Assumptions, Disagreement, and Overprecision: Theory and Evidence

Constructing beliefs about the world often requires simplifying assumptions. However, it is often cognitively costly or even impossible to consider how all possible assumptions might affect beliefs. We develop a formal model of individuals who properly recognize uncertainty conditional on their assumptions (“within-model uncertainty”), but do not fully appreciate the uncertainty they assume away (“across-model uncertainty”). Our main results connect this tendency to use simplified models with overprecision (too-small variance estimates) and disagreement (interpersonal variance in mean predictions). If individuals independently choose an assumption in proportion to its probability of being true, across-model uncertainty, overprecision, and disagreement exactly coincide. We explore these predictions in an experimental setting where people are given a scatterplot and provide mean and ariance estimates for out-of-sample predictions. Consistent with the theory, we find that variance stimates are more responsive to changes in within-model uncertainty than across-model uncertainty, nd that overprecision and disagreement rise with across-model uncertainty. Finally, we analyze observational data from the Survey of Professional Forecasters, and find that forecasts are overprecise, and more overprecise in problems with more disagreement.