Andrea’s Complexity MOOC at Santa Fe


I have always been interested in chaos and complexity, and how they might be applied to human systems. After wading knee-deep into conversations about complexity I realized I didn’t really understand the science, the math, the physics or the computer science models that were the basis of complexity and chaos theory so I enrolled in a MOOC (Massive Online Open Course) at the Sante Fe Institute taught by the remarkable and charming Melanie Mitchell.

After several months and many high school math flashbacks, I came away with useful definitions and a starting point to our discussions of complexity.

Complex Systems are large networks of simple, interacting elements which, following simple rules, produce emergent, collective, complex behaviour.

Some clarifications:
Interacting elements: Agents or people in a system which are interconnected, interdependent, and have unpredictable effects on each other.

Following Simple Rules: Simple rules provide the blueprint for any pattern that is produced from a complex system.

A good example of Simple Rules that govern a large number of agents is flocking behavior. Flocking behaviour has no central control and is instead governed by three simple rules:
1. Separation – Avoid crowding your neighbors
2. Alignment – Steer towards the average heading of neighbours
3. Cohesion – Steer towards the average position of neighbours

Emergent behaviour is defined as behaviour that appears not to involve any central coordination. Emergence can be defined as novel patterns arising in a system that are not the result of one agent or the sum of parts.

From this initial definition, I started to think of complexity as a worldview in which to see systems. Moving directly from a math equation or a scientific model to a model of human behaviour can be fraught and contentious, but by thinking of models as lenses, we start to see scalable patterns.

Other topics arising from my personal study and the Santa Fe connection are: non-linear, dynamic, periodic attractors, sensitive dependence on initial conditions, patterns through iterations, networks and hubs, fractals, scaling. We are excited about these characteristics of complex systems and will tackle them in the future.

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