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Bitcoin: A Natural Oligopoly

Author

Listed:
  • Nick Arnosti

    (Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, Minnesota 55455)

  • S. Matthew Weinberg

    (Department of Computer Science, Princeton University, Princeton, New Jersey 08540)

Abstract

We argue that the concentrated production and ownership of Bitcoin mining hardware arise naturally from the economic incentives of Bitcoin mining. We model Bitcoin mining as a two-stage competition; miners compete in prices to sell hardware while competing in quantities for mining rewards. We characterize equilibria in our model and show that small asymmetries in operational costs result in highly concentrated ownership of mining equipment. We further show that production of mining equipment will be dominated by the miner with the most efficient hardware, who will sell hardware to competitors while possibly also using it to mine.

Suggested Citation

  • Nick Arnosti & S. Matthew Weinberg, 2022. "Bitcoin: A Natural Oligopoly," Management Science, INFORMS, vol. 68(7), pages 4755-4771, July.
    • Handle: RePEc:inm:ormnsc:v:68:y:2022:i:7:p:4755-4771
    DOI: 10.1287/mnsc.2021.4095
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    References listed on IDEAS

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    Cited by:

    1. Gersbach, Hans & Mamageishvili, Akaki & Pitsuwan, Fikri, 2023. "Crowdsearch," CEPR Discussion Papers 18529, C.E.P.R. Discussion Papers.
      • Hans Gersbach & Akaki Mamageishvili & Fikri Pitsuwan, 2023. "Crowdsearch," Papers 2311.08532, arXiv.org.
    2. Kong, Xiaolin & Ma, Chaoqun & Ren, Yi-Shuai & Baltas, Konstantinos & Narayan, Seema, 2024. "A comparative analysis of the price explosiveness in Bitcoin and forked coins," Finance Research Letters, Elsevier, vol. 61(C).
    3. Li, Xiao & Wu, Ruoxi & Wang, Chen, 2024. "Impacts of bitcoin on monetary system: Is China's bitcoin ban necessary?," Research in International Business and Finance, Elsevier, vol. 69(C).
    4. Li, Zhicheng & Li, Jialong & Zhou, Ke, 2023. "Bitcoin transaction fees and the decentralization of Bitcoin mining pools," Finance Research Letters, Elsevier, vol. 58(PB).
    5. Siu Hin Tang & Mathieu Rosenbaum & Chao Zhou, 2023. "Forecasting Volatility with Machine Learning and Rough Volatility: Example from the Crypto-Winter," Papers 2311.04727, arXiv.org, revised Feb 2024.

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