Simplicity = Low uncertainty
Take a simple system like a light switch, there are not really a lot of possibilities for something unexpected to happen. In one configuration of the switch the light is on, and the other one the light is off. The only thing that could possibly happen is that in both configurations the light remains off (I can’t really think of a situation for when both configurations actually turn the light on apart coming from a fake switch).
Now as you scale this example, up to a certain level of complexity, you will start to see this model being combined in serial and parallel. In serial we may have a lamp (with its switch) plugged onto an extension cord that has a safety switch, itself plugged onto a UK electric socket with its safety switch too. But that’s not it, the socket is part of the electrical network of your house that is all linked to the electric board panel with fuses and switches too. If you live in a flat, this flat has its own fuse box that is also linked with some master electric board panel that manages the electricity to flow in the building delivered to the multiple communal parts and apartments. And it doesn’t stop here. Off course electricity is distributed across cities via an “electric grid system” that has electric transformers covering a specific territory, and this is itself part of a network of power plants that produce this electricity in the first place.
I will take this even further as we need material and a set of skilled and organised humans to build, run and maintain these power plants. Each individual node, interconnected with a vast system, which itself has interconnection with other systems, all trying to synchronise with one another desperately, in a cosmic dance, as an attempt to avoid chaos.
When experts look at problems for things like “why don’t you have electricity coming into your living room”, they will start with the consequences and go as upstream as they can to find the cause of such consequences. But this can only work on what’s called “complicated problems”, where a specialist can clearly map the system he is looking at and where there are no real unknowns (like a watchmaker or a mechanic working on a car engine — such systems are complicated, could be simple, but aren’t what we call “complex” systems — you need a certain number of parts and mechanism to make this work, and with time, passion and experience, one can master these systems and leave no rocks uncovered).
Complexity = High uncertainty
This time let’s take a complex system, like the socio-economical system. Despite the tales of politicians, there is nothing more volatile than the economy. There have been so many regulations, laws and rules designed in the past century that it becomes impossible for an educated citizen to really understand it all. You will need to hire people that specialised in deciphering the codes behind this unnecessary and complex bureaucratic language.
Not only these systems of codifications that regulates in part the economy are complex, but they are all interconnected with other systems of codification makes it even harder to sometimes do international trade without having to deal with a lot (and I mean a lot) of paperwork! Although there are unified codes today as globalisation is reaching its peak, they actually add layers of complexity. The Brexit (UK exiting the EU) was partly announced as a solution to the increasing complexity of regulations that was imposed on EU members, not only requiring them to comply with the system but also to finance it! (Not that I support Brexit, I value humans above money, and I think the world is ours and we should be free to explore it. We don’t ask for a fish or a bird to show us their passports when they migrate seasonally, neither do we ask them to pay taxes for staying over).
Beyond it, the economy is itself part of a more vastly interconnected mesh of systems: taxes make it partly interface with the political system, stock market fluctuates responding to instant information generated by the media systems (including internet, social media, etc.), and the housing market impact on the banking system (housing belongs to the social system that constitutes a city for example, which its itself part of a larger system, like a state).
Now take the Covid-19 pandemic, most economists present us today with complicated graphs that are trying to depict some financial predictions for the future, but these same economists couldn’t actually predict the 2008 economic bubble burst in the first place.
A world we don’t understand
We have to be humble. As the circle of our knowledge expands, the perimeter of our ignorance lengthen. Experts tend to fall into fallacious thinking when they use their specialist knowledge to look at more complex problems where there are a lot of unknown unknowns. Not only we cannot predict the future, but we can’t neither fully see and comprehend the depth of our reality and its possibilities (otherwise we will have God-like powers).
What if instead of controlling what we don’t understand, we could use and harness uncertainty for our benefits? We’ve seen ideas taken to the extreme, with its dictatorial and totalitarian side-effect of over-controlling every aspect of life, the people, and what they think. Utopias are nice ideas, but they definitely cannot be implemented in real-life, and dystopian literature has been addressing the risk utopias presents when one try to make it happen (Aldous Huxley wrote A Brave New World before Hitler took power, and George Orwell’s novel 1984 chillingly looks similar to the current methods corporations and medias use to bias peoples’ opinions, thoughts and behaviours).
What if we propose a grand vision but don’t impose it on people? What if this grand vision isn’t perfect and constantly refines itself as new shocks and opportunities come up? What if you could just let the seed of an idea be subjected to its environment in such a way this seed can take roots and form a magnificent eco-system?