IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v70y2024i4p2188-2208.html
   My bibliography  Save this article

A Mean Field Games Model for Cryptocurrency Mining

Author

Listed:
  • Zongxi Li

    (Operations Research & Financial Engineering Department, Princeton University, Princeton, New Jersey 08540)

  • A. Max Reppen

    (Questrom School of Business, Boston University, Boston, Massachusetts 02215; Rafik B. Hariri Institute for Computing and Computational Science & Engineering, Boston, Massachusetts 02215)

  • Ronnie Sircar

    (Operations Research & Financial Engineering Department, Princeton University, Princeton, New Jersey 08540)

Abstract

We propose a mean field game model to study the question of how centralization of reward and computational power occur in Bitcoin-like cryptocurrencies. Miners compete against each other for mining rewards by increasing their computational power. This leads to a novel mean field game of jump intensity control, which we solve explicitly for miners maximizing exponential utility and handle numerically in the case of miners with power utilities. We show that the heterogeneity of their initial wealth distribution leads to greater imbalance of the reward distribution, and increased wealth heterogeneity over time, or a “rich get richer” effect. This concentration phenomenon is aggravated by a higher Bitcoin mining reward and reduced by competition. Additionally, an advantaged miner with cost advantages such as access to cheaper electricity, contributes a significant amount of computational power in equilibrium, unaffected by competition from less efficient miners. Hence, cost efficiency can also result in the type of centralization seen among miners of cryptocurrencies.

Suggested Citation

  • Zongxi Li & A. Max Reppen & Ronnie Sircar, 2024. "A Mean Field Games Model for Cryptocurrency Mining," Management Science, INFORMS, vol. 70(4), pages 2188-2208, April.
  • Handle: RePEc:inm:ormnsc:v:70:y:2024:i:4:p:2188-2208
    DOI: 10.1287/mnsc.2023.4798
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.2023.4798
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mnsc.2023.4798?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:70:y:2024:i:4:p:2188-2208. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.