Introduction to Complexity Science

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  • Course Overview and Week 1: Introduction to Complex Systems
    • An overview of what is covered in the first topic: an introduction to complex systems, explaining how complexity science has evolved, how it has been applied in society, and why it is important to gain a basic understanding of complex systems. Like for all sciences, complexity science is not a spectators' sport. After learning models and methods from the lectures, you will need to try some of these out to develop a practical feel for what they mean and what they can do. This is where the Jupyter Notebook exercises come in. In this course week, we will try out two Jupyter Notebook exercises, on: (1) the Nagel-Schreckenberg model of vehicular traffic, and (2) the Game of Life.
  • Week 2: Robustness, Resilience, and Sustainability
    • In this 2nd topic, we look at how robustness, resilience and sustainability can be defined for complex systems, and some case studies that showcase these attributes.
  • Week 3: Regime Shifts and Tipping Points
    • In this third topic, we move on to looking at regime shifts and tipping points and their applications in forecasting.
  • Week 4: Introduction to Agent-Based Modeling
    • Next, we look at Agent-Based Modeling - what it is, how it works, why it is used and how to use it. We then try a Jupyter Notebook exercise on Schelling’s Segregation Model.
  • Week 5: Introduction to Static Complex Network
    • Lastly, we look at complex networks and their attributes before looking at different network models. We end this topic with a Jupyter Notebook exercise on epidemics on complex networks.

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