However, I continue to disagree. In the example of an “instruction” which you gave, the program was using a location being “pointed at” for accumulation. In a Universal Turing Machine, it would read through. Even though both could perform the same function, a RAM machine is not the same for figuring Kolmogorov Complexity.
In other words, before introducing Kolmogorov Complexity – the computer/machine we are speaking of must be normalized or else it is apples and oranges. If a RAM machine can do it in one instruction because it is hard-wired, but a Turing machine requires 1,000 instructions – then the Kolmogorov Complexity of the result will depend on whether we are speaking of the “instruction” of the RAM computer or the equivalent Universal Turing Machine.
Also, it occurs to me we ought to be looking at biological autonomous self-organizing complexity with the same kind of “lens” that we use in quantum information theory – e.g. introduce von Neumann entropy for computational density matrices. IMHO, the variance in nature may be closer to quantum states than classical physics.
Not if we are talking about finite systems (and we are); for these the intrinsic KC of the entire system is always going to be the same. See #507 (I think) about this. As I state in my other post about this specific point, this is new and also unpublished -- it is actually one of my personal (and relatively minor) contributions to the field. One of the things high on my TODO list is to publish a comprehensive tome on finite computational theory, particularly with respect to how it differs from traditional computational theory.