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Oceanic Games: Centralization Risks and Incentives in Blockchain Mining

In: Mathematical Research for Blockchain Economy

Author

Listed:
  • Nikos Leonardos

    (National and Kapodistrian University of Athens)

  • Stefanos Leonardos

    (Singapore University of Technology and Design)

  • Georgios Piliouras

    (Singapore University of Technology and Design)

Abstract

To participate in the distributed consensus of permissionless blockchains, prospective nodes—or miners—provide proof of designated, costly resources. However, in contrast to the intended decentralization, current data on blockchain mining unveils increased concentration of these resources in a few major entities, typically mining pools. To study strategic considerations in this setting, we employ the concept of Oceanic Games [27]. Oceanic Games have been used to analyze decision making in corporate settings with small numbers of dominant players (shareholders) and large numbers of individually insignificant players, the ocean. Unlike standard equilibrium models, they focus on measuring the value (or power) per entity and per unit of resource in a given distribution of resources. These values are viewed as strategic components in coalition formations, mergers and resource acquisitions. Considering such issues relevant to blockchain governance and long-term sustainability, we adapt oceanic games to blockchain mining and illustrate the defined concepts via examples. The application of existing results reveals incentives for individual miners to merge in order to increase the value of their resources. This offers an alternative perspective to the observed centralization and concentration of mining power. Beyond numerical simulations, we use the model to identify issues relevant to the design of future cryptocurrencies and formulate prospective research questions.

Suggested Citation

  • Nikos Leonardos & Stefanos Leonardos & Georgios Piliouras, 2020. "Oceanic Games: Centralization Risks and Incentives in Blockchain Mining," Springer Proceedings in Business and Economics, in: Panos Pardalos & Ilias Kotsireas & Yike Guo & William Knottenbelt (ed.), Mathematical Research for Blockchain Economy, pages 183-199, Springer.
    • Handle: RePEc:spr:prbchp:978-3-030-37110-4_13
    DOI: 10.1007/978-3-030-37110-4_13
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    Citations

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    Cited by:

    1. Matringe, Nadia & Power, Michael, 2024. "Memories lost: A history of accounting records as forms of projection," Accounting, Organizations and Society, Elsevier, vol. 112(C).
    2. Koki, Constandina & Leonardos, Stefanos & Piliouras, Georgios, 2022. "Exploring the predictability of cryptocurrencies via Bayesian hidden Markov models," Research in International Business and Finance, Elsevier, vol. 59(C).
    3. Yun Kuen Cheung & Stefanos Leonardos & Georgios Piliouras & Shyam Sridhar, 2021. "From Griefing to Stability in Blockchain Mining Economies," Papers 2106.12332, arXiv.org.

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