Introduction to modeling complex systems

This week, we define what models are and explain why modeling is key to understanding complex systems.

Lhd offers three definitions of (mechanistic) models:

• simplified worlds and computational parabolas
• rule-based phenomenological (qualitative?) mechanisms
• in silico experiments

As cognitive scientist, I would add that models are also cognitive gadgets (see also Heyes' book). When Lhd talks about his romantic love for modeling, he describes it as a way of life. When something becomes a way of life, it changes how you engage with the world around you. You can sit on the shore of Lake Champlain and start pondering the timing of blue-green algae. With some modeling experience, you may start thinking about how the different parts of a system come together. You learn to set aside certain details while abstracting other components. Abstraction leads to greater generalization, enabling you to create narratives that apply to multiple contexts.

To clarify our definition of models, we can examine some examples of complex systems:

Emergence, co-evolution, instabilities, feedbacks, interdependence, adaptive, and complex. All these features are at the heart of complex systems. Were there any commonalities among the examples from the clip? One that I find intriguing is how various crowds behave in coherent ways, such as circle pits, bird flocking (fun fact: the sound made by a flock is called a murmuration), and fish bowl (or fish tornadoes?). Each of these exhibits interesting properties, such as nonseparability or emergence. But are circle pits really the same as flocking? By labeling both as emergent phenomena, are we potentially overlooking some key differences?

Bonus video: