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Goodhart'S Law And Machine Learning: A Structural Perspective

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  • Christopher A. Hennessy
  • Charles A. E. Goodhart

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

  • Christopher A. Hennessy & Charles A. E. Goodhart, 2023. "Goodhart'S Law And Machine Learning: A Structural Perspective," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 1075-1086, August.
    • Handle: RePEc:wly:iecrev:v:64:y:2023:i:3:p:1075-1086
    DOI: 10.1111/iere.12633
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    References listed on IDEAS

    as
    1. Ian Ball, 2019. "Scoring Strategic Agents," Papers 1909.01888, arXiv.org, revised May 2024.
    2. Alex Frankel & Navin Kartik, 2022. "Improving Information from Manipulable Data," Journal of the European Economic Association, European Economic Association, vol. 20(1), pages 79-115.
    3. Lucas, Robert Jr, 1976. "Econometric policy evaluation: A critique," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 1(1), pages 19-46, January.
    Full references (including those not matched with items on IDEAS)

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