People are often called upon to make important decisions involving uncertainty in domains in which they are not experts, such as medical treatment, financial planning and precautions for severe weather. The mission of the DMU lab is to uncover the psychological factors that impact such decisions in order to determine how best to support them. We are particularly interested in discovering methods for presenting relevant uncertainty information to decision-makers to improve decision quality.
Most of our work to date has been in the domain of weather, which provides an excellent model for decision-making under uncertainty because high-quality uncertainty estimates are available from numerical models. Our research suggests that everyday users make better decisions when they have the relevant information. They understand and benefit from fairly complex data, such as explicit numeric uncertainty estimates, if it is carefully expressed. Moreover, people intuitively understand that forecasts involve uncertainty and have greater trust in forecasts that include explicit numeric uncertainty estimates than when this information is omitted. This research has implications for decisions in many other domains.