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 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.

Date: 2024-07
New Economics Papers: this item is included in nep-des and nep-mic
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.iser.osaka-u.ac.jp/static/resources/docs/dp/2024/DP1247.pdf

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:dpr:wpaper:1247

Access Statistics for this paper

More papers in ISER Discussion Paper from Institute of Social and Economic Research, The University of Osaka Contact information at EDIRC.
Bibliographic data for series maintained by Librarian ().