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Improving Information from Manipulable Data

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  • Alex Frankel
  • Navin Kartik

Abstract

Data-based decisionmaking 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 becomes less informative when decisions depend more strongly on data. We formalize why and how a decisionmaker should commit to underutilizing data. Doing so attenuates information loss and thereby improves allocation accuracy.

Suggested Citation

  • Alex Frankel & Navin Kartik, 2019. "Improving Information from Manipulable Data," Papers 1908.10330, arXiv.org, revised Feb 2021.
    • Handle: RePEc:arx:papers:1908.10330
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    References listed on IDEAS

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    Cited by:

    1. John W. Patty & Elizabeth Maggie Penn, 2023. "Algorithmic Fairness with Feedback," Papers 2312.03155, arXiv.org.
    2. Eduardo Perez‐Richet & Vasiliki Skreta, 2022. "Test Design Under Falsification," Econometrica, Econometric Society, vol. 90(3), pages 1109-1142, May.
    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. Christa N. Gibbs & Benedict Guttman-Kenney & Donghoon Lee & Scott T. Nelson & Wilbert H. van der Klaauw & Jialan Wang, 2024. "Consumer Credit Reporting Data," NBER Working Papers 32791, National Bureau of Economic Research, Inc.
    6. Tsakas, Elias & Tsakas, Nikolas, 2021. "Noisy persuasion," Games and Economic Behavior, Elsevier, vol. 130(C), pages 44-61.
    7. 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.
    8. Jiadong Gu, 2024. "Data Trade and Consumer Privacy," Papers 2406.12457, arXiv.org, revised Jul 2024.
    9. Tan, Teck Yong, 2023. "Optimal transparency of monitoring capability," Journal of Economic Theory, Elsevier, vol. 209(C).
    10. John W. Patty & Elizabeth Maggie Penn, 2022. "Algorithmic Fairness and Statistical Discrimination," Papers 2208.08341, arXiv.org.

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