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Improving Information from Manipulable Data

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  • Alex Frankel
  • Navin Kartik

Abstract

Data-based decision making must account for the manipulation of data by agents who are aware of how decisions are being made and want to affect their allocations. We study a framework in which, due to such manipulation, data become less informative when decisions depend more strongly on data. We formalize why and how a decision maker should commit to underutilizing data. Doing so attenuates information loss and thereby improves allocation accuracy.

Suggested Citation

  • 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.
    • Handle: RePEc:oup:jeurec:v:20:y:2022:i:1:p:79-115.
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    File URL: http://hdl.handle.net/10.1093/jeea/jvab017
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    Cited by:

    1. John W. Patty & Elizabeth Maggie Penn, 2022. "Algorithmic Fairness and Statistical Discrimination," Papers 2208.08341, arXiv.org.
    2. John W. Patty & Elizabeth Maggie Penn, 2023. "Algorithmic Fairness with Feedback," Papers 2312.03155, arXiv.org.
    3. Jordan Adamson & Lucas Rentschler, 2023. "Criminal justice from a public choice perspective: an introduction to the special issue," Public Choice, Springer, vol. 196(3), pages 223-227, September.
    4. Lichtig, Avi & Weksler, Ran, 2023. "Information transmission in voluntary disclosure games," Journal of Economic Theory, Elsevier, vol. 210(C).
    5. 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.

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