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Auto.gov: Learning-based Governance for Decentralized Finance (DeFi)

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  • Jiahua Xu
  • Yebo Feng
  • Daniel Perez
  • Benjamin Livshits

Abstract

Decentralized finance (DeFi) is an integral component of the blockchain ecosystem, enabling a range of financial activities through smart-contract-based protocols. Traditional DeFi governance typically involves manual parameter adjustments by protocol teams or token holder votes, and is thus prone to human bias and financial risks, undermining the system's integrity and security. While existing efforts aim to establish more adaptive parameter adjustment schemes, there remains a need for a governance model that is both more efficient and resilient to significant market manipulations. In this paper, we introduce "Auto$.$gov", a learning-based governance framework that employs a deep Qnetwork (DQN) reinforcement learning (RL) strategy to perform semi-automated, data-driven parameter adjustments. We create a DeFi environment with an encoded action-state space akin to the Aave lending protocol for simulation and testing purposes, where Auto$.$gov has demonstrated the capability to retain funds that would have otherwise been lost to price oracle attacks. In tests with real-world data, Auto$.$gov outperforms the benchmark approaches by at least 14% and the static baseline model by tenfold, in terms of the preset performance metric--protocol profitability. Overall, the comprehensive evaluations confirm that Auto$.$gov is more efficient and effective than traditional governance methods, thereby enhancing the security, profitability, and ultimately, the sustainability of DeFi protocols.

Suggested Citation

  • Jiahua Xu & Yebo Feng & Daniel Perez & Benjamin Livshits, 2023. "Auto.gov: Learning-based Governance for Decentralized Finance (DeFi)," Papers 2302.09551, arXiv.org, revised May 2025.
  • Handle: RePEc:arx:papers:2302.09551
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    References listed on IDEAS

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    3. Jiahua Xu & Yebo Feng, 2022. "Reap the Harvest on Blockchain: A Survey of Yield Farming Protocols," Papers 2210.04194, arXiv.org, revised Dec 2022.
    4. Oehmke, Martin, 2014. "Liquidating illiquid collateral," Journal of Economic Theory, Elsevier, vol. 149(C), pages 183-210.
    5. Hall, Thomas W., 2012. "The collateral channel: Evidence on leverage and asset tangibility," Journal of Corporate Finance, Elsevier, vol. 18(3), pages 570-583.
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