IDEAS home Printed from https://ideas.repec.org/a/aea/apandp/v108y2018p58-63.html
   My bibliography  Save this article

Human Judgment and AI Pricing

Author

Listed:
  • Ajay Agrawal
  • Joshua S. Gans
  • Avi Goldfarb

Abstract

This paper examines the pricing choices of a provider of artificial intelligence (AI) services. It does so in the context of AI providing predictions to a decision-maker who also exercises what we term judgment; specifically, the discovery of payoffs from action/state pairs. An AI facilitates the decision-maker obtaining judgment through experience, which is one source of demand for AI services. The other source is prediction when (and if) the decision-maker has a need for state-contingent decision-making. We show that the need to encourage learning means that the AI provider is constrained in its ability to extract rents from decision-makers.

Suggested Citation

  • Ajay Agrawal & Joshua S. Gans & Avi Goldfarb, 2018. "Human Judgment and AI Pricing," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 58-63, May.
    • Handle: RePEc:aea:apandp:v:108:y:2018:p:58-63
    Note: DOI: 10.1257/pandp.20181022
    as

    Download full text from publisher

    File URL: https://www.aeaweb.org/doi/10.1257/pandp.20181022
    Download Restriction: no
    File URL: https://www.aeaweb.org/articles/attachments?retrieve=E1bVNES-KEUZne1CyAIF2UNL4rbVODRM
    Download Restriction: Access to full text is restricted to AEA members and institutional subscribers.
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Marie Obidzinski & Yves Oytana, 2022. "Advisory algorithms and liability rules," Working Papers 2022-04, CRESE.
    2. 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.

    More about this item

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aea:apandp:v:108:y:2018:p:58-63. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Michael P. Albert (email available below). General contact details of provider: https://edirc.repec.org/data/aeaaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.