Human Judgment and AI Pricing
NBER Working Paper No. 24284
Recent artificial intelligence advances can be seen as improvements in prediction. We examine how such predictions should be priced. We model two inputs into decisions: a prediction of the state and the payoff or utility from different actions in that state. The payoff is unknown, and can only be learned through experiencing a state. It is possible to learn that there is a dominant action across all states, in which case the prediction has little value. Therefore, if predictions cannot be credibly contracted upfront, the seller cannot extract the full value, and instead charges the same price to all buyers.
Document Object Identifier (DOI): 10.3386/w24284
Published: Ajay Agrawal & Joshua S. Gans & Avi Goldfarb, 2018. "Human Judgment and AI Pricing," AEA Papers and Proceedings, vol 108, pages 58-63. citation courtesy of
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