The whole is more than the simple sum of its parts.
It was with this very short phrase that my fascination with complexity sciences began.
To sum what’s a huge area of knowledge, complexity sciences aims to study complex systems, systems that display emergent behaviours, which cannot be predicted by the characteristics of its parts. From societies, to astronomy, biology, medicine, politics, economics…. we find complex systems everywhere.
From the birth of complexity sciences (around the 1950’s) to today, those who study this area of knowledge gained access to plenty of new techniques and methodologies to use, mainly from technology and mathematics. One of the most used techniques is mathematically model the system being studied and using data to assess its accuracy.
Using data to backup the mathematical model is crucial and eventually…. this lead up to a whole different area inside the complexity sciences constellation….ya know….data science. 🙂 . We can say that complexity sciences are the grandma of data science.
All these years and a Masters in Complexity Sciences later and I keep with me very important lessons from my time as a complexity sciences student.
1 – Knowing where your data comes from matters, a lot – Every data has an origin and said origin, more often than not, is the reason of many peculiar things in that data. Going to the source, as much as possible, allows for us to understand the story of the data and the system. And more often, the problems one finds in data analysis…..yep, you’ll find why they happen in the origin.
2 – Considering the system where the data exists is crucial in your work – your data doesn’t exist in a void. If one wants to have a productive project that will be used by those around you, one needs to consider the system in which the data is integrated. What is the tech stack available to you? How reliable is it? Who are your “clients” within the system? What is the level of mathematics and data visualization they are used to? Are people going to directly interact with your model? If yes, how? All of these are questions we need to answer when working on a project to best provide a suitable answer for it. Every system has its own peculiarities so spend your time knowing them.
3 – Simplify whenever possible, explain whenever needed, keep everything registered – Do not overcomplicate a model. This seems counterproductive, doesn’t it? However, when we spend our time trying our best to find the underlying rules of a model and simplifying them as possible, we gain a better understanding of the system, being better able to work within it. Add to this a careful register of your work, adding useful explanations to your client that make everything as clear as possible and you’ll be an effective data scientist. And will help you maintain the model for a long time, avoiding the “Wtf is this?” situations as much as possible.
4- Finally, be open to the wonders of emergent behaviour – One of the coolest parts of a complex system is what is called emergent behaviour, or behaviour that cannot be deduced by the characteristics of the parts of the system alone. It gives us a peak on the wonders of communication and network behaviour that exists in such wonders of nature as a beehive, the ocean or even yourself, dear human. Ya know that you are a freakish awesome marvel of nature that is more than the simple sum of your cells? =) right. These emergent behaviours are, more often than not, sources of great study and research projects that aim to discover the wonders of everything around us and might give a peak of crucial factors to study in the system you are working/studying. Let some control go and let nature lead you, you’ll be amazed.
And if nothing of the four points took you to think about complexity sciences, maybe these adorable pups will. Remember, none of them knows how to create a pinwheel, all they know is that they want that milk…. badly =)