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Optimal Design for Social Learning

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  • Yeon Koo Che
  • Johannes Horner

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.
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Suggested Citation

  • Yeon Koo Che & Johannes Horner, 2015. "Optimal Design for Social Learning," Levine's Bibliography 786969000000001075, UCLA Department of Economics.
    • Handle: RePEc:cla:levrem:786969000000001075
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    References listed on IDEAS

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    1. Simon, Leo K & Stinchcombe, Maxwell B, 1989. "Extensive Form Games in Continuous Time: Pure Strategies," Econometrica, Econometric Society, vol. 57(5), pages 1171-1214, September.
    2. Lawrence M. Ausubel, 2004. "An Efficient Ascending-Bid Auction for Multiple Objects," American Economic Review, American Economic Association, vol. 94(5), pages 1452-1475, December.
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    Cited by:

    1. Dirk Bergemann & Alessandro Pavan, 2015. "Introduction to JET Symposium Issue on "Dynamic Contracts and Mechanism Design"," Cowles Foundation Discussion Papers 2016, Cowles Foundation for Research in Economics, Yale University.
    2. Daron Acemoglu & Ali Makhdoumi & Azarakhsh Malekian & Asuman Ozdaglar, 2017. "Fast and Slow Learning From Reviews," NBER Working Papers 24046, National Bureau of Economic Research, Inc.
    3. Anastasios Dosis & Abhinay Muthoo, 2019. "Experimentation in Dynamic R&D Competition," Working Papers hal-02102518, HAL.
    4. Renault, Jérôme & Solan, Eilon & Vieille, Nicolas, 2017. "Optimal dynamic information provision," Games and Economic Behavior, Elsevier, vol. 104(C), pages 329-349.
    5. Bergemann, Dirk & Pavan, Alessandro, 2015. "Introduction to Symposium on Dynamic Contracts and Mechanism Design," Journal of Economic Theory, Elsevier, vol. 159(PB), pages 679-701.
    6. Nikhil Garg & Ramesh Johari, 2021. "Designing Informative Rating Systems: Evidence from an Online Labor Market," Manufacturing & Service Operations Management, INFORMS, vol. 23(3), pages 589-605, May.

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    More about this item

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • M52 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics - - - Compensation and Compensation Methods and Their Effects

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