🤖 AI Summary
This study addresses the inability of traditional macroeconomic models to capture the discontinuous and structural nature of institutional collapse. It proposes a discrete-time Stochastic Network Governance (SNG) model that integrates econophysics, network science, and institutional economics, centered on binary institutional genes to represent institutional complementarities, endogenous growth, and the nonlinear macroeconomic costs of reform. Innovatively embedding CEPII gravity data and IMF banking crisis records into an agent-based Monte Carlo simulation, the model reveals—for the first time—a global phase transition mechanism driven by institutional collapse and spatial capital flows. It introduces the “hub-risk paradigm” and demonstrates the emergent resilience of spatial firewall networks. The framework successfully replicates historical episodes such as the Soviet Union’s dissolution and demonstrates predictive power regarding institutional resilience and crisis propagation across the world’s 100 largest economies from 1970 to 2017.
📝 Abstract
Traditional macroeconomic growth models rely on general equilibrium and continuous, frictionless institutional transitions, failing to account for the catastrophic structural collapses observed in empirical economic history. We propose the Stochastic Networked Governance (SNG) model, a discrete-time, agent-based framework that bridges econophysics, network science, and institutional economics. By defining jurisdictions through a binary institutional genome, the model formalizes institutional complementarity, endogenous growth, and the non-linear macroeconomic penalties of structural reform (the "J-Curve"). Using the CEPII Gravity Database and the IMF Systemic Banking Crises dataset, we move beyond theoretical topologies to execute an empirical historical simulation from 1970 to 2017 across the top 100 global economies. Through Monte Carlo ensembles, we demonstrate how scale-invariant exogenous shocks and spatial capital flight drive global phase transitions, exposing the mathematical mechanics of the 1989-1991 Soviet collapse, the Hub-Risk Paradigm, and the emergent resilience of spatially firewalled market networks.