Information projection: model and applications

People exaggerate the extent to which their information is shared with others. This paper introduces the concept of such information projection and provides a simple but widely applicable model. The key application describes a novel agency conflict in a frictionless learning environment. When monitoring with ex post information, biased evaluators exaggerate how much experts could have known ex ante and underestimate experts on average. Experts, to defend their reputations, are too eager to base predictions on ex ante information that substitutes for the information jurors independently learn ex post and too reluctant to base predictions on ex ante information that complements the information jurors independently learn ex post. Instruments that mitigate Bayesian agency conflicts are either ineffective or directly backfire. Applications to defensive medicine are discussed.