What does good complicated complicated?

Complexity means unpredictability

The situation is different when it comes to complexity. It can be characterized by the following features:

  • The system is influenced by at least two or more factors. The exact number of factors is unknown
  • It is not known exactly how much and when each factor will affect the system
  • The factors can interact with one another - it is not known exactly whether they do this and to what extent

Here, too, a graphic to illustrate these relationships: You have factors that influence the system again. Instead of the five numbered factors, two are shown here with question marks that are not known.

The action arrows are again shown in orange-red. Here the arrows not only affect the system, but (possibly) each other as well. The strong arrows are definite and known interactions, the light orange arrows are not known and / or cannot be foreseen.

One can see particularly well here: Although the majority of the factors are known (namely 3 out of 5), the majority of the effects between the factors and on the system are unknown or cannot be assessed (namely 6 out of 10 arrows).

In doing so, I have neglected in the graphic that not only the neighboring factors in the graphic interact, but e.g. factor 1 and factor 3 also have an interaction. Only then would the graphics have become very confusing.

Identification features and examples for complex systems

As in the manual above, I also have a distinguishing feature here when it comes to a complex system: Whenever you could write a guide instead of a manual, the probability is high that the process is complex.

By advisor I mean: try strategy Y for matter X. If that doesn't help, try Strategy Z and so on.
A few examples of complex systems: the human body, the weather, road traffic or financial crises.

In every system there are actors that we either do not know, whose influence we cannot assess, or even both.

Complexity in the smartphone

A ubiquitous example of complex processes are smartphones and the apps installed on them. In the early years of software development, programs were written autonomously. There was little or no interaction or interfaces with other programs. That made the system manageable - at best, complicated.
With the increasing desire to network software applications, the complexity increased. When programming, interactions between the programs had to be considered. However, it was still known which software interacted with another and in what form.
How many apps do you have on your smartphone? You can now choose from thousands of applications and they are installed in seconds. It is now impossible for the programmer of an app to know which programs each user has installed and is using on the mobile device. It cannot be predicted which errors can occur when the programs are used in parallel. The software world has changed from a complicated to a complex system.

Strategies for dealing with complex issues

The first step to more successfully dealing with complex issues is to realize that we cannot control them. In some ways they are unpredictable. That is not to say that we cannot make it a little more manageable. However, you should realize that you can never completely penetrate and control such systems.

Another strategy is that you shouldn't rely too heavily on numbers. Rather, let your common sense work and try to reduce the complexity of a system by temporarily excluding factors and effects from your consideration that may have only a very minor influence.

You can then work with the remaining factors, define measures to improve a situation and observe the results.

Another strategy is to change your planning horizon. As soon as the systems are complex, it makes little sense to set up long-term budget figures. In such cases, reduce the planning horizon and incorporate more frequent planning rounds.

Let's take the example of the weather again: We only know relatively rough relationships and can therefore only make short-term accurate forecasts about the weather in a defined region. To make a statement today about whether we will have a white Christmas next year is not feasible.

Even the prediction of how high the risk of rain is on a day is based on current measurements and statistical information about the weather on days with similar measured values. It has little to do with control.

The natural enemy of intricacy and complexity: simplicity

The opposite of what we perceive to be complicated and / or complex is simplicity. With the book author Dr. Michael Hartschen, I talked about exactly this topic in detail in podcast episode 004. Please listen to it, it's worth it!

In quality management, we do particularly well to recognize the difference and come up with the simplest possible solutions. This is because an increasing part of the work in QM consists of reducing complexity so that we can even work out solutions.

Conclusion - the difference between "complicated" and "complex" is important!

After reading the above, you probably realize that something can either be complicated or complex. But never both.

In order to deal with systems correctly, the distinction between complex and complicated issues should not be neglected. Depending on which of the two terms is currently applicable, we will have little or no success with the strategies for the other term.

If you want, observe in the near future whether you hear the words “complexity”, “complex”, “complicated” or “complicated” in your environment or even use them yourself. Then please think about whether the term fits the particular situation to which it is being applied.

I am curious if you will notice anything. Then send me an email.

The following posts go well with the topic: