Machine Learning Algorithms for Enhancing Governance in Blockchain-Based DAOs

Abstract

This study presents an innovative framework integrating machine learning algorithms to enhance the operational efficacy of blockchain-based decentralized autonomous organizations (DAOs). By leveraging reinforcement learning, deep Q-networks, natural language processing (NLP), and genetic algorithms, the framework addresses core challenges in governance automation, decision optimization, and adaptive resource management within DAOs. The proposed method demonstrates superior performance across critical metrics such as accuracy, scalability, convergence speed, and energy efficiency. Additionally, domain-specific visualizations, such as real-time Q-value convergence and NLP-enhanced proposal analysis, are introduced to ensure interpretability and transparency. This multidisciplinary approach not only overcomes inherent limitations in traditional DAO structures but also introduces a scalable, intelligent, and fair governance model capable of dynamic adaptation in decentralized environments.