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Optimal Revelation of Life-Changing Information

Author

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  • Nikolaus Schweizer

    (Department of Econometrics and Operations Research, Tilburg University, 5037 AB Tilburg, The Netherlands)

  • Nora Szech

    (Department of Economics, Karlsruhe Institute of Technology, D-76131 Karlsruhe, Germany; Berlin Social Science Center, 10785 Berlin, Germany; CESifo, 81679 Munchen, Germany)

Abstract

Information about the future may be instrumentally useful yet scary. For example, many patients shy away from precise genetic tests about their dispositions for severe diseases. They are afraid that a bad test result could render them desperate as a result of anticipatory feelings. We show that partially revealing tests are typically optimal when anticipatory utility interacts with an instrumental need for information. The same result emerges when patients rely on probability weighting. Optimal tests provide only two signals, which renders them easily implementable. While the good signal is typically precise, the bad one remains coarse. This way, patients have a substantial chance to learn that they are free of the genetic risk in question. Yet even if the test outcome is bad, they do not end in a situation without hope.

Suggested Citation

  • Nikolaus Schweizer & Nora Szech, 2018. "Optimal Revelation of Life-Changing Information," Management Science, INFORMS, vol. 64(11), pages 5250-5262, November.
    • Handle: RePEc:inm:ormnsc:v:64:y:2018:i:11:p:5250-5262
    DOI: 10.1287/mnsc.2017.2913
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    Cited by:

    1. Friehe, Tim & Pannenberg, Markus, 2021. "Time preferences and overconfident beliefs: Evidence from germany," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 92(C).
    2. Marta Serra-Garcia & Nora Szech, 2020. "Demand for COVID-19 Antibody Testing and Why It Should Be Free," Working Papers 2020-036, Human Capital and Economic Opportunity Working Group.
    3. Elias Carroni & Giuseppe Pignataro & Luigi Siciliani, 2023. "Persuasion in Physician Agency," Discussion Papers 23/01, Department of Economics, University of York.
    4. Thomas Mariotti & Nikolaus Schweizer & Nora Szech & Jonas von Wangenheim, 2023. "Information Nudges and Self-Control," Management Science, INFORMS, vol. 69(4), pages 2182-2197, April.
    5. Alonso, Ricardo & Câmara, Odilon, 2021. "Organizing Data Analytics," CEPR Discussion Papers 16768, C.E.P.R. Discussion Papers.
    6. Hedlund, Jonas, 2017. "Bayesian persuasion by a privately informed sender," Journal of Economic Theory, Elsevier, vol. 167(C), pages 229-268.
    7. Simeon Schudy & Verena Utikal, 2018. "Does Imperfect Data Privacy Stop People from Collecting Personal Data?," Games, MDPI, vol. 9(1), pages 1-23, March.
    8. Jonas Hedlund & Allan Hernández-Chanto & Carlos Oyarzún, 2021. "Contagion Management through Information Disclosure," Discussion Papers Series 651, School of Economics, University of Queensland, Australia.
    9. Marta Serra-Garcia & Nora Szech, 2023. "Incentives and Defaults Can Increase COVID-19 Vaccine Intentions and Test Demand," Management Science, INFORMS, vol. 69(2), pages 1037-1049, February.
    10. Serra Garcia, Marta & Szech, Nora, 2020. "Understanding demand for COVID-19 antibody testing," Working Paper Series in Economics 140, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    11. Lane, Tom, 2022. "Intrinsic preferences for unhappy news," Journal of Economic Behavior & Organization, Elsevier, vol. 202(C), pages 119-130.
    12. Duraj, Jetlir & He, Kevin, 0. "Dynamic information preference and communication with diminishing sensitivity over news," Theoretical Economics, Econometric Society.
    13. Ozan Candogan & Philipp Strack, 2021. "Optimal Disclosure of Information to a Privately Informed Receiver," Papers 2101.10431, arXiv.org, revised Jan 2022.
    14. Candogan, Ozan & Strack, Philipp, 2023. "Optimal disclosure of information to privately informed agents," Theoretical Economics, Econometric Society, vol. 18(3), July.
    15. Daniele Pennesi, 2020. "Identity and information acquisition," Carlo Alberto Notebooks 610, Collegio Carlo Alberto, revised 2021.
    16. Farzaneh Farhadi & Demosthenis Teneketzis, 2022. "Dynamic Information Design: A Simple Problem on Optimal Sequential Information Disclosure," Dynamic Games and Applications, Springer, vol. 12(2), pages 443-484, June.

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    More about this item

    Keywords

    test design; revelation of information; design of beliefs; medical tests; anticipatory utility; Huntington’s disease;
    All these keywords.

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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