Working Paper: NBER ID: w24284
Authors: Ajay K. Agrawal; Joshua S. Gans; Avi Goldfarb
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.
Keywords: No keywords provided
JEL Codes: D81; L12; O33
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| AI (C45) | decision-making quality (L15) |
| learning about actions (C99) | AI service demand (C45) |
| experience level (J24) | pricing strategy (L11) |
| credibility of contracts (D86) | pricing strategies (D49) |