Talking about problems

This article is part of our upcoming project Words. Stay tuned for more content! Co-authored by Max Kinninger




Everyone got problems. Most people react to their problems on a range from slightly unpleasant to a full blown life crisis. That is mainly due to the fact that we are mostly intimidated and overstrained by them. But with a little help, every problem may be fixed. To help you with this, the terminology and the kinds of problems are further discussed below.


When considering problems, there are generally speaking three attributes a problem can have: complicated, complex and difficult. These attributes are not mutually exclusive, therefore a problem can have every combination of these attributes.


Let’s start with complicated problems. Complicated means the context of the problem consists of a high number of individual pieces that are stringed together in a linear way. The best example is a clockwork. Many small gear-wheels work together. You can predict that the first wheel will spin the second and so on, but the sheer amount of wheels makes it hard to find the reason the clock stopped working in the first place. An overwhelming number of components results in complicacy.

Whenever you deal with people, the linearity of interaction comes crashing down. It is not possible to predict a human interaction since people are not acting consistent. Therefore whenever we are confronted with a problem concerning the interaction between people, like a fight, we call it complex. Complexity results from non-linear interaction between components.

Finally, there are difficult problems. Difficult means that we are clueless about how to solve the problem. We have no starting point for a solution. The best example is the nine-dots puzzle. You have to connect all 9 points in a 3x3 Matrix by using just 4 lines. It is impossible to do so while staying inside the Matrix, you have to literally think outside the box. By the way, that’s how the phrase got coined. Difficulties could arise if the wrong premises are assumed (staying inside the box). On the other hand the lack of premises could lead to difficulties as well: Ask someone without physical knowledge how magnets work and they will find it difficult to do so. To conclude, difficulties result from an absence of premises or the presence of wrong ones.


Let’s move on to the scaling of these problem attributes: The question at hand is under which circumstances a problem can be labeled complicated, complex or difficult and if they have gradients.

Complexity is an absolute attribute. A problem can be labeled complex if there are two components interacting in a non-linear way. The degree of complexity depends on the number of components in the system, their structure and their intensity of interaction.

Complicacy in contrast is a partially relative problem attribute: It depends on the training and capacity of the individual working memory. But since humans are indeed humans, you can be fairly certain that there is a bunch of problems which are complicated for everyone. This means an absolute border of complicacy can be postulated, but this border may be lower, depending on the individual. The degree of complicacy depends on the number of components at hand, their kind of interaction and the individual ability and training to process the given problematic situation.

Difficulty is the attribute with the most individual variation. Since it depends on the individual previous experience and the resulting personal premises concerning the given situation (short version: subjectivism), difficulties can arise from a variety of factors. The easy part is to label a current problem: It can be either difficult or not difficult, depending on whether we have an idea on how to solve the problem. Retrospectively the difficulty could be graded by the amount of time used to solve the problem, but this indicator is also highly individual and of considerable methodological flaws.


To understand the usefulness of this differentiation, we have to go on and talk about methods to overcome these different problems. This is where this concept becomes practically applicable: An understanding of different kinds of problems as well as their most fitting solution methods allows for faster solving. Understand it as a toolkit to use in your everyday life.

To address complicated problems, you should conduct an analysis. Analysing things means breaking the problematic condition into it’s components and describe their linear interactions one by one. An analysis always asks the question “What is happening?” Go from there, find the failing part and fix it.

Difficult problems know two solution paths: The first one is the transfer. A difficult problem can be addressed by transferring a known solution method from a different but similar context to the current problem. The solution performance of a transfer depends on the similarity of the situation it was taken from and the situation it is applied to. A more thorough solution method for difficult problems is the diagnosis. A diagnosis is an hypothesis about the cause of a problem. This hypothesis allows to fight the problem at its roots. A diagnosis therefore asks the question “Why is it happening?” Try to understand what premise causes your troubling symptoms and adjust your premises.

Solving complex problems is the hardest part: Professor Peter Kruse, a german professor of psychology and a famous systems thinker, said there were four ways to deal with complexity: Trial and error, ignoring complexity, simplifying/over-rationalizing complexity and intuition. The last one is what you should go to as the other ones are definitely not fitted to address a complex problem. Try to head on complex problems with your gut, it will make better judgments than your brain.


To wrap it up, the understanding of your specific problem will give you guidance on how to solve it. There goes an old saying: For someone with a hammer, every problem is a nail. But for someone with a toolbox, a problem can be solved with a screwdriver, a wrench or a saw.

Tristan Poetzsch

Computer aided cognition and AI specialist, currently working at Nexgen Business Consultants.


Berlin Lab

Berlin, Germany
  032 229 340 927
  This email address is being protected from spambots. You need JavaScript enabled to view it.

Wuerzburg Lab

Würzburg, Germany
  032 229 340 927
  This email address is being protected from spambots. You need JavaScript enabled to view it.