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Information Sale on Network

Author

Listed:
  • Jihwan Do
  • Lining Han
  • Xiaoxi Li

Abstract

This paper studies a stylized model of a monopoly data seller when information-sharing network exists among data buyers. We show that, if the buyers' prior information is sufficiently noisy, the optimal selling strategy is characterized by a maximum independent set, which is the largest set of buyers who do not have information-sharing link at all. In addition, the precision of the seller's data decreases in the number of information-sharing links among buyers, but it is higher than the socially efficient level of precision.

Suggested Citation

  • Jihwan Do & Lining Han & Xiaoxi Li, 2024. "Information Sale on Network," Papers 2404.05546, arXiv.org.
  • Handle: RePEc:arx:papers:2404.05546
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    File URL: http://arxiv.org/pdf/2404.05546
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    References listed on IDEAS

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    1. Cheng, Chen & Xing, Yiqing, 2022. "Which networks permit stable allocations? A theory of network-based comparisons," Theoretical Economics, Econometric Society, vol. 17(4), November.
    2. Lode Li, 2002. "Information Sharing in a Supply Chain with Horizontal Competition," Management Science, INFORMS, vol. 48(9), pages 1196-1212, September.
    3. Ozsoylev, Han N. & Walden, Johan, 2011. "Asset pricing in large information networks," Journal of Economic Theory, Elsevier, vol. 146(6), pages 2252-2280.
    4. Leister, C. Matthew, 2020. "Information acquisition and welfare in network games," Games and Economic Behavior, Elsevier, vol. 122(C), pages 453-475.
    5. Page, Lionel & Siemroth, Christoph, 2017. "An experimental analysis of information acquisition in prediction markets," Games and Economic Behavior, Elsevier, vol. 101(C), pages 354-378.
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