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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
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    Citations

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    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 Aug 2023.

    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

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