USENIX Security2025Top-tier venue
Voting-Bloc Entropy: A New Metric for DAO Decentralization
Andrés Fábrega, Amy Zhao, Jay Yu, James Austgen, Sarah Allen, Kushal Babel, Mahimna Kelkar, Ari Juels
Abstract
Decentralized Autonomous Organizations (DAOs) use smart contracts to foster communities working toward common goals. Existing definitions of decentralization, however -- the'D'in DAO -- fall short of capturing the key properties characteristic of diverse and equitable participation. This work proposes a new framework for measuring DAO decentralization called Voting-Bloc Entropy (VBE, pronounced''vibe''). VBE is based on the idea that voters with closely aligned interests act as a centralizing force and should be modeled as such. VBE formalizes this notion by measuring the similarity of participants'utility functions across a set of voting rounds. Unlike prior, ad hoc definitions of decentralization, VBE derives from first principles: We introduce a simple (yet powerful) reinforcement learning-based conceptual model for voting, that in turn implies VBE. We first show VBE's utility as a theoretical tool. We prove a number of results about the (de)centralizing effects of vote delegation, proposal bundling, bribery, etc. that are overlooked in previous notions of DAO decentralization. Our results lead to practical suggestions for enhancing DAO decentralization. We also show how VBE can be used empirically by presenting measurement studies and VBE-based governance experiments. We make the tools we developed for these results available to the community in the form of open-source artifacts in order to facilitate future study of DAO decentralization.
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Install the CLIlune papers fulltext 0e700970-e488-4d14-a945-a13dbbb7f3beCited by top-tier papers3
- B-Privacy: Defining and Enforcing Privacy in Weighted VotingSamuel Breckenridge, Dani Vilardell, Andrés Fábrega, Amy Zhao et al.USENIX Security 2026 · 2 citations
- On the Pitfalls of Modeling Individual KnowledgeWojciech Ciszewski, Stefan Dziembowski, Tomasz Lizurej, Marcin MielniczukCCS 2026
- Mind the Gap: Detecting Description-Execution Mismatch Attacks in DAO GovernanceBowen Cai, Nanzi Yang, Weiheng Bai, Youshui Lu et al.CCS 2026
Builds on12
- Making Smart Contracts SmarterLoi Luu, Duc-Hiep Chu, Hrishi Olickel, Prateek Saxena et al.CCS 2016 · 2,306 citations
- Celebrating Diversity in Shared Multi-Agent Reinforcement LearningChenghao Li, Tonghan Wang, Chengjie Wu, Qianchuan Zhao et al.NeurIPS 2021 · 224 citations
- Trajectory Diversity for Zero-Shot CoordinationAndrei Lupu, Brandon Cui, Hengyuan Hu, Jakob N. FoersterICML 2021 · 157 citations
- Learning to Coordinate Manipulation Skills via Skill Behavior DiversificationYoungwoon Lee, Jingyun Yang, Joseph J. LimICLR 2020 · 98 citations
- Maximum Entropy Population-Based Training for Zero-Shot Human-AI CoordinationRui Zhao, Jinming Song, Yufeng Yuan, Haifeng Hu et al.AAAI 2023 · 94 citations
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