Markets for Models
This addresses market inefficiencies in model sales for prediction problems, but it is incremental as it builds on existing economic and machine learning concepts.
The paper tackles the problem of firms selling prediction models to a consumer in a market setting, showing that firms may choose different modeling techniques or inefficiently biased/costly models to deter competition, with outcomes expressed in terms of bias-variance decompositions.
Motivated by the prevalence of prediction problems in the economy, we study markets in which firms sell models to a consumer to help improve their prediction. Firms decide whether to enter, choose models to train on their data, and set prices. The consumer can purchase multiple models and use a weighted average of the models bought. Market outcomes can be expressed in terms of the \emph{bias-variance decompositions} of the models that firms sell. We give conditions when symmetric firms will choose different modeling techniques, e.g., each using only a subset of available covariates. We also show firms can choose inefficiently biased models or inefficiently costly models to deter entry by competitors.