Simplifying system’s problems increase the system complexity​

Most of us are following the analytical approach when we face complex problems. “Analytical approach” is the use of an appropriate process to break a problem down into the smaller pieces necessary to solve it. Each piece becomes a smaller and easier problem to solve (

While this method might be beneficial for specific scenarios, it is counter-effective when it used for systems. Regretfully, the analytic approach is used a lot to resolve complex systems issues and as a result, increase the complexity and problems.

A system is a collection of parts that interact with each other to reach a common purpose. As the system has more elements and more interaction between the components, the complexity level of a system increases. Parts of a system can be (and usually are) subsystems that have their purpose, parts, and interactions. As more parts of the systems are subsystems, the complexity of a system is increasing exponentially.

Systems are all around us: the economy, our body, weather, and companies are just several examples of systems. I will focus only on companies in this post, although everything that I will discuss applies to any other systems.

As I mentioned above, there is a direct correlation between the system complexity and the number of elements in the system and the interactions between them. A paradox exists from both short-term and long-term perspective when we are using analytical thinking. From a short-term standpoint: when we break a complex problem into smaller parts, we increase (short term) the preserved simplicity of the (System) problem, which decreases the preserved complexity. As a result, we encourage to break the issue to even smaller parts. This causal loop motivates us to break a problem into smaller and smaller pieces.

Following the causality of system complexity and from a long-term perspective, as we are breaking a system into more components, we increase the number of the system parts and as a result of the number of interactions, it’s just taking time to see the bad fruits of the long-term loop (perceptual delay). So the short-term illusion of preserved simplicity creates a long-term reinforcing loop that increases system complexity. When we see that the system looks more complex, we are trying to break it into more pieces (following analytical thinking). Those two loops create infinite loops that increase complexity and problems, instead of resolving issues. You can see the two reinforcing causal loops in the diagram below.


Those two short term and long term reinforcing loops increasing the system complexity and creating new problems and/or increasing the current one. No wonder why we have a systematic issue around resolving complex system problems.

So, what is the solution? Instead of running away from complexity, we need to learn how complexity is working and use the right tools to understand the complexity and to fix the purpose, structure of the parts and the interactions of the parts. One way to do it is to use system thinking. The casual loops I used in this post is one of the available tools provided by system thinking. We need to change our thinking paradigm. Instead of fear complexity and chaos, we need to understand them and leverage them to our own benefit.


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