Problem spaces can be simple, complicated, complex and chaotic (the Cynefin framework (Snowden & Boone, 2007)).
Simple
You can clearly identify actor, input and output
Complicated
Cause and effect are there, but you need expertise (or some analysis) to see them.
Complex
There are too many factors in play. Complex systems rely a lot on situational data for their outcome and are rarely (if ever) reproducible. That said, reason out the output afterwards. An example of a complex system are elections.
One can navigate these systems by tweaking aspects of them and seeing how it plays out (In many ways, it sounds a lot like machine learning).
Chaotic
Impossible to predict or understand an outcome. An eldritch horror of sorts...
Disorder is not knowing how to fit a problem space into one of the aforementioned categories.