  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 an autoencoder architecture and 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 vector clustering algorithm 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, identifies challenges and opportunities for forensics and analysis of
anonymous networks, and offers a novel perspective on decentralized governance,
informing future policy, regulation, and governance practices.
