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Test Design Under Falsification

Author

Listed:
  • Eduardo Perez

    (ECON - Département d'économie (Sciences Po) - Sciences Po - Sciences Po - CNRS - Centre National de la Recherche Scientifique, CEPR - Center for Economic Policy Research - CEPR)

  • Vasiliki Skreta

    (University of Texas at Austin [Austin])

Abstract

We characterize a receiver-optimal test when manipulations are possible in the form of type falsification. Optimal design exploits the following manipulator trade-off: while falsification may lead to better grades, it devalues their meaning. We show that optimal tests can be derived among falsification-proof ones. Our optimal test has a single ‘failing' grade, and a continuum of ‘passing' grades. It makes the manipulator indifferent across all moderate levels of falsification. Good types never fail, but bad types may pass. An optimal test delivers at least half of the full-information value to the receiver. A three-grade optimal test also performs well.

Suggested Citation

  • Eduardo Perez & Vasiliki Skreta, 2018. "Test Design Under Falsification," Working Papers hal-03393136, HAL.
    • Handle: RePEc:hal:wpaper:hal-03393136
    Note: View the original document on HAL open archive server: https://sciencespo.hal.science/hal-03393136
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Jacopo Bizzotto & Alessandro De Chiara, 2022. "Frequent audits and honest audits," Working Papers 202202, Oslo Metropolitan University, Oslo Business School.
    2. Bizzotto, Jacopo & Rüdiger, Jesper & Vigier, Adrien, 2020. "Testing, disclosure and approval," Journal of Economic Theory, Elsevier, vol. 187(C).
    3. Smolin, Alex & Doval, Laura, 2021. "Information Payoffs: An Interim Perspective," TSE Working Papers 21-1247, Toulouse School of Economics (TSE).
    4. Terstiege, Stefan & Wasser, Cédric, 2020. "Buyer-optimal extensionproof information," Journal of Economic Theory, Elsevier, vol. 188(C).
    5. Alonso, Ricardo & Câmara, Odilon, 2021. "Organizing Data Analytics," CEPR Discussion Papers 16768, C.E.P.R. Discussion Papers.
    6. Tsakas, Elias & Tsakas, Nikolas, 2021. "Noisy persuasion," Games and Economic Behavior, Elsevier, vol. 130(C), pages 44-61.
    7. Parakhonyak, Alexei & Vikander, Nick, 2023. "Information design through scarcity and social learning," Journal of Economic Theory, Elsevier, vol. 207(C).
    8. Alex Frankel & Navin Kartik, 2019. "Improving Information from Manipulable Data," Papers 1908.10330, arXiv.org, revised Feb 2021.
    9. Nguyen, Anh & Tan, Teck Yong, 2021. "Bayesian persuasion with costly messages," Journal of Economic Theory, Elsevier, vol. 193(C).
    10. Laura Doval & Alex Smolin, 2021. "Persuasion and Welfare," Papers 2109.03061, arXiv.org, revised Sep 2023.
    11. Tan, Teck Yong, 2023. "Optimal transparency of monitoring capability," Journal of Economic Theory, Elsevier, vol. 209(C).
    12. Eilat, Ran & Neeman, Zvika, 2023. "Communication with endogenous deception costs," Journal of Economic Theory, Elsevier, vol. 207(C).
    13. Redlicki, Bartosz & Redlicki, Jakub, 2022. "Communication with Costly and Detectable Falsification," Journal of Economic Behavior & Organization, Elsevier, vol. 202(C), pages 452-470.
    14. Yingkai Li & Xiaoyun Qiu, 2023. "Screening Signal-Manipulating Agents via Contests," Papers 2302.09168, arXiv.org, revised Feb 2024.
    15. Garrett, Daniel F. & Georgiadis, George & Smolin, Alex & Szentes, Balázs, 2023. "Optimal technology design," Journal of Economic Theory, Elsevier, vol. 209(C).
    16. Monte, Daniel & Linhares, Luis Henrique, 2023. "Stealth Startups, Clauses, and Add-ons: A Model of Strategic Obfuscation," MPRA Paper 115926, University Library of Munich, Germany.
    17. Babichenko, Yakov & Talgam-Cohen, Inbal & Xu, Haifeng & Zabarnyi, Konstantin, 2022. "Regret-minimizing Bayesian persuasion," Games and Economic Behavior, Elsevier, vol. 136(C), pages 226-248.
    18. Teddy Mekonnen & Zeky Murra-Anton & Bobak Pakzad-Hurson, 2023. "Persuaded Search," Papers 2303.13409, arXiv.org, revised Oct 2023.

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    More about this item

    Keywords

    Information Design; Falsification; Tests; Manipulation; Cheating; Persuasion;
    All these keywords.

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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