Paper: arXiv 2302.09551

Authors: 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.

Complexity vs Empirical Score

  • Math Complexity: 5.5/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper introduces an innovative deep Q-network framework applied to automated DeFi protocol governance and parameter tuning. It provides a solid simulation environment modeled after Aave and demonstrates resilience against price oracle attacks, though real-world execution and smart contract code verification remain to be fully evaluated.

Research Flowchart

  flowchart TD
    A["Research Goal: Create automated, attack-resilient DeFi governance"] --> B["Build Aave-like DeFi Simulation Environment"]
    C["Inputs: Real-world Market Data & Price Oracle Attack Scenarios"] --> B
    B --> D["Train Deep Q-Network RL Strategy (Auto.gov)"]
    D --> E["Execute Semi-Automated Parameter Adjustments"]
    E --> F["Outcomes: Retains funds during oracle attacks & beats baseline profitability by 10x"]