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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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    1. Duffie, Darrell & Dworczak, Piotr, 2018. "Robust Benchmark Design," Research Papers 3175, Stanford University, Graduate School of Business.
    2. Eduardo Perez‐Richet & Vasiliki Skreta, 2022. "Test Design Under Falsification," Econometrica, Econometric Society, vol. 90(3), pages 1109-1142, May.
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    8. Rick Harbaugh & Eric Rasmusen, 2018. "Coarse Grades: Informing the Public by Withholding Information," American Economic Journal: Microeconomics, American Economic Association, vol. 10(1), pages 210-235, February.
    9. Duffie, Darrell & Dworczak, Piotr, 2021. "Robust benchmark design," Journal of Financial Economics, Elsevier, vol. 142(2), pages 775-802.
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    Cited by:

    1. Eduardo Perez‐Richet & Vasiliki Skreta, 2022. "Test Design Under Falsification," Econometrica, Econometric Society, vol. 90(3), pages 1109-1142, May.
    2. Tsakas, Elias & Tsakas, Nikolas, 2021. "Noisy persuasion," Games and Economic Behavior, Elsevier, vol. 130(C), pages 44-61.
    3. repec:hal:spmain:info:hdl:2441/31aa5v8jtp9p48jlhrq44psjoa is not listed on IDEAS
    4. Tan, Teck Yong, 2023. "Optimal transparency of monitoring capability," Journal of Economic Theory, Elsevier, vol. 209(C).
    5. repec:hal:wpspec:info:hdl:2441/31aa5v8jtp9p48jlhrq44psjoa is not listed on IDEAS

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