Optimal Design for Social Learning

81 Pages Posted: 30 Apr 2015

See all articles by Yeon-Koo Che

Yeon-Koo Che

Columbia University

Johannes Horner

Yale University - Cowles Foundation

Date Written: April 30, 2015

Abstract

This paper studies the design of a recommender system for organizing social learning on a product. To improve incentives for early experimentation, the optimal design trades off fully transparent social learning by over-recommending a product (or “spamming”) to a fraction of agents in the early phase of the product cycle. Under the optimal scheme, the designer spams very little about a product right after its release but gradually increases the frequency of spamming and stops it altogether when the product is deemed sufficiently unworthy of recommendation. The optimal recommender system involves randomly triggered spamming when recommendations are public - as is often the case for product ratings - and an information “blackout” followed by a burst of spamming when agents can choose when to check in for a recommendation. Fully transparent recommendations may become optimal if a (socially-benevolent) designer does not observe the agents’ costs of experimentation.

Keywords: Experimentation, Social learning, Mechanism design

JEL Classification: D82, D83, M52

Suggested Citation

Che, Yeon-Koo and Horner, Johannes, Optimal Design for Social Learning (April 30, 2015). Cowles Foundation Discussion Paper No. 2000, Available at SSRN: https://ssrn.com/abstract=2600931 or http://dx.doi.org/10.2139/ssrn.2600931

Yeon-Koo Che

Columbia University ( email )

420 W. 118th Street, 1016IAB
New York, NY 10027
United States
212-854-8276 (Phone)

HOME PAGE: http://www.columbia.edu/~yc2271

Johannes Horner (Contact Author)

Yale University - Cowles Foundation ( email )

Box 208281
New Haven, CT 06520-8281
United States

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