Towards a general theory of consensus: probabilistic distributed fault tolerance consensus, decentralized voting in DAOs, and a unified consensus framework

Fan, Yixuan (2025) Towards a general theory of consensus: probabilistic distributed fault tolerance consensus, decentralized voting in DAOs, and a unified consensus framework. PhD thesis, University of Glasgow.

Abstract

Consensus serves as a foundational mechanism in both social coordination and distributed technical systems. While machine consensus research in engineering focuses on fault tolerance and synchronization, social science emphasizes human deliberation, participation, and governance. However, the increasing convergence of human and machine decision making, exemplified by decentralized autonomous organizations (DAOs), intelligent agents, and cyber-physical social systems, demands a more integrated and theoretically robust understanding of consensus. This thesis addresses this interdisciplinary gap by investigating consensus across three interconnected dimensions: probabilistic fault-tolerant consensus systems, human-driven voting mechanisms in DAOs, and a unified conceptual framework bridging human and machine consensus.

The first part of the thesis focuses on distributed fault-tolerant consensus in uncertain environments. Traditional approaches often rely on deterministic assumptions about node failures and fixed quorum rules. These assumptions may fail to reflect real-world systems where node behaviour is influenced by heterogeneous reliability and probabilistic failures. To address this limitation, a probabilistic modelling framework is proposed, treating node reliability as a stochastic variable. Within this framework, consensus outcomes are classified into three categories: safe, risky, and compromised. A new concept, referred to as the reliability quorum, is introduced to provide a more flexible threshold for achieving consensus based on targeted reliability levels. This model enables system designers to tailor fault tolerance according to specific reliability requirements, providing both analytical clarity and practical adaptability.

The second part investigates consensus in decentralized systems primarily driven by human-oriented agents, using DAO voting as a representative case. In contrast to deterministic coordination among machines, DAO consensus arises from voluntary participation, heterogeneous voting power, and non-uniform approval conditions. To guide the analysis, the thesis introduces the DAO governance triangle alongside the SEED framework, which qualitatively evaluates voting mechanisms across four dimensions: Security, Efficiency, Effectiveness, and Decentralization. Building on this conceptual foundation, the study proceeds to a quantitative investigation of two key SEED dimensions. For decentralization, a stochastic process model is proposed to capture probabilistic participation and power distribution, leading to the formulation of the Consistency Rate and the Decentralization Coefficient as quantitative indicators. For efficiency, the model is further extended to characterize the interactions among participation probability, voting duration, and approval rate, enabling a formal evaluation of voting responsiveness and resource usage. Simulation results support both aspects of the analysis, revealing how power concentration, turnout behaviour, and mechanism design jointly influence decentralization and efficiency in DAO voting.

The third part presents a unifying conceptual framework to analyse consensus across human, machine, and human-machine hybrid systems. Despite disciplinary differences, the thesis identifies three core components of any consensus process: participants (the actors of agreement), communication (the medium of exchange), and state (the evolving representation of agreement). Framing consensus as an entropy-reduction process that resolves cognitive or informational divergence, this abstraction enables comparative analysis across diverse systems. The framework also distinguishes among human consensus, machine consensus, and human-machine hybrid consensus, and offers design guidelines aligned with the characteristics and limitations of each.

Together, these three threads construct a comprehensive theory of consensus that connects distributed computation, social governance, and emerging hybrid collectives. By integrating modelling, evaluation, and abstraction, this thesis contributes a multi-layered foundation for understanding and designing consensus mechanisms that are robust, scalable, and trustworthy in increasingly decentralized and intelligent environments.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
• Colleges/Schools: College of Science and Engineering > School of Engineering
• Supervisor's Name: Zhang, Professor Lei and Sun, Dr. Yao
• Date of Award: 2025
• Depositing User: Theses Team
• Unique ID: glathesis:2025-85432
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 21 Nov 2025 10:53
• Last Modified: 21 Nov 2025 15:19
• Thesis DOI: 10.5525/gla.thesis.85432
• URI: https://theses.gla.ac.uk/id/eprint/85432

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