DAOs as Complex Adaptive Systems: Governance Under Conditions of Decentralized Complexity

Andrea Cesaretti ()

Chapter 8 in DAO Governance in Theory and Practice, 2025, pp 179-206 from Springer

Abstract: Abstract This chapter reframes DAOs as complex adaptive systems (CAS), emphasizing that governance outcomes emerge not from static code but from fluid interactions among participants, narratives, markets, and external environments. Drawing on complexity theoryComplexity theoryadaptive systems and institutional analysis, it argues that DAOs are evolving ecosystems shaped by emergence, non-linearity, feedback loops, and adaptive learning rather than deterministic rule-following. Through case evidence, the chapter illustrates how spontaneous norms, narrative dynamics, and informational cascades generate emergent governance behaviors, while small events often trigger disproportionate systemic effects. Feedback loops, social, economic, and technical, are shown to both amplify and stabilize DAO governanceDAO governancedelegation systems, highlighting the role of institutional memory and reflexive processes in sustaining coherence over time. Concepts such as governance fitness landscapes and phase transitions are introduced to capture how DAOs move across peaks and valleys of resilience, legitimacyLegitimacy, and efficiency. Building on these insights, the chapter outlines design principles for adaptive governance: modularity, error tolerance, reflexivity, and structural diversity. Practical tools such as meta-governance frameworks, dynamic quorum, and simulation models translate these principles into operational practice. Ultimately, DAOs are presented not as rigid machines but as living institutional ecologies, antifragile systems capable of learning, reorganizing, and thriving amidst uncertainty.

Date: 2025
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DOI: 10.1007/978-3-032-09675-3_8

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