Stochastic Networked Governance: Bridging Econophysics and Institutional Dynamics in a Positive-Sum Agent-Based Model

TL;DR

The paper introduces the Stochastic Networked Governance model, an agent-based framework combining econophysics, network science, and institutional economics to simulate macroeconomic dynamics and crises from 1970 to 2017.

Contribution

It develops a novel discrete-time agent-based model that captures institutional complementarity, endogenous growth, and structural reform penalties, validated with empirical historical data.

Findings

Demonstrates how shocks and capital flight cause phase transitions in global economies.

Reveals mechanisms behind the Soviet collapse and market resilience.

Shows the importance of spatial network structures in economic stability.

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.