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The blockchain folk theorem

Author

Listed:
  • Bruno Biais

    (TSM - Toulouse School of Management Research - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - CNRS - Centre National de la Recherche Scientifique - TSM - Toulouse School of Management - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse)

  • Christophe Bisière

    (TSM - Toulouse School of Management Research - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - CNRS - Centre National de la Recherche Scientifique - TSM - Toulouse School of Management - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse)

  • Matthieu, Bouvard

    (TSM - Toulouse School of Management Research - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - CNRS - Centre National de la Recherche Scientifique - TSM - Toulouse School of Management - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse)

  • Catherine Casamatta

    (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique, TSM - Toulouse School of Management Research - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - CNRS - Centre National de la Recherche Scientifique - TSM - Toulouse School of Management - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse)

Abstract

Blockchains are distributed ledgers, operated within peer-to-peer networks. We model the proof-of-work blockchain protocol as a stochastic game and analyze the equilibrium strategies of rational, strategic miners. Mining the longest chain is a Markov perfect equilibrium, without forking, in line with Nakamoto (2008). The blockchain protocol, however, is a coordination game, with multiple equilibria. There exist equilibria with forks, leading to orphaned blocks and persistent divergence between chains. We also show how forks can be generated by information delays and software upgrades. Last we identify negative externalities implying that equilibrium investment in computing capacity is excessive

Suggested Citation

  • Bruno Biais & Christophe Bisière & Matthieu, Bouvard & Catherine Casamatta, 2019. "The blockchain folk theorem," Post-Print hal-02281914, HAL.
    • Handle: RePEc:hal:journl:hal-02281914
    DOI: 10.1093/rfs/hhy095
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    References listed on IDEAS

    as
    1. Jonathan Chiu & Thorsten V Koeppl, 2019. "Blockchain-Based Settlement for Asset Trading," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1716-1753.
    2. Mariana Khapko & Marius Andrei Zoican, 2017. ""Smart" Settlement," Post-Print hal-01491563, HAL.
    3. Josh Lerner & Jean Tirole, 2002. "Some Simple Economics of Open Source," Journal of Industrial Economics, Wiley Blackwell, vol. 50(2), pages 197-234, June.
    4. John Duggan, 2012. "Noisy Stochastic Games," Econometrica, Econometric Society, vol. 80(5), pages 2017-2045, September.
    5. Biais, Bruno & Foucault, Thierry & Moinas, Sophie, 2015. "Equilibrium fast trading," Journal of Financial Economics, Elsevier, vol. 116(2), pages 292-313.
    6. Cass, David & Shell, Karl, 1983. "Do Sunspots Matter?," Journal of Political Economy, University of Chicago Press, vol. 91(2), pages 193-227, April.
    7. Harold L. Cole & Timothy J. Kehoe, 2000. "Self-Fulfilling Debt Crises," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 67(1), pages 91-116.
    8. Tirole, Jean, 1982. "On the Possibility of Speculation under Rational Expectations," Econometrica, Econometric Society, vol. 50(5), pages 1163-1181, September.
    9. Lin William Cong & Zhiguo He, 2019. "Blockchain Disruption and Smart Contracts," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1754-1797.
    10. David Yermack, 2017. "Corporate Governance and Blockchains," Review of Finance, European Finance Association, vol. 21(1), pages 7-31.
    11. Vincent Glode & Richard C. Green & Richard Lowery, 2012. "Financial Expertise as an Arms Race," Journal of Finance, American Finance Association, vol. 67(5), pages 1723-1759, October.
    12. Glode, V. & Green, R.C. & Lowery, R., 2010. "Financial Expertise as an Arms Race," Other publications TiSEM 6316f2ea-660e-4b55-82fc-1, Tilburg University, School of Economics and Management.
    13. Glode, V. & Green, R.C. & Lowery, R., 2010. "Financial Expertise as an Arms Race," Other publications TiSEM 7f1ffc85-7ad2-4d99-82a2-b, Tilburg University, School of Economics and Management.
    14. Crawford, Vincent, 1998. "A Survey of Experiments on Communication via Cheap Talk," Journal of Economic Theory, Elsevier, vol. 78(2), pages 286-298, February.
    15. John Duggan, 2012. "Noisy Stochastic Games," RCER Working Papers 570, University of Rochester - Center for Economic Research (RCER).
    16. Christian Catalini & Joshua S. Gans, 2016. "Some Simple Economics of the Blockchain," NBER Working Papers 22952, National Bureau of Economic Research, Inc.
    17. Tirole, Jean, 1985. "Asset Bubbles and Overlapping Generations," Econometrica, Econometric Society, vol. 53(6), pages 1499-1528, November.
    18. Max Raskin & David Yermack, 2016. "Digital Currencies, Decentralized Ledgers, and the Future of Central Banking," NBER Working Papers 22238, National Bureau of Economic Research, Inc.
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    More about this item

    Keywords

    Blockchains ; Peer-to-peer architecture (Computer networks) ; Markov processes ; Nash equilibrium ; Cryptocurrency mining *Stochastic analysis Software upgrades Computable general equilibrium models;
    All these keywords.

    JEL classification:

    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • G2 - Financial Economics - - Financial Institutions and Services
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software

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