I mean, we can definitively talk about simplicity/complexity in a fairly easy way when it comes to mathematical structures or data structures in my opinion.
For instance, a binary tree that contains just a root node is clearly simpler than a binary tree with three nodes, if we take "simple" to mean "with less parts" and complex to mean "with more parts". Similarly, a "molecule" is more complex than an "atom".
This is a useful definition, I think, because when we write computer programs they always written in some programming language, with a syntax that yields some kind of abstract tree, so ultimately we'll always have _some_ kind of graph-like nature to the computer program, both syntactically and semantically, and surely graphs also permit the same kind of complexity metrics.
I'm not saying measuring the number of nodes is _the_ way of getting at complexity, I'm just pointing out that there's no real difficulty in defining it.
Complexity means more stuff, and we simply take it as a premise that we can only fit so much stuff in our head at the same time.
For instance, a binary tree that contains just a root node is clearly simpler than a binary tree with three nodes, if we take "simple" to mean "with less parts" and complex to mean "with more parts". Similarly, a "molecule" is more complex than an "atom".
This is a useful definition, I think, because when we write computer programs they always written in some programming language, with a syntax that yields some kind of abstract tree, so ultimately we'll always have _some_ kind of graph-like nature to the computer program, both syntactically and semantically, and surely graphs also permit the same kind of complexity metrics.
I'm not saying measuring the number of nodes is _the_ way of getting at complexity, I'm just pointing out that there's no real difficulty in defining it.
Complexity means more stuff, and we simply take it as a premise that we can only fit so much stuff in our head at the same time.