Optimal Design for Social Learning

Yeon-Koo Che and Johannes Hörner

No 2000, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

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 (search for similar items in EconPapers)
JEL-codes: D82 D83 M52 (search for similar items in EconPapers)
Pages: 80 pages
Date: 2015-04
New Economics Papers: this item is included in nep-mic
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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