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.
My main interest is about Crisis, Recovery, and the effects of Policies in the complex adaptive system.
We have experienced several crises which shook the world or some regions.
The 2008 Financial crisis was one of them. The impact was the most serious since the Great depression.
It is just the Black swan? Otherwise, the Dragon King ?
Is it different from other crises?
It has affected and changed many things in economic, political and social institutions.
Policy makers want to restore economies and societies.
However, advanced countires have suffered from slow economic recovery and increasing inequality.
Are there any problems in governments' policies for recovery? Are they insufficient or inappropriate?
Whatever you answer, you will confront the weaknesses of conventional models and recovery policies. This is my starting point of my research.
I completed my dissertation in 2015. My dissertation contains three models.
The first one is for systemic risks in random networks and power-law networks. I suggest a crisis is a very-rare-but-extremely dangerous event.
The second one is called "Hidden spread of systemic risk model". It shows the spread patterns of systemic risks in the multiple networks,and the pop-corn effect.
Finally, I provide an explanation about the slow recovery after the 2008 financial crisis. My model is an evolutionary prisoner's dilemma game in a network.
or a graphical PD game.
We are interconnected with others. My current behavior can affect future or current behaviors of my neighbors.
This interconnectedness creates complexity. In the complex system, there are no static equilibrium and representative agent. This seems to be more realistic.
Macro-social phenomena are emergent through interactions of heterogenous agents. We can easily find examples for it, such as GDP, voting results, conflicts, etc.
Ond of the famous examples is the tragedy of commons.
This tragedy emerges from interactions of agents who NEVER want this result. Regardless of the agents' aims, the unexpected outcome can occur.
Let's focus on macro-behavior through micro-motives. Crisis and Recovery are also collective phenomena of the macro-behaviors. Although no one wants a crisis and all want a fast recovery,
the collective phenomena of the agents are often unexpected. It is the nature of the complex system. I think we can find better explnations and predictions to our current issues of the inter-dependent world.
I created a simple Netlogo model for the tragedy of commons. 
