Key Findings
Many of our findings are surprising. For instance, although some experts contend that numerical uncertainty information (e.g., 30% chance) is too complex for most people to understand, our research suggests something different. We have shown that people, regardless of education level, can understand and use numerical uncertainty information to make better decisions, as long as it is presented in a way that is compatible with the natural decision process. This is not to say that the resulting decisions are economically optimal or that most people have a deep theoretical understanding of probability theory. We know they do not. Nonetheless, decisions improve when numerical uncertainty estimate are provided compared to when they are withheld as shown in these papers: [5][7][8][9][10][11][13][14][15][16][18][20][22][23][24][25][26][28][29][30][32][33][41][44]
In addition, contradicting the adage, a picture is worth a thousand words, we find that although visualizations of uncertainty information can sometimes be helpful, in many cases they are also confusing. As a result, we recommend that their use should be carefully considered and suggest some alternative strategies for communication uncertainty in these papers: [6][19][36][39]
Another example is the belief that most people are confused by and distrust forecasts for the same event that are inconsistent with one another (e.g., Monday forecast: 2 inches of snow on Friday; Tuesday Forecast: 4 inches of snow on Friday). Our research contracts these notions as well. In fact, inaccuracy is a far bigger problem to trust in forecasts than is inconsistency. Moreover, people glean important information from forecast inconsistency that helps them to make better decisions in some cases. [31][35][42][44]
We have also found that people can understand fairly complex explanations of scientific processes (e.g., mechanisms of mRNA vaccines and climate change) as long as they are expressed in everyday language and target known misunderstandings. [37][38][43]
Methodological Approach
We use several methodological approaches in our research. Cognitive-ethnographic work based on observations and surveys help us gain an understanding of the context, background knowledge and goals with which people approach critical decisions.
In addition, we use experimental methods, incorporating realistic decisions often with actual monetary rewards, in computer simulations, to test specific questions.
Because our work is applied, it is by nature interdisciplinary. We work directly with domain experts to understand decision-making in context as well as the role and sources of uncertainty in each domain. The benefit of interdisciplinary collaboration from the behavioral science perspective is that it sharpens the questions we ask and ensures the ecological validity of the stimuli we test. This is particularly important when studying people’s understanding of something for which they have prior experience, as is often the case with applied questions. If the stimuli contradict prior experience, even subtly, it may have an unintended influence on participants responses and invalidate research results.
Students joining the lab will learn about the intersection of traditional decision theory and applied questions. They will help to pursue research designed to bridge the gaps between these two approaches and impact real-world situations. They will have the opportunity to work on interdisciplinary teams and present their research in a number of interdisciplinary settings.