We offer a #datadriven theory of #belief formation that explains sudden surges in economic #uncertainty and their consequences. It argues that people, like #bayesian econometricians, estimate a distribution of macroeconomic outcomes but do not know the true distribution. The paper shows how real‑time estimation of distributions with non‑normal tails can result in large fluctuations in uncertainty, particularly related to tail events or "black swans." Using real‑time GDP data, the authors find that revisions in estimated #blackswan #risk explain most of the fluctuations in uncertainty. These findings highlight the importance of #accounting for the effects of uncertainty and non‑normality in economic decision‑making and #policymaking.