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Experimentation and Approval Mechanisms

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  • Andrew McClellan

Abstract

We study the design of approval rules when costly experimentation must be delegated to an agent with misaligned preferences. When the agent has the option to end experimentation, we show that in contrast to standard stopping problems, the optimal approval rule must be history‐dependent. We characterize the optimal rule and show the approval threshold moves downward over the course of experimentation. We find that private information may qualitatively change the optimal mechanism: an agent can choose a fast‐track option in the form of an initially depressed approval threshold. On expiry of the fast track, the threshold jumps up, introducing more stringent standards. Our results provide a theoretical foundation for both the loosening of approval standards on longer experimentation paths and fast‐track mechanisms.

Suggested Citation

  • Andrew McClellan, 2022. "Experimentation and Approval Mechanisms," Econometrica, Econometric Society, vol. 90(5), pages 2215-2247, September.
    • Handle: RePEc:wly:emetrp:v:90:y:2022:i:5:p:2215-2247
    DOI: 10.3982/ECTA17021
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    References listed on IDEAS

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    1. Emeric Henry & Gianmarco Ottaviano, 2019. "Research and the Approval Process: the Organization of Persuasion," Sciences Po publications info:hdl:2441/1gr6n3t28b9, Sciences Po.
    2. Emeric Henry & Marco Ottaviani, 2019. "Research and the Approval Process: The Organization of Persuasion," American Economic Review, American Economic Association, vol. 109(3), pages 911-955, March.
    3. Dirk Bergemann & Ulrigh Hege, 2005. "The Financing of Innovation: Learning and Stopping," RAND Journal of Economics, The RAND Corporation, vol. 36(4), pages 719-752, Winter.
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    5. Marina Halac & Navin Kartik & Qingmin Liu, 2016. "Optimal Contracts for Experimentation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 83(3), pages 1040-1091.
    6. Thomas Balzer & Klaus Janßen, 2002. "A duality approach to problems of combined stopping and deciding under constraints," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 55(3), pages 431-446, June.
    7. Thomas Balzer & Klaus Janßen, 2002. "A duality approach to problems of combined stopping and deciding under constraints," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 55(3), pages 431-446, June.
    8. Kruse, Thomas & Strack, Philipp, 2015. "Optimal stopping with private information," Journal of Economic Theory, Elsevier, vol. 159(PB), pages 702-727.
    9. Emeric Henry & Marco Ottaviani, 2019. "Research and the Approval Process: The Organization of Persuasion," American Economic Review, American Economic Association, vol. 109(3), pages 911-955, March.
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    Cited by:

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    2. Mustapha Nyenye Issah, 2024. "Feedback strategies in the market with uncertainties," Papers 2410.16203, arXiv.org.
    3. McClellan, Andrew, 2025. "The dynamics of project standards," Journal of Economic Theory, Elsevier, vol. 224(C).
    4. Itai Arieli & Yakov Babichenko & Dima Shaiderman & Xianwen Shi, 2025. "Persuading while Learning," Working Papers tecipa-791, University of Toronto, Department of Economics.
    5. Yingkai Li & Jonathan Libgober, 2023. "Incentivizing Forecasters to Learn: Summarized vs. Unrestricted Advice," Papers 2310.19147, arXiv.org, revised Dec 2025.
    6. Ben-Porath, Elchanan & Dekel, Eddie & Lipman, Barton L., 2026. "Mechanism design for acquisition of/stochastic evidence," Theoretical Economics, Econometric Society, vol. 21(1), January.
    7. Silvia Martinez-Gorricho & Carlos Oyarzun, 2024. "Testing under information manipulation," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 77(3), pages 849-890, May.
    8. Sebastian Gryglewicz & Aaron Kolb, 2025. "Strategic Pricing in Volatile Markets," Operations Research, INFORMS, vol. 73(1), pages 444-460, January.

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