We show that the formalization of representativeness (Kahneman and Tversky (1972)) developed by Gennaioli and Shleifer (2010) features an intrinsic connection between uncertainty and overreaction. In the time series domain, we develop this connection in a smooth version of Diagnostic Expectations (DE), where overreaction varies smoothly with uncertainty. Intuitively, under smoothness, lower uncertainty leaves less room for representativeness to distort beliefs. Smooth DE provides a joint, parsimonious micro-foundation for key features of survey data: overreaction to news, stronger overreaction at longer horizons, overconfidence in subjective uncertainty, and a new stylized fact documented here—overreaction intensifies with higher uncertainty. An analytical Real Business Cycle model with Smooth DE accounts for survey overreaction and overconfidence, as well as three salient business cycle properties: asymmetry, countercyclical macro volatility, and countercyclical micro volatility.

We propose a new theory of price rigidity based on firms' Knightian uncertainty about their competitive environment. This uncertainty has two key implications. First, firms learn about the shape of their demand function from past observations of quantities sold. This learning gives rise to kinks in the expected profit function at previously observed prices, making those prices both sticky and more likely to reoccur. Second, uncertainty about the relationship between aggregate and industry‐level inflation generates nominal rigidity. We prove the main insights analytically and quantify the effects of our mechanism. Our estimated quantitative model is consistent with a wide range of micro‐level pricing facts that are typically challenging to match jointly. It also implies significantly more persistent monetary non‐neutrality than in standard models, allowing it to generate large real effects from nominal shocks.