Computational Architects of Society: Quantum Machine Learning for Social Rule Genesis
Shan Shan

TL;DR
This paper introduces a novel quantum-inspired computational framework combining quantum mechanics and generative AI to simulate and analyze the emergence and evolution of social norms as complex, uncertain systems.
Contribution
It presents a new theoretical and computational approach that applies quantum principles to model social systems and norm dynamics, bridging social theory and quantum computing.
Findings
Quantum principles enable modeling of uncertainty and emergence in social systems.
Simulations show convergence toward social norms and spontaneous emergence of new equilibria.
The framework provides a foundation for quantum-informed social theory and system redesign.
Abstract
The quantification of social science remains a longstanding challenge, largely due to the philosophical nature of its foundational theories. Although quantum computing has advanced rapidly in recent years, its relevance to social theory remains underexplored. Most existing research focuses on micro-cognitive models or philosophical analogies, leaving a gap in system-level applications of quantum principles to the analysis of social systems. This study addresses that gap by proposing a theoretical and computational framework that combines quantum mechanics with Generative AI to simulate the emergence and evolution of social norms. Drawing on core quantum concepts--such as superposition, entanglement, and probabilistic measurement--this research models society as a dynamic, uncertain system and sets up five ideal-type experiments. These scenarios are simulated using 25 generative agents,…
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Taxonomy
TopicsEmbodied and Extended Cognition · Quantum Mechanics and Applications · Chaos, Complexity, and Education
