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Human Judgment and AI Pricing

Author

Listed:
  • 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.

Suggested Citation

  • Ajay K. Agrawal & Joshua S. Gans & Avi Goldfarb, 2018. "Human Judgment and AI Pricing," NBER Working Papers 24284, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:24284
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    Cited by:

    1. Agrawal, Ajay & Gans, Joshua S. & Goldfarb, Avi, 2019. "Exploring the impact of artificial Intelligence: Prediction versus judgment," Information Economics and Policy, Elsevier, vol. 47(C), pages 1-6.
    2. Marie Obidzinski & Yves Oytana, 2022. "Advisory algorithms and liability rules," Working Papers 2022-04, CRESE.

    More about this item

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • L12 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Monopoly; Monopolization Strategies
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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