Anarchy, Nonlinearity, and Emergence in International Relations: A Spiking Neural Network Framework Approach

This research proposes a novel approach to modeling international relations by leveraging the Spiking Neural Networks (SNN) framework within a systems theory perspective. While existing systems theory methods, including agent-based modeling, network analysis, and dynamical systems approaches have provided valuable insights into international phenomena, they face critical limitations: insufficient temporal dynamics in capturing event-driven processes, absence of mechanisms for history-dependent relational evolution, difficulties in multi-scale integration across micro, meso, and macro levels, and challenges in systematically constructing appropriate dynamical equations from theoretical assumptions or empirical data. The SNN framework addresses these limitations through four key advantages. First, its event-driven spiking mechanism naturally unifies the discrete occurrence of international events with their continuous effects. Second, synaptic plasticity mechanisms provide biologically-inspired rules for modeling how interstate relations evolve based on historical interaction sequences, capturing strengthening, weakening, and memory effects. Third, the hierarchical neuron-synapse-network structure corresponds naturally to the actor-relation-system architecture of international relations, enabling coupled analysis across scales within a unified mathematical framework. Fourth, the decomposition into neuronal and synaptic dynamics creates a structured search space that makes automated equation discovery computationally tractable through methods such as symbolic regression and neural architecture search. Our methodology employs data-driven approaches, utilizing international event databases (GDELT, ICEWS), relational data (trade flows, alliances), and behavioral data (policy texts, voting records) to automatically explore and validate dynamical equations. The research aims to develop scalable open-source infrastructure supporting flexible customization of dynamics, apply the framework to concrete cases such as great power competition and regional alliance evolution, and employ dynamical systems theory and algebraic topology to analyze emergent mechanisms. Expected contributions include methodological innovation in computational IR modeling, technical outputs lowering interdisciplinary barriers, theoretical insights into systemic phase transitions and critical phenomena, and practical tools for policy simulation and counterfactual analysis.