用智能体模拟去中心化治理投票，效果接近人类决策。
DAO-AI: Evaluating Collective Decision-Making through Agentic AI in Decentralized Governance
- 构建智能体自动分析提案、检索历史数据并独立投票。
- 3000+真实提案测试显示其决策与人类和代币加权结果高度一致。
- 可解释、可审计，适合研究区块链治理的AI设计者。
本文首次实证研究了智能体作为去中心化治理中自主决策者的应用。基于来自主要协议的超过3000个提案，我们构建了一个能理解提案背景、检索历史讨论数据并独立决定投票立场的智能体。该智能体在基于可验证区块链数据的真实财务仿真环境中运行，通过模块化可组合程序（MCP）工作流实现，由Agentics框架定义数据流与工具使用。我们评估了该智能体决策与人类及代币加权结果的一致性，采用精心设计的评估指标发现其具有强一致性。研究结果表明，智能体可在真实DAO治理场景中增强集体决策能力，提供可解释、可审计且基于实证的信号。本研究为去中心化金融系统中可解释、经济严谨的AI代理设计提供了支持。
原文摘要 · Abstract (English)
This paper presents a first empirical study of agentic AI as autonomous decision-makers in decentralized governance. Using more than 3K proposals from major protocols, we build an agentic AI voter that interprets proposal contexts, retrieves historical deliberation data, and independently determines its voting position. The agent operates within a realistic financial simulation environment grounded in verifiable blockchain data, implemented through a modular composable program (MCP) workflow that defines data flow and tool usage via Agentics framework. We evaluate how closely the agent's decisions align with the human and token-weighted outcomes, uncovering strong alignments measured by carefully designed evaluation metrics. Our findings demonstrate that agentic AI can augment collective decision-making by producing interpretable, auditable, and empirically grounded signals in realistic DAO governance settings. The study contributes to the design of explainable and economically rigorous AI agents for decentralized financial systems.
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