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Bitcoin: a new proof-of-work system with reduced variance

Author

Listed:
  • Danilo Bazzanella

    (Politecnico di Torino)

  • Andrea Gangemi

    (Politecnico di Torino)

Abstract

Since its inception, bitcoin has used the popular consensus protocol proof-of-work (PoW). PoW has a well-known flaw: it distributes all rewards to a single miner (or pool) who inserts a new block. Consequently, the variance of rewards and the mining enterprise risk are extremely high. In 2016, Shi proposed addressing this problem with a theoretical algorithm. We introduce an easily-implemented PoW variant that improves Shi’s idea. The network must not find a single nonce but a few to insert a block. This simple change allows for a fairer distribution of rewards and also has the effect of regularizing the insertion time of blocks. This method would facilitate the emergence of small pools or autonomous miners.

Suggested Citation

  • Danilo Bazzanella & Andrea Gangemi, 2023. "Bitcoin: a new proof-of-work system with reduced variance," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-14, December.
  • Handle: RePEc:spr:fininn:v:9:y:2023:i:1:d:10.1186_s40854-023-00505-2
    DOI: 10.1186/s40854-023-00505-2
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    References listed on IDEAS

    as
    1. Ning Shi, 2016. "A new proof-of-work mechanism for bitcoin," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-8, December.
    2. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & David Martinez-Rego & Fan Wu & Lingbo Li, 2022. "Cryptocurrency trading: a comprehensive survey," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-59, December.
    3. Min Xu & Xingtong Chen & Gang Kou, 2019. "A systematic review of blockchain," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-14, December.
    4. Helder Sebastião & Pedro Godinho, 2021. "Forecasting and trading cryptocurrencies with machine learning under changing market conditions," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-30, December.
    5. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & Lingbo Li & David Martinez-Regoband & Fan Wu, 2020. "Cryptocurrency Trading: A Comprehensive Survey," Papers 2003.11352, arXiv.org, revised Jan 2022.
    Full references (including those not matched with items on IDEAS)

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