Our Seminars & Workshops

Title: Screening Knowledge

A principal (she) tests an agent's (he) knowledge of a subject matter. She has preferences over his unobserved quality, which is correlated with his knowledge. Modeling knowledge as beliefs over an unknown state, I show that optimal tests are simple: They take the form of True-False, weighted True-False or True-False-Unsure, regardless of the principal's preferences, the distribution of the agent's beliefs, its correlation with his quality or his knowledge thereof. The need to elicit knowledge forces the principal to trade-off the efficacy of the test in terms of whom it rewards, against how much it rewards them. The optimal resolution of this trade-off may lead to a partial penalty for an "obvious" answer even if it is correct, a partial reward for a "counterintuitive" answer even if it is incorrect, or a reward for admitting ignorance. When the principal can pick the subject matter, she picks one that admits no such obvious answers. In this case, the highly prevalent True-False test is always optimal, regardless of principal's preferences, agent's learning, or the specific optimal choice of the subject matter.