  Information functionals allow to quantify the degree of randomness of a given
probability distribution, either absolutely (through min/max entropy
principles) or relative to a prescribed reference one. Our primary aim is to
analyze the "minimum information" assumption, which is a classic concept (R.
Balian, 1968) in the random matrix theory. We put special emphasis on generic
level (eigenvalue) spacing distributions and the degree of their randomness, or
alternatively - information/organization deficit.
