I once saw a visualization that basically partitioned decisions on a 2D plane. From that perspective, decision trees might just be a fancy word for kD-Trees partitioning the possibility space and attaching an action to the volumes.

Given that assumption, the nebulous decision making could stem from expert's decisions being more nuanced in the granularity of the surface separating 2 distinct actions. It might be a rough technique, but nonetheless it should be able to lead to some pretty good approximations.

You have this thing a little backwards that it is unintentionally hilarious.

Decision trees predate KD trees by a decade.

Both use recursive partitioning of function domain a fundamental and an old idea.