[ICBTheory]

4. On “This is just the No Free Lunch Theorem again”

Well … not quite. The No Free Lunch theorem says no optimizer is universally better across all functions. That’s an averaging result.

But this paper is not at all about average-case optimization. It’s about specific classes of problems—social ambiguity, paradigm shifts, semantic recursion—where: a)The tail exponent alpha is = or < 1 —>no mean exists, b) Kolmogorov complexity is incompressible, and c) the symbol space lacks the needed abstraction

In these spaces, learning collapses not due to lack of training, but due to structural divergence. Entropy grows with depth. More data doesn’t help. It makes it worse.

That is what “IOpenER” means: Information Opens, Entropy Rises.

It is NOT a theorem about COST… rather a structure about meaning. What exactly is so hard to understand about this?