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Model misspecification is a common approach to model belief formation distortions. Misspecified models can be decomposed into two classes of distortions: prospective and retrospective biases (Bohren and Hauser 2023). Prospective biases correspond to distortions in forecasting future beliefs, while retrospective biases correspond to distortions in interpreting information ex post. We disentangle the impact of these two distortions on optimal lending contracts in the context of an entrepreneur who borrows to invest in a project. The entrepreneur learns about project quality from a signal, which she interprets with a misspecified model. A lender leverages each form of bias in distinct ways. 

This paper develops a general framework to study how misinterpreting information impacts learning. Our main result is a simple criterion to characterize long‐run beliefs based on the underlying form of misspecification. We present this characterization in the context of social learning, then highlight how it applies to other learning environments, including individual learning. A key contribution is that our characterization applies to settings with model heterogeneity and provides conditions for entrenched disagreement. Our characterization can be used to determine whether a representative agent approach is valid in the face of heterogeneity, study how differing levels of bias or unawareness of others' biases impact learning, and explore whether the impact of a bias is sensitive to parametric specification or the source of information. This unified framework synthesizes insights gleaned from previously studied forms of misspecification and provides novel insights in specific applications, as we demonstrate in settings with partisan bias, overreaction, naive learning, and level‐k reasoning.