Fairness in Token Delegation: Mitigating Voting Power Concentration in DAOs

2025-10-07

AI Summary

Decentralized Autonomous Organization (DAO) governance suffers from widespread delegation mismatch and power concentration. Current reputation-based delegation mechanisms exacerbate visibility bias, granting disproportionate influence to a small set of highly ranked delegates—often misaligned with the community’s actual governance preferences. Method: We propose the first multi-source collaborative analysis framework integrating on-chain behavior, forum discourse, and large language models (LLMs). It leverages LLMs to quantitatively measure community governance interests and assess their alignment with delegate actions. Contribution/Results: Empirical analysis reveals significant divergence between current delegation patterns and token holders’ expressed preferences; ranking interfaces further entrench inequality. Building on these findings, we design a delegation optimization mechanism centered on “interest consistency.” Experiments demonstrate that this mechanism substantially improves governance representativeness and fairness.

Technology Category

• Multiagent Systems: Mechanism Design
• Search and Optimization: Distributed Search
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Decentralized Autonomous Organizations (DAOs) aim to enable participatory governance, but in practice face challenges of voter apathy, concentration of voting power, and misaligned delegation. Existing delegation mechanisms often reinforce visibility biases, where a small set of highly ranked delegates accumulate disproportionate influence regardless of their alignment with the broader community. In this paper, we conduct an empirical study of delegation in DAO governance, combining on-chain data from five major protocols with off-chain discussions from 14 DAO forums. We develop a methodology to link forum participants to on-chain addresses, extract governance interests using large language models, and compare these interests against delegates' historical behavior. Our analysis reveals that delegations are frequently misaligned with token holders' expressed priorities and that current ranking-based interfaces exacerbate power concentration. We argue that incorporating interest alignment into delegation processes could mitigate these imbalances and improve the representativeness of DAO decision-making.

Problem

• Addressing voting power concentration in DAO governance systems
• Mitigating misalignment between delegation choices and token holder interests
• Reducing visibility biases in existing token delegation mechanisms

Innovation

• Links forum participants to on-chain addresses using methodology
• Extracts governance interests with large language models
• Compares interests against delegates' historical voting behavior