Measuring Beliefs Under Ambiguity

People’s beliefs about uncertain events play a pivotal role in real-world decisions. Examples include entrepreneurs who have to assess the chance that their business activities will be successful, patients who have to decide on a risky treatment, and policy makers who have to assess the risks of climate change. Existing methods to measure beliefs are complex and make restrictive assumptions that limit their use in assisting and understanding everyday decision-making. In “Measuring Beliefs under Ambiguity,” Mohammed Abdelloui, Han Bleichrodt, Emmanuel Kemel, and Olivier l’Haridon propose a simple method to measure beliefs that is valid under general assumptions and that requires only three measurements. The ease of the method makes it straightforward to use in practice. An experiment of expected temperatures shows that the beliefs elicited by the method are well calibrated and similar to those elicited by more complex and time-consuming methods. This study provides an easy tool to help both individuals and policy makers make better decisions. This paper presents a simple method to measure the beliefs of a decision-maker with nonneutral ambiguity attitudes. Our method requires three simple choices, is incentive compatible, and allows for risk aversion and deviations from expected utility, including probability weighting and ambiguity aversion. An experiment using two natural sources of uncertainty (the temperature in Rotterdam and in New York City) shows that the model’s estimated beliefs are well calibrated, sensitive to the source of uncertainty, and similar to beliefs estimated by more sophisticated but time-consuming methods.