GFN & Technology Models Library (2025–2035) (1.0.0)
GFN & Technology Models Library (2025–2035) is a collection of four interrelated computational models focused on global financial networks, cascading default risks, technological development, and technological lag, with particular attention to Russia, the USA, and China. The library combines agent-based modeling, system dynamics, Monte-Carlo simulation, and network analysis. It includes: (1) a Numba-accelerated Monte-Carlo technology index model; (2) a hybrid GFN + cascading defaults (Gai–Kapadia) + Russian system dynamics model; (3) a twin-simulation GFN+SBS framework with selective bailout and Russia’s peripheral position; and (4) an endogenous GFN model with technology centrality, dependence, and tech lag dynamics under sanctions scenarios. GFN & Technology Models Library (2025–2035) is a collection of four interrelated computational models focused on global financial networks, cascading default risks, technological development, and technological lag, with particular attention to Russia, the USA, and China.
The library combines agent-based modeling, system dynamics, Monte-Carlo simulation, and network analysis. It includes:
- Technology Index 2035 (Monte-Carlo + Numba) — hybrid SD/ABM model projecting a composite technology index under different scenarios (Russia baseline, China, USA) with aggressive Numba optimization.
- RUS-GFN-TSI-2035 CASCADE — hybrid model integrating a Gai–Kapadia-style cascading defaults network, an agent-based Global Financial Network layer, and a system-dynamics module for the Russian economy (TSI, inflation, technology, trust, etc.).
- GFN+SBS Twin-Simulation Framework — agent-based GFN + Shadow Banking System model comparing two regimes (with selective bailout vs pure market dynamics), calibrated to a stylized 2026 starting point, with focus on Russia’s peripheral position and risk of network exile.
- EndogenousGFN + Technology Block + Tech Lag — endogenous evolution of the Global Financial Network with explicit technology variables (tech_centrality, tech_dependence, tech_lag) under various sanctions and technology-restriction scenarios.
Purpose: support research on financial stability, sanctions impacts, technological competition, and hybrid ABM–SD modeling.
Technical details: Python 3.10+, dependencies include numpy, pandas, networkx, scipy, numba, streamlit, plotly. Source code is available on GitHub and archived on Zenodo (DOI: 10.5281/zenodo.22766267). An environment.yml is provided for reproducibility.
Release Notes
The library is intended for researchers in computational social science, financial stability, sanctions impact assessment, technological competition, and hybrid modeling. It supports exploration of systemic risk transmission, consequences of selective financial interventions, and long-term effects of technological restrictions and isolation.
Associated Publications
GFN & Technology Models Library (2025–2035) 1.0.0
GFN & Technology Models Library (2025–2035) is a collection of four interrelated computational models focused on global financial networks, cascading default risks, technological development, and technological lag, with particular attention to Russia, the USA, and China. The library combines agent-based modeling, system dynamics, Monte-Carlo simulation, and network analysis. It includes: (1) a Numba-accelerated Monte-Carlo technology index model; (2) a hybrid GFN + cascading defaults (Gai–Kapadia) + Russian system dynamics model; (3) a twin-simulation GFN+SBS framework with selective bailout and Russia’s peripheral position; and (4) an endogenous GFN model with technology centrality, dependence, and tech lag dynamics under sanctions scenarios. GFN & Technology Models Library (2025–2035) is a collection of four interrelated computational models focused on global financial networks, cascading default risks, technological development, and technological lag, with particular attention to Russia, the USA, and China.
The library combines agent-based modeling, system dynamics, Monte-Carlo simulation, and network analysis. It includes:
- Technology Index 2035 (Monte-Carlo + Numba) — hybrid SD/ABM model projecting a composite technology index under different scenarios (Russia baseline, China, USA) with aggressive Numba optimization.
- RUS-GFN-TSI-2035 CASCADE — hybrid model integrating a Gai–Kapadia-style cascading defaults network, an agent-based Global Financial Network layer, and a system-dynamics module for the Russian economy (TSI, inflation, technology, trust, etc.).
- GFN+SBS Twin-Simulation Framework — agent-based GFN + Shadow Banking System model comparing two regimes (with selective bailout vs pure market dynamics), calibrated to a stylized 2026 starting point, with focus on Russia’s peripheral position and risk of network exile.
- EndogenousGFN + Technology Block + Tech Lag — endogenous evolution of the Global Financial Network with explicit technology variables (tech_centrality, tech_dependence, tech_lag) under various sanctions and technology-restriction scenarios.
Purpose: support research on financial stability, sanctions impacts, technological competition, and hybrid ABM–SD modeling.
Technical details: Python 3.10+, dependencies include numpy, pandas, networkx, scipy, numba, streamlit, plotly. Source code is available on GitHub and archived on Zenodo (DOI: 10.5281/zenodo.22766267). An environment.yml is provided for reproducibility.
Release Notes
The library is intended for researchers in computational social science, financial stability, sanctions impact assessment, technological competition, and hybrid modeling. It supports exploration of systemic risk transmission, consequences of selective financial interventions, and long-term effects of technological restrictions and isolation.