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Should we do more when we know less? The effect of technology risk on optimal effort

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  • Lu Li
  • Richard Peter

Abstract

Technology risk arises when the effectiveness of self‐insurance and self‐protection activities depends on exogenous factors or is only imperfectly known. It affects optimal behavior via a preference channel and a technology channel. In this paper, we identify conditions for unambiguous comparative statics with respect to the presence of, as well as various stochastic changes in technology risk. These conditions involve prudence, relative risk aversion, relative prudence, and several new measures of mitigation efficacy. We highlight cases where technology risk raises the optimal level of effort, consistent with the precautionary principle. We discuss several applications of our results.

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  • Lu Li & Richard Peter, 2021. "Should we do more when we know less? The effect of technology risk on optimal effort," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(3), pages 695-725, September.
    • Handle: RePEc:bla:jrinsu:v:88:y:2021:i:3:p:695-725
    DOI: 10.1111/jori.12339
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    3. Liudong Chen & Bolun Xu, 2024. "Prudent Price-Responsive Demands," Papers 2405.16356, arXiv.org.
    4. Heinzel Christoph & Richard Peter, 2021. "Precautionary motives with multiple instruments," Working Papers SMART 21-09, INRAE UMR SMART.
    5. Heinzel, Christoph & Peter, Richard, 2021. "Precautionary motives with multiple instruments," Working Papers 316521, Institut National de la recherche Agronomique (INRA), Departement Sciences Sociales, Agriculture et Alimentation, Espace et Environnement (SAE2).
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    7. Anand, Vaibhav, 2022. "The Value of Forecast Improvements: Evidence from Advisory Lead Times and Vehicle Crashes," MPRA Paper 114491, University Library of Munich, Germany.
    8. Claudio Bonilla & Marcos Vergara, 2022. "New results on precautionary saving and nonlinear risks," Journal of Economics, Springer, vol. 136(2), pages 177-189, July.
    9. Pablo Gutiérrez Cubillos & Roberto Pastén, 2023. "Nonlinear risks: a unified framework," Theory and Decision, Springer, vol. 95(1), pages 11-32, July.
    10. Martin Eling & Irina Gemmo & Danjela Guxha & Hato Schmeiser, 2024. "Big data, risk classification, and privacy in insurance markets," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 49(1), pages 75-126, March.
    11. Johannes G. Jaspersen & Richard Peter & Marc A. Ragin, 2023. "Probability weighting and insurance demand in a unified framework," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 48(1), pages 63-109, March.
    12. Anand, Vaibhav, 2001. "The Value of Forecast Improvements: Evidence from Advisory Lead Times and Vehicle Crashes," SocArXiv hdpga, Center for Open Science.
    13. Richard Peter, 2024. "The economics of self-protection," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 49(1), pages 6-35, March.
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