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Adversarial blockchain queues and trading on a CFMM

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  • Andrew W. Macpherson

Abstract

We describe a plausible probabilistic model for a blockchain queueing environment in which rational, profit-maximising schedulers impose adversarial disciplines on incoming messages containing a payload that encodes a state transition in a machine. The model can be specialised to apply to chains with fixed or variable block times, traditional priority queue disciplines with `honest' schedulers, or adversarial public mempools. We find conditions under which the model behaves as a bulk-service queue with priority discipline and derive practical expressions for the relative block and message number of a transaction. We study this setup in the context of orders to a CFMM DEX where the execution price a user receives may be quite sensitive to its positioning in the chain -- in particular, to a string of transactions scheduled for prior execution which is not knowable at the time of order creation. We derive statistical models for the price impact of this order flow both in the presence and absence of MEV extraction activity.

Suggested Citation

  • Andrew W. Macpherson, 2023. "Adversarial blockchain queues and trading on a CFMM," Papers 2302.01663, arXiv.org, revised Feb 2023.
    • Handle: RePEc:arx:papers:2302.01663
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    References listed on IDEAS

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    1. Jiahua Xu & Krzysztof Paruch & Simon Cousaert & Yebo Feng, 2021. "SoK: Decentralized Exchanges (DEX) with Automated Market Maker (AMM) Protocols," Papers 2103.12732, arXiv.org, revised Mar 2023.
    2. Matheus V. X. Ferreira & David C. Parkes, 2022. "Credible Decentralized Exchange Design via Verifiable Sequencing Rules," Papers 2209.15569, arXiv.org, revised Apr 2023.
    3. A. Chakraborti & I. Muni-Toke & M. Patriarca & F. Abergel, 2011. "Econophysics Review : II. Agent-based models," Post-Print hal-03332946, HAL.
    4. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-137, February.
    5. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frédéric Abergel, 2011. "Econophysics review: II. Agent-based models," Post-Print hal-00621059, HAL.
    6. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frederic Abergel, 2011. "Econophysics review: II. Agent-based models," Quantitative Finance, Taylor & Francis Journals, vol. 11(7), pages 1013-1041.
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    Cited by:

    1. Andrew W. Macpherson, 2024. "Do backrun auctions protect traders?," Papers 2401.08302, arXiv.org.

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