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Optimal Feedback Dynamics Against Free-Riding in Collective Experimentation

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Listed:
  • 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 that the agents are long-lived and forward-looking, their incentive to wait and see other agents' experiences poses a significant obstacle to social learning. We show that the optimal feedback mechanism to mitigate information free-riding takes a strikingly simple form: the principal recommends adoption as long as she observes no bad news, but only with some probability; once she does not recommend at some point, she stays silent forever after that. Our analysis suggests the optimality of premature termination, which in turn implies that: (i) false positives (termination in the good state) are more acceptable than false negatives (continuation in the bad state); (ii) overly cautious mechanisms that are biased toward termination can be welfare-enhancing.

Suggested Citation

  • Chia-Hui Chen & Hulya Eraslan & Junichiro Ishida & Takuro Yamashita, 2024. "Optimal Feedback Dynamics Against Free-Riding in Collective Experimentation," ISER Discussion Paper 1247, Institute of Social and Economic Research, Osaka University.
    • Handle: RePEc:dpr:wpaper:1247
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    File URL: https://www.iser.osaka-u.ac.jp/library/dp/2024/DP1247.pdf
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    References listed on IDEAS

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    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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