This research examines the polycentric governance of digital assets in blockchain-based Decentralized Autonomous Organizations (DAOs). It offers a theoretical framework and addresses a critical challenge facing decentralized governance by developing a method to identify sybils, or spurious identities. Sybils pose significant organizational sustainability threats to DAOs and other, commons-based online communities, and threat models are identified. The experimental method uses graph deep learning techniques to identify sybil activity in a DAO governance dataset (snapshot.org). Specifically, a Graph Convolutional Neural Network (GCNN) learned voting behaviours and a fast k-means vector clustering algorithm (FAISS) used high-dimensional embeddings to identify similar nodes in a graph. The results reveal that deep learning can effectively identify sybils, reducing the voting graph by 2-5%. This research underscores the importance of sybil resistance in DAOs and offers a novel perspective on decentralized governance, informing future policy, regulation, and governance practices.

翻译：本研究探讨了基于区块链的去中心化自治组织(DAO)中数字资产的多中心治理问题。本文提出了一个理论框架，并通过开发识别女巫身份(虚假身份)的方法，解决了去中心化治理面临的关键挑战。女巫攻击对DAO及其他基于共享资源的在线社区构成重大组织可持续性威胁，本文识别了相关威胁模型。实验方法采用图深度学习技术，在DAO治理数据集(snapshot.org)中识别女巫活动。具体而言，图卷积神经网络(GCNN)学习投票行为，快速k均值向量聚类算法(FAISS)利用高维嵌入识别图中的相似节点。研究结果表明，深度学习能够有效识别女巫攻击，使投票图规模缩减2-5%。本研究强调了DAO中抗女巫攻击的重要性，为去中心化治理提供了新视角，并对未来政策、监管及治理实践具有参考价值。