GDP-Driven Structural and Dynamical Heterogeneity in the Synchronization of Chaotic Macroeconomic Networks
Thierry Njougouo, Fernando Fagundes Ferreira, and Diego Garlaschelli

TL;DR
This paper explores how structural and dynamical heterogeneity influence synchronization in chaotic macroeconomic networks, revealing fragile global synchronization with intermittent breakdowns due to disparities.
Contribution
It introduces a unified framework combining structural and dynamical heterogeneity to analyze synchronization transitions in macroeconomic networks.
Findings
Mean-field approach accurately predicts dynamics in homogeneous networks.
Heterogeneous networks exhibit partial synchronization and intermittency.
Laminar phase durations follow a power-law distribution, indicating intermittent synchronization.
Abstract
We investigate the emergence of synchronization in a network of coupled chaotic macroeconomic systems. Each node represents an economy characterized by three key variables savings, gross domestic product (GDP), and foreign capital inflows. These economies interact or are connected through a fitness-based probability that depends on the potential GDP of each node. This formulation allows both structural heterogeneity, arising from uneven network connectivity, and dynamical heterogeneity, due to differences in local parameters, to be explored within a unified framework. Using both numerical simulations and a mean-field approximation, by varying the coupling strength and the degree of heterogeneity of both network topology and dynamical behavior of the nodes, we analyze synchronization transitions. Our results show that the mean-field approach accurately captures the collective dynamics in…
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