Voting-Bloc Entropy: A New Metric for DAO Decentralization
- URL: http://arxiv.org/abs/2509.22620v1
- Date: Fri, 26 Sep 2025 17:46:07 GMT
- Title: Voting-Bloc Entropy: A New Metric for DAO Decentralization
- Authors: Andrés Fábrega, Amy Zhao, Jay Yu, James Austgen, Sarah Allen, Kushal Babel, Mahimna Kelkar, Ari Juels,
- Abstract summary: Decentralized Autonomous Organizations (DAOs) use smart contracts to foster communities working toward common goals.<n>This work proposes a new framework for measuring decentralization called Voting-Bloc Entropy (VBE)<n>VBE is based on the idea that voters with closely aligned interests act as a centralizing force and should be modeled as such.
- Score: 12.631360643036137
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Toward a Sustainable Federated Learning Ecosystem: A Practical Least Core Mechanism for Payoff Allocation [71.86087908416255]
We introduce a payoff allocation framework based on the least core (LC) concept.<n>Unlike traditional methods, the LC prioritizes the cohesion of the federation by minimizing the maximum dissatisfaction.<n>Case studies in federated intrusion detection demonstrate that our mechanism correctly identifies pivotal contributors and strategic alliances.
arXiv Detail & Related papers (2026-02-03T11:10:50Z) - SoK: Measuring Blockchain Decentralization [7.274273862904251]
In the context of blockchain systems, the importance of decentralization is undermined by the lack of a widely accepted methodology to measure it.<n>We put forth a framework that categorizes all measurement techniques used in previous work based on the resource they target.<n>We complement this framework with an empirical analysis designed to evaluate whether the various pre-processing steps and metrics used in prior work capture the same underlying concept of decentralization.
arXiv Detail & Related papers (2025-01-30T11:37:34Z) - Proof-of-Data: A Consensus Protocol for Collaborative Intelligence [4.362312381717716]
We propose a blockchain-based Byzantine fault-tolerant federated learning framework based on a novel Proof-of-Data (PoD) consensus protocol.<n>PoD is able to enjoy the benefit of learning efficiency and system liveliness from societal-scale PoW-style learning.<n>To mitigate false reward claims by data forgery from Byzantine attacks, a privacy-aware data verification and contribution-based reward allocation mechanism is designed to complete the framework.
arXiv Detail & Related papers (2025-01-06T12:27:59Z) - Future of Algorithmic Organization: Large-Scale Analysis of Decentralized Autonomous Organizations (DAOs) [45.02792904507959]
Decentralized Autonomous Organizations (DAOs) resemble early online communities, particularly those centered around open-source projects.
In just a few years, the deployment of governance tokens surged with a total of $24.5 billion and 11.1M governance token holders collectively managing decisions across over 13,000s as of 2024.
We examine factors such as voting power, participation, and characteristics dictating the level of decentralization, thus, the efficiency of management structures.
arXiv Detail & Related papers (2024-10-16T23:45:10Z) - DAO Decentralization: Voting-Bloc Entropy, Bribery, and Dark DAOs [6.92620603424171]
Decentralized Autonomous Organizations (DAOs) use smart contracts to foster communities working toward common goals.
We propose a new metric called Voting-Bloc Entropy (VBE) that formalizes a broad notion of decentralization in voting on proposals.
We present the first practical realization of a Dark Sapphire, a proposed mechanism for privacy-preserving corruption of identity systems.
arXiv Detail & Related papers (2023-11-06T21:13:06Z) - Networked Communication for Decentralised Agents in Mean-Field Games [59.01527054553122]
We introduce networked communication to the mean-field game framework.<n>We prove that our architecture has sample guarantees bounded between those of the centralised- and independent-learning cases.<n>We show that our networked approach has significant advantages over both alternatives in terms of robustness to update failures and to changes in population size.
arXiv Detail & Related papers (2023-06-05T10:45:39Z) - Unpacking How Decentralized Autonomous Organizations (DAOs) Work in
Practice [54.47385318258732]
Decentralized Autonomous Organizations (DAOs) have emerged as a novel way to coordinate a group of entities towards a shared vision.
In just a few years, over 4,000 DAOs have been launched in various domains, such as investment, education, health, and research.
Despite such rapid growth and diversity, it is unclear how theses actually work in practice and to what extent they are effective in achieving their goals.
arXiv Detail & Related papers (2023-04-17T01:30:03Z) - Decentralized Local Stochastic Extra-Gradient for Variational
Inequalities [125.62877849447729]
We consider distributed variational inequalities (VIs) on domains with the problem data that is heterogeneous (non-IID) and distributed across many devices.
We make a very general assumption on the computational network that covers the settings of fully decentralized calculations.
We theoretically analyze its convergence rate in the strongly-monotone, monotone, and non-monotone settings.
arXiv Detail & Related papers (2021-06-15T17:45:51Z) - Consensus Control for Decentralized Deep Learning [72.50487751271069]
Decentralized training of deep learning models enables on-device learning over networks, as well as efficient scaling to large compute clusters.
We show in theory that when the training consensus distance is lower than a critical quantity, decentralized training converges as fast as the centralized counterpart.
Our empirical insights allow the principled design of better decentralized training schemes that mitigate the performance drop.
arXiv Detail & Related papers (2021-02-09T13:58:33Z) - Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain
Adaptation using Structurally Regularized Deep Clustering [119.88565565454378]
Unsupervised domain adaptation (UDA) is to learn classification models that make predictions for unlabeled data on a target domain.
We propose a hybrid model of Structurally Regularized Deep Clustering, which integrates the regularized discriminative clustering of target data with a generative one.
Our proposed H-SRDC outperforms all the existing methods under both the inductive and transductive settings.
arXiv Detail & Related papers (2020-12-08T08:52:00Z) - Byzantine-resilient Decentralized Stochastic Gradient Descent [85.15773446094576]
We present an in-depth study towards the Byzantine resilience of decentralized learning systems.
We propose UBAR, a novel algorithm to enhance decentralized learning with Byzantine Fault Tolerance.
arXiv Detail & Related papers (2020-02-20T05:11:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.