Stochastic Networked Governance: Bridging Econophysics and Institutional Dynamics in a Positive-Sum Agent-Based Model
TLDR
The SNG model uses econophysics and network science to simulate how institutional changes and shocks drive economic collapses, validated with historical data.
Key contributions
- Introduces the Stochastic Networked Governance (SNG) model, an agent-based framework bridging econophysics and network science.
- Formalizes institutional complementarity, endogenous growth, and the J-Curve effect of structural reform.
- Empirically simulates 100 global economies from 1970-2017 using CEPII and IMF datasets.
- Demonstrates how shocks and capital flight drive phase transitions, explaining the Soviet collapse and market resilience.
Why it matters
Traditional economic models struggle to explain catastrophic collapses. This paper introduces a novel, empirically validated agent-based model. It offers a deeper understanding of how institutional dynamics and shocks drive major economic shifts and crises.
Original 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.
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