of complexity, from subjects with different sophistication levels. We show that (1) ambiguity aversion is robust to sophistication, but the strong relationship previously reported between attitudes toward ambiguity and compound risk is not and (2) Ellsberg ambiguity attitude can be partly explained by attitudes toward complexity for less sophisticated subjects only. Overall, regardless of the subject’s sophistication level, the main driver of Ellsberg ambiguity attitude is a specific treatment of unknown probabilities.

We investigate the validity of a double random incentive system where only a subset of subjects is paid for one of their choices. By focusing on individual decision-making under risk and ambiguity, we show that using either a standard random incentive system, where all subjects are paid, or a double random system, where only 10% of subjects are paid, yields similar preference elicitation results. These findings suggest that adopting a double random incentive system could significantly reduce experimental costs and logistic efforts, thereby facilitating the exploration of individual decision-making in larger-scale and higher-stakes experiments.

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 different 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.

We explore decision-making under uncertainty using a framework that decom- poses uncertainty into three distinct layers: (1) risk, which entails inherent random- ness within a given probability model; (2) model ambiguity, which entails uncertainty about the probability model to be used; and (3) model misspecification, which en- tails 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 different 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.

Prior beliefs and their updating play a crucial role in decisions under uncertainty, and theories about them have been well established in classical Bayesianism. Yet, they are almost absent for ambiguous decisions from experience. This paper proposes a new decision model that incorporates the role of prior beliefs, beyond the role of ambiguity attitudes, into the analysis of such decisions. Hence, it connects ambiguity theories, popular in economics, with decision from experience, popular (mostly) in psychology, to the benefit of both. A reanalysis of some existing data sets from the literature on decisions from experience shows that the model that incorporates prior beliefs into the estimation of subjective probabilities outperforms the commonly used model that approximates subjective probabilities with observed relative frequencies. Controlling for subjective priors, we obtain more accurate measurements of ambiguity attitudes, and thus a new explanation of the gap between decision from description and decision from experience. This paper was accepted by Manel Baucells, decision analysis.

A recent strand of the literature on decision-making under uncertainty has pointed to an intriguing behavioral gap between decisions made from description and decisions made from experience. This study reinvestigates this description-experience gap to understand the impact that sampling experience has on decisions under risk. Our study adopts a complete sampling paradigm to address the lack of control over experienced probabilities by requiring complete sampling without replacement. We also address the roles of utilities and ambiguity, which are central in most current decision models in economics. Thus, our experiment identifies the deviations from expected utility due to over- (or under-) weighting of probabilities. Our results confirm the existence of the behavioral gap, but they provide no evidence for the underweighting of small probabilities within the complete sampling treatment. We find that sampling experience attenuates rather than reverses the inverse S-shaped probability weighting under risk.