Simple is not the opposite of complex

“This is too complex, we have to make it simpler.” I would assume we have all heard this sentence at some point or another. Be it about political decisions, processes, products, forms and so on.

You don’t just make a complex system simpler.

My proposition now is that if someone finds something too complex, they have not understood it. Of course, the world around us is getting more and more complex. And we don’t have the time to understand everything in the necessary depth to make sense out of it.
BUT, if you are in control over a situation and you find it too complex, please, don’t try to make it simpler without understanding it first. Maybe the system is not that complicated after all, when you understand it better.

Any complex system is made up of simple rules. All systems have nodes or actors, relationships and perspectives. And each element of the system follows simple rules. It’s the amount of rules and interconnectedness of rules that make it not so easy to understand. And yes, it is a lot of work to dissect a system to understand all the rules, relations and motivations.

Tangent: Ant Hill

An ant hill is a complex system. It consists of thousands of individual animals. And yet, something like a structure is visible when looking at it. But why? For example, ants follow simple rules when collecting food.

1. When a scout finds food, take a piece and bring it back to the ant hill
2. When bringing food back to the ant hill, drop pheromone
3. Workers follow the pheromone
4. Never cross the path!

This behavior leads at first to slightly chaotic movements that quickly bring structure. As more and more ants follow, the shortest path between ant hill and food source evolves, and the classic ant roads can be observed.

Complex systems follow simple rules. So when a system is too complex, try to find the simpler underlying rules.

Relations between rules and the web of causation

When simple rules are identified, certain actions or rules might sound irrelevant. Now what makes a system complex? Simple rules, but not just a loose collection of rules. These rules have dependencies. Not every rule with every other rule, but most probably a lot. These relations build a web of causation. An unwanted outcome of a system will most probably rely on multiple factors that went wrong. Things don’t just happen.

Understand the action and reaction between rules! And understand the relations between rules before you remove one. It might cause havoc later down the line.

A simple example

Let’s simplify a form where you have to enter your address and merge the house number and the street name into one field. When the address label is printed later it uses an address validation service. That API expects street name and house number in separate fields. The number field is now gone or empty. So you have to somehow implement a function to split the field again in a way that makes sense.
This might have caused delay in delivering the next version, as you had to wait for the function in the address validation service to be adjusted. Or in a worse scenario your address validation failed too often and someone had to manually intervene with the system, causing a jam of orders. Or even worse, the address labels are now printed wrong and a certain percentage of your mail doesn’t reach its intended destination, which leads to unhappy customers, more work in customer service, and ultimately in loss of money. And that only because you wanted to make a form look a bit simpler.

Wrong assumptions

Understanding a complex system can be an overwhelming and time-consuming task. And while there are good heuristics that help us to understand things faster, we should still question those assumptions. Especially when certain outcomes of reality don’t match the working state of the mental model. Keep observing the system.

Complex Adaptive Systems

While this topic deserves several books on its own, I want to emphasize the dynamic part of systems. Systems evolve, adapt and adjust. Many systems seem to have a self-healing mode, which is simply driven by strong motivations within the system to survive in some way or the other. When you remove one element (a node, a rule, a relation) the system might be able to stabilize itself without “external” help into a state that you still observe as “healthy”. You could come to the conclusion, that the removal of the element didn’t cause any harm, because in your abstract view of the system you cannot observe any evidence against it.
In climate change discussions you will often hear about the tipping point. And that is when a system is not able to stabilize back in its former state without intensive external investment of energy and it will fall to a new, “lower” level, that it is able to sustain – maybe.

Be careful when removing for you obviously irrelevant elements of a system! You might not understand what just has happened. Remember the web of causation.

I could tell you several examples from science, work or the wood workshop. But for the sake of simplicity of this article I don’t.