【Proposal Abstract】Anarchy, Nonlinearity, and Emergence in International Relations - A Spiking Neural Network Framework Approach

Citation: HUANG, W. (2026, January 12). Anarchy, Nonlinearity, and Emergence in International Relations: A Spiking Neural Network Framework Approach. https://doi.org/10.17605/OSF.IO/XEKDA

Abstract

This research proposes a novel approach to modeling international relations by leveraging Quantum Spiking Neural Networks (QSNNs), integrating quantum computing principles with event-based neural dynamics. While existing systems theory methods have provided valuable insights, they face critical limitations in representing the rich qualitative diversity of international events.

Traditional approaches treat events as scalar occurrences or categorical variables, unable to capture subtle gradations in event meaning, intensity, and strategic ambiguity. The QSNN framework addresses this through quantum state representation on the Bloch sphere: each event is encoded as a quantum state vector with unit norm but distinct phase and amplitude components, enabling continuous high-dimensional representation of event qualities while maintaining geometric structure.

This quantum encoding allows us to:

• Distinguish infinitely many event types via continuous phase space
• Apply quantum physics tools (geometric phases, entanglement, QFT) to IR
• Represent superposition-like political phenomena
• Model relational structures as quantum fields

Combined with event-based neural computation, QSNNs enable efficient processing of sparse IR event data while unlocking powerful theoretical physics toolkits.

Expected contributions include a quantum-theoretic paradigm for IR modeling, quantum-enhanced equation discovery, insights into entanglement structures in alliance networks, and policy simulation incorporating quantum interference effects.