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Theoretical framework demonstrates an AI-assisted self-governance model for democratic societies, suggesting improved resource allocation and complex decision-making over representative systems.
Cybernetic Democracy examines whether artificial intelligence could reduce the information-processing and coordination constraints that shaped representative government. Rather than proposing machine rule, the framework preserves human political sovereignty while using AI for preference articulation, institutional memory, multi-objective optimization, policy simulation, delegated attention and continuous feedback. The model distinguishes democratic, epistemic and causal weight; introduces participation neutrality, incentive compatibility and intertemporal Pareto optimization; separates Policy, Governance and Integrity loops; and proposes recursive jurisdiction from local to global levels. It further develops requirements for cybersecurity, universal accessibility, economic viability, gradual adoption and institutional continuity during prolonged technological failure. The central hypothesis is testable: a decentralized, AI-assisted and continuously feedback-driven democratic architecture may manage social complexity with less information loss, greater continuity, better resource allocation and stronger error correction than predominantly representative systems designed under earlier technological constraints.
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Cyril Philipp Zodtner (2026) studied this question.