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Searching for Results: Optimal Platform Design in a Network Setting

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  • Charlson, G.

Abstract

Online platforms that link buyers and sellers are able to shape the pattern of observation between market participants. We model buyer-seller interactions as a series of bipartite graphs, which are each realised with a probability chosen by the platform owner. Prominent sellers disproportionately increase competition, which decreases prices. To maximise profit, the platform owner ensures that the size of the neighbourhood of each buyer is the same in each state of the world and randomises buyer observation across all sellers on the platform, a result that still holds even when buyers are heterogeneous. When products are vertically differentiated, the platform owner faces a trade-off between biasing observation towards high-quality products and increasing competition. The extent to which platforms highlight high-quality products and the number of sellers depends on the characteristics of the market(s) in which they operate.

Suggested Citation

  • Charlson, G., 2020. "Searching for Results: Optimal Platform Design in a Network Setting," Cambridge Working Papers in Economics 20118, Faculty of Economics, University of Cambridge.
    • Handle: RePEc:cam:camdae:20118
    Note: gc556
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    References listed on IDEAS

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    Cited by:

    1. Charlson, G., 2021. "Third-Degree Price Discrimination in the Age of Big Data," Janeway Institute Working Papers 2104, Faculty of Economics, University of Cambridge.
    2. Charlson, G., 2021. "Third-Degree Price Discrimination in the Age of Big Data," Cambridge Working Papers in Economics 2159, Faculty of Economics, University of Cambridge.

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    JEL classification:

    • D20 - Microeconomics - - Production and Organizations - - - General
    • L20 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - General

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