IDEAS home Printed from https://ideas.repec.org/p/ehl/lserod/118656.html
   My bibliography  Save this paper

Goodhart's law and machine learning: a structural perspective

Author

Listed:
  • A. Hennessy, Christopher
  • Goodhart, C. A. E.

Abstract

We develop a simple structural model to illustrate how penalized regressions generate Goodhart bias when training data are clean but covariates are manipulated at known cost by future agents. With quadratic (extremely steep) manipulation costs, bias is proportional to Ridge (Lasso) penalization. If costs depend on absolute or percentage manipulation, the following algorithm yields manipulation-proof prediction: Within training data, evaluate candidate coefficients at their respective incentive-compatible manipulation configuration. We derive analytical coefficient adjustments: slopes (intercept) shift downward if costs depend on percentage (absolute) manipulation. Statisticians ignoring manipulation costs select socially suboptimal penalization. Model averaging reduces these manipulation costs.

Suggested Citation

  • A. Hennessy, Christopher & Goodhart, C. A. E., 2023. "Goodhart's law and machine learning: a structural perspective," LSE Research Online Documents on Economics 118656, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:118656
    as

    Download full text from publisher

    File URL: http://eprints.lse.ac.uk/118656/
    File Function: Open access version.
    Download Restriction: no
    ---><---

    More about this item

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:ehl:lserod:118656. 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: LSERO Manager (email available below). General contact details of provider: https://edirc.repec.org/data/lsepsuk.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.