When a new disease appears (e.g., the swine flu), we do not have a precise idea of the likelihood of the threats we are facing, unlike with known diseases (e.g., the regular flu). A situation in which probabilities are unknown is called ambiguous. When probabilities are known, the context is risky. Ambiguity is not only frequent in medical decisions, but also, for example, in financial and environmental decisions (e.g., global warming). Many people are averse to ambiguity, i.e. they are more careful under ambiguity than under risk. This behavior might seem reasonable, but ambiguity aversion may lead to decisions that are not in the decision makers' best interests. Ambiguity aversion has hardly been studied in medical decisions and it is unclear what policy makers should do about it.
This research project aims to develop a set of methods to take ambiguity into account in policy decisions, with a special focus on health policy. It will be organized around three current challenges in the ambiguity literature.
First, recent research has raised doubts about the way ambiguity attitude has commonly been modeled. We currently do not know which way of modeling is most appropriate. This project will shed light on this question.
Second, the psychological literature on "sources of uncertainty" has highlighted that ambiguity attitude depends on how competent decision makers feel about the type of uncertainty they are facing. However, little is known about the way attitudes change when people learn about sources of uncertainty. I will study the impact of learning on ambiguity attitude (how does our behavior change when we become more familiar with the swine flu?).
Finally, I will assess whether the impact of ambiguity on health-related decisions is negative and how governments could improve health policies by taking into account attitudes towards ambiguity.