My Research

Political Economy and Computationl Social Science Network, Simulation, and Computational Modeling

Computational Social Science

When defining Computational Social Science (CSS), a central theme consistently emerges: the use of advanced computational methods to analyze large-scale datasets with the aim of understanding and interpreting human behavior. What distinguishes CSS is not merely its focus on social phenomena, but its reliance on computational techniques and extensive data to achieve insights.
CSS rests on two primary pillars: Network Analysis and Social Simulation.
Network Analysis has flourished with the availability of rich relational data and advancements in computational tools, enabling more robust and data-driven exploration of complex social structures and interactions.
Social Simulation, on the other hand, takes a model-driven approach, using computational models to simulate and study emergent patterns and behaviors within social systems.

Computational Social Science 1 (Network)     Computational Social Science 2 (Simulation)

The Political Economy of Systems

My research explores the political economy of infrastructure, debt, and global power—how states and markets intersect to shape resilience, decline, and transformation. Building on analyses of public debt, market interest rates, and reserve-currency dynamics, I examine how nations manage the tension between internal constraints and external dependencies. This inquiry extends to understanding the thresholds of systemic transition, where mounting fiscal pressures and geopolitical shifts compel societies to renegotiate access, stability, and influence. Through this lens, I seek to uncover how governance, infrastructure, and financial architectures co-evolve amid complexity and global change.

Crisis, Recovery, and Policy in Complex Systems

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My research centers on crisis, recovery, and the policy dynamics within complex adaptive systems. Throughout history, we have witnessed multiple crises that have shaken the world or specific regions — among them, the 2008 financial crisis, whose impact was the most severe since the Great Depression. Was it a mere Black Swan event — unpredictable and rare — or a Dragon King, emerging from deep systemic instabilities? How was it different from past crises?

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In my dissertation, I developed two models that together explore the dynamics of crisis and recovery in complex systems. The first model examines systemic risk in random and power-law networks, suggesting that crises are rare but extremely dangerous emergent events. Building on this foundation, I developed the Hidden Spread of Systemic Risk Model, which simulates how risks propagate across multiple interconnected networks, producing what I term the “popcorn effect”—sudden bursts of cascading failures. Through this simulation, we can observe how, during the 2008 financial crisis, risks moved back and forth between the real-economy network and the financial network, amplifying instability and spreading failures throughout the system. The second model addresses the slow recovery that followed the 2008 financial crisis through an evolutionary Prisoner’s Dilemma on networks, illustrating how patterns of cooperation and defection evolve within interconnected structures and shape long-term resilience.

Network Analysis

Many human-made systems are fundamentally networked. Individuals, institutions, and even nations can all be viewed as nodes within interconnected systems. The concepts of network theory extend across diverse domains—trade, transportation, the spread of ideas and innovation, financial systems, macroeconomic structures, and contagion dynamics. To study these complex relationships, I use Gephi, R, and Python as my primary tools for visualization and analysis.

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The figure above illustrates the network of notable Renaissance figures, revealing the intricate connections among artists, thinkers, and patrons across disciplines. This visualization captures how creativity and influence circulated through the Renaissance world.

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The next figure presents my personal Facebook network, composed of one large component and several smaller sub-networks. The structure of each cluster reflects distinct patterns of interaction, showing how individuals connect, communicate, and form communities. Through such analyses, we can uncover the hidden architecture of social relationships and the interconnected patterns that shape collective behavior.

Innovation Network

SKIN model for Innovation Network.