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Optimal Feedback Dynamics Against Free-Riding in Collective Experimentation

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  • Chia-Hui Chen
  • Hulya Eraslan
  • Junichiro Ishida
  • Takuro Yamashita

Abstract

We consider a dynamic model in which a principal decides what information to release about a product of unknown quality (e.g., a vaccine) to incentivize agents to experiment with the product. Assuming forward-looking agents, their incentive to wait and see others’ experiences poses a significant obstacle to social learning, implying suboptimality of full transparency. We show that the optimal feedback mechanism to mitigate information free-riding takes a strikingly simple form: the principal makes a binary recommendation and recommends against adoption with some probability even when she is relatively optimistic; once she recommends against adoption, she never reverses her stance.

Suggested Citation

  • Chia-Hui Chen & Hulya Eraslan & Junichiro Ishida & Takuro Yamashita, 2024. "Optimal Feedback Dynamics Against Free-Riding in Collective Experimentation," ISER Discussion Paper 1247r, Institute of Social and Economic Research, The University of Osaka, revised Sep 2025.
    • Handle: RePEc:dpr:wpaper:1247r
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    References listed on IDEAS

    as
    1. Ian Ball, 2023. "Dynamic Information Provision: Rewarding the Past and Guiding the Future," Econometrica, Econometric Society, vol. 91(4), pages 1363-1391, July.
    2. Ian Ball, 2023. "Dynamic Information Provision: Rewarding the Past and Guiding the Future," Papers 2303.09675, arXiv.org.
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