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Monopoly Pricing in the Presence of Social Learning

Author

Listed:
  • Davide Crapis

    (Lyft, Inc., San Francisco, California 94107)

  • Bar Ifrach

    (Airbnb, Inc., San Francisco, California 94103)

  • Costis Maglaras

    (Columbia Business School, Columbia University, New York, New York 10027)

  • Marco Scarsini

    (Department of Economics and Finance, Libera Università Internazionale degli Studi Sociali, 00197 Rome, Italy)

Abstract

A monopolist offers a product to a market of consumers with heterogeneous quality preferences. Although initially uninformed about the product quality, they learn by observing past purchase decisions and reviews of other consumers. Our goal is to analyze the social learning mechanism and its effect on the seller’s pricing decision. Consumers follow an intuitive, non-Bayesian decision rule. Under conditions that we identify, we show that consumers eventually learn the product’s quality. We show how the learning trajectory can be approximated in settings with high demand intensity via a mean-field approximation that highlights the dynamics of this learning process, its dependence on the price, and the market heterogeneity with respect to quality preferences. Two pricing policies are studied: a static price and one with a single price change. Finally, numerical experiments suggest that pricing policies that account for social learning may increase revenues considerably relative to policies that do not.

Suggested Citation

  • Davide Crapis & Bar Ifrach & Costis Maglaras & Marco Scarsini, 2017. "Monopoly Pricing in the Presence of Social Learning," Management Science, INFORMS, vol. 63(11), pages 3586-3608, November.
  • Handle: RePEc:inm:ormnsc:v:63:y:2017:i:11:p:3586-3608
    DOI: 10.1287/mnsc.2016.2526
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    11. Nan Yang & Renyu Zhang, 2022. "Dynamic pricing and inventory management in the presence of online reviews," Production and Operations Management, Production and Operations Management Society, vol. 31(8), pages 3180-3197, August.
    12. Salhab, Rabih & Le Ny, Jérôme & Malhamé, Roland P. & Zaccour, Georges, 2022. "Dynamic marketing policies with rating-sensitive consumers: A mean-field games approach," European Journal of Operational Research, Elsevier, vol. 299(3), pages 1079-1093.
    13. Yang, Rui & Tang, Wansheng & Dou, Mengdi & Zhang, Jianxiong, 2021. "Pricing and investing in co-creation with customers for a duopoly," International Journal of Production Economics, Elsevier, vol. 237(C).
    14. Man Yu & Laurens Debo & Roman Kapuscinski, 2016. "Strategic Waiting for Consumer-Generated Quality Information: Dynamic Pricing of New Experience Goods," Management Science, INFORMS, vol. 62(2), pages 410-435, February.
    15. Lu Xiao & Hang Zhang & Yong Qin, 2020. "Competitive Pricing of Innovative Products with Consumers’ Social Learning," Sustainability, MDPI, vol. 12(9), pages 1-13, May.
    16. Bar Ifrach & Costis Maglaras & Marco Scarsini & Anna Zseleva, 2019. "Bayesian Social Learning from Consumer Reviews," Operations Research, INFORMS, vol. 67(5), pages 1209-1221, September.
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    19. Wonho Song & Sangkon Park & Doojin Ryu, 2017. "Information Quality of Online Reviews in the Presence of Potentially Fake Reviews," Korean Economic Review, Korean Economic Association, vol. 33, pages 5-34.
    20. Qian Ma & Biying Shou & Jianwei Huang & Tamer Başar, 2021. "Monopoly Pricing with Participation‐Dependent Social Learning About Quality of Service," Production and Operations Management, Production and Operations Management Society, vol. 30(11), pages 4004-4022, November.
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    More about this item

    Keywords

    social learning; information aggregation; bounded rationality; optimal pricing;
    All these keywords.

    JEL classification:

    • D49 - Microeconomics - - Market Structure, Pricing, and Design - - - Other
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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