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Diffusion Models for Peer-to-Peer (P2P) Media Distribution: On the Impact of Decentralized, Constrained Supply

Author

Listed:
  • Kartik Hosanagar

    (Operations and Information Management, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Peng Han

    (aQuantive, Inc. (Microsoft), Seattle, Washington 98104)

  • Yong Tan

    (Michael G. Foster School of Business, University of Washington, Seattle, Washington 98195)

Abstract

In peer-to-peer (P2P) media distribution, users obtain content from other users who already have it. This form of decentralized product distribution demonstrates several unique features. Only a small fraction of users in the network are queried when a potential adopter seeks a file, and many of these users might even free-ride, i.e., not distribute the content to others. As a result, generated demand might not always be fulfilled immediately. We present mixing models for product diffusion in P2P networks that capture decentralized product distribution by current adopters, incomplete demand fulfillment and other unique aspects of P2P product diffusion. The models serve to demonstrate the important role that P2P search process and distribution referrals---payments made to users that distribute files---play in efficient P2P media distribution. We demonstrate the ability of our diffusion models to derive normative insights for P2P media distributors by studying the effectiveness of distribution referrals in speeding product diffusion and determining optimal referral policies for fully decentralized and hierarchical P2P networks.

Suggested Citation

  • Kartik Hosanagar & Peng Han & Yong Tan, 2010. "Diffusion Models for Peer-to-Peer (P2P) Media Distribution: On the Impact of Decentralized, Constrained Supply," Information Systems Research, INFORMS, vol. 21(2), pages 271-287, June.
    • Handle: RePEc:inm:orisre:v:21:y:2010:i:2:p:271-287
    DOI: 10.1287/isre.1080.0221
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    References listed on IDEAS

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    6. Levent V. Orman, 2016. "Information markets over trust networks," Electronic Commerce Research, Springer, vol. 16(4), pages 529-551, December.

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