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A Mean Field Games Model for Cryptocurrency Mining

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  • Zongxi Li
  • A. Max Reppen
  • Ronnie Sircar

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, 2019. "A Mean Field Games Model for Cryptocurrency Mining," Papers 1912.01952, arXiv.org, revised Jan 2022.
  • Handle: RePEc:arx:papers:1912.01952
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    References listed on IDEAS

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    Cited by:

    1. Charles Bertucci & Louis Bertucci & Jean-Michel Lasry & Pierre-Louis Lions, 2020. "Mean Field Game Approach to Bitcoin Mining," Papers 2004.08167, arXiv.org.
    2. Rene Carmona, 2020. "Applications of Mean Field Games in Financial Engineering and Economic Theory," Papers 2012.05237, arXiv.org.
    3. Hansjörg Albrecher & Dina Finger & Pierre-Olivier Goffard, 2022. "Blockchain mining in pools: Analyzing the trade-off between profitability and ruin," Working Papers hal-03336851, HAL.

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