Human Judgment and AI Pricing

Ajay Agrawal, Joshua Gans and Avi Goldfarb

No 24284, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: 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.

JEL-codes: D81 L12 O33 (search for similar items in EconPapers)
Date: 2018-02
New Economics Papers: this item is included in nep-big, nep-ind and nep-mic
Note: IO PR
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Citations: View citations in EconPapers (1)

Published as Ajay Agrawal & Joshua S. Gans & Avi Goldfarb, 2018. "Human Judgment and AI Pricing," AEA Papers and Proceedings, vol 108, pages 58-63.

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