I introduce a model of scoring. A receiver tries to predict a latent characteristic of the sender . The sender has a vector of features, which can be distorted at a cost. An intermediary observes the features of the sender and assigns a linear score, which is passed to the receiver. The optimal scoring rule downweights features on which gaming is heterogeneous. Optimal scoring discourages sender manipulation and allows the receiver to make more accurate predictions than full transparency .
I introduce a model of scoring. An intermediary aggregates multiple features of a sender into a score. A receiver sees this score and takes a decision. The receiver wants his decision to match the sender's latent characteristic. But the sender wants the most favorable decision, and she can distort each of her features at a private cost. I characterize the receiver-optimal scoring rule. This rule underweights some features to deter sender distortion, and overweights other features to keep the score unbiased. The receiver prefers this score to seeing the sender's full features because the coarser information mitigates his commitment problem .
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An intermediary aggregates multiple features of a sender into a score. A receiver sees this score and takes a decision. The receiver wants his decision to match the sender's latent characteristic. But the sender wants the most favorable decision, and she can distort each of her features at a private cost. I characterize the receiver-optimal scoring rule. This rule underweights some features to deter sender distortion, and overweights other features to keep the score unbiased. The
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tries to predict a latent characteristic of the sender . The sender has a vector of features, which can be distorted at a cost. An intermediary observes the features of the sender and assigns a linear score, which is passed to the receiver. The optimal scoring rule downweights features on which gaming is heterogeneous. Optimal scoring discourages sender manipulation and allows the receiver to make more accurate predictions than full transparency
prefers this score to seeing the sender's full features because the coarser information mitigates his commitment problem