Year: 2025
Type: preprint
Abstract: 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.
Language: English
Subfield: General Decision Sciences
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W4413415391