IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2404.16056.html
   My bibliography  Save this paper

Intelligent Machines and Incomplete Information

Author

Listed:
  • Sujata Goala
  • Mridu Prabal Goswami
  • Surajit Borkotokey

Abstract

The distribution of efficient individuals in the economy and the efforts that they will put in if they are hired, there are two important concerns for a technologically advanced firm. wants to open a new branch. The firm does not have information about the exact level of efficiency of an individual when she is hired. We call this situation incomplete information. The standard principal agent models assume that employees know their efficiency levels. Hence these models design incentive-compatible mechanisms. An incentive-compatible mechanism ensures that a participant does not have the incentive to misreport her efficiency level. This paper does not assume that employees know how efficient they are. This paper assumes that the production technology of the firm is intelligent, that is, the output of the machine reveals the efficiency levels of employees. Employees marginal contributions to the total output of the intelligent machine, the probability distribution of the levels of efficiency and employees costs of efforts together define a game of incomplete information. A characterization of ex-ante Nash Equilibrium is established. The results of the characterization formalize the relationship between the distribution of efficiency levels and the distribution of output.

Suggested Citation

  • Sujata Goala & Mridu Prabal Goswami & Surajit Borkotokey, 2024. "Intelligent Machines and Incomplete Information," Papers 2404.16056, arXiv.org.
  • Handle: RePEc:arx:papers:2404.16056
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2404.16056
    File Function: Latest version
    Download Restriction: no
    ---><---

    More about this item

    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:arx:papers:2404.16056. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.