Optimal Feedback Dynamics Against Free-Riding in Collective Experimentation

Chia-Hui Chen, Hulya Eraslan, Junichiro Ishida and Takuro Yamashita

ISER Discussion Paper from Institute of Social and Economic Research, The University of Osaka

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.

Date: 2024-07, Revised 2025-09
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Working Paper: Optimal Feedback Dynamics Against Free-Riding in Collective Experimentation (2024)
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