Abstract
I introduce a model of predictive scoring. A receiver wants to predict a sender’s quality. An intermediary observes multiple features of the sender and aggregates them into a score. Based on the score, the receiver takes a decision. The sender wants the most favorable decision, and she can distort each feature at a privately known cost. I characterize the most accurate scoring rule. This rule underweights some features to deter sender distortion, and overweights other features so that the score is correct on average. The receiver prefers this score to full disclosure because the aggregated information mitigates his commitment problem.