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Human Judgment and AI Pricing

Ajay K. AgrawalJoshua S. GansAvi Goldfarb

出版	National Bureau of Economic Research, 2018
URL	http://books.google.com.hk/books?id=pwx00AEACAAJ&hl=&source=gbs_api

註釋

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.