IDEAS home Printed from https://ideas.repec.org/a/aea/aejmic/v11y2019i1p44-78.html
   My bibliography  Save this article

The Risk of Failure: Trial and Error Learning and Long-Run Performance

Author

Listed:
  • Steven Callander
  • Niko Matouschek

Abstract

Innovation is often the key to sustained progress, yet innovation itself is difficult and highly risky. Success is not guaranteed as breakthroughs are mixed with setbacks and the path of learning is typically far from smooth. How decision makers learn by trial and error and the efficacy of the process are inextricably linked to the incentives of the decision makers themselves and, in particular, to their tolerance for risk. In this paper, we develop a model of trial and error learning with risk averse agents who learn by observing the choices of earlier agents and the outcomes that are realized. We identify sufficient conditions for the existence of optimal actions. We show that behavior within each period varies in risk and performance and that a performance trap develops, such that low performing agents opt to not experiment and thus fail to gain the knowledge necessary to improve performance. We also show that the impact of risk reverberates across periods, leading, on average, to divergence in long-run performance across agents.

Suggested Citation

  • Steven Callander & Niko Matouschek, 2019. "The Risk of Failure: Trial and Error Learning and Long-Run Performance," American Economic Journal: Microeconomics, American Economic Association, vol. 11(1), pages 44-78, February.
    • Handle: RePEc:aea:aejmic:v:11:y:2019:i:1:p:44-78
    Note: DOI: 10.1257/mic.20160359
    as

    Download full text from publisher

    File URL: https://www.aeaweb.org/doi/10.1257/mic.20160359
    Download Restriction: no
    File URL: https://www.aeaweb.org/articles/attachments?retrieve=7YnBL7rs3WBv_0XscM3OfgP-jF9pgjIt
    Download Restriction: no
    File URL: https://www.aeaweb.org/articles/attachments?retrieve=Mh_fgsuZ6lu3VX1aofit1LspJGUU15Pl
    Download Restriction: Access to full text is restricted to AEA members and institutional subscribers.
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Can Urgun & Leeat Yariv, 2021. "Retrospective Search: Exploration and Ambition on Uncharted Terrain," Working Papers 2021-33, Princeton University. Economics Department..
    2. Tang, Xuli & Li, Xin & Ding, Ying & Song, Min & Bu, Yi, 2020. "The pace of artificial intelligence innovations: Speed, talent, and trial-and-error," Journal of Informetrics, Elsevier, vol. 14(4).
    3. Natkamon Tovanich & Nicolas Soulié & Nicolas Heulot & Petra Isenberg, 2022. "The evolution of mining pools and miners’ behaviors in the Bitcoin blockchain," Post-Print hal-03610424, HAL.
    4. Steven Callander & Niko Matouschek, 2022. "The Novelty of Innovation: Competition, Disruption, and Antitrust Policy," Management Science, INFORMS, vol. 68(1), pages 37-51, January.
    5. Yariv, Leeat & Urgun, Can, 2020. "Retrospective Search: Exploration and Ambition on Uncharted Terrain," CEPR Discussion Papers 15534, C.E.P.R. Discussion Papers.
    6. Christoph Carnehl & Johannes Schneider, 2021. "A Quest for Knowledge," Papers 2102.13434, arXiv.org, revised Jul 2024.

    More about this item

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aea:aejmic:v:11:y:2019:i:1:p:44-78. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Michael P. Albert (email available below). General contact details of provider: https://edirc.repec.org/data/aeaaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.