The Origin Of The Quantum, Part I: An Incomplete Phase Space Picture

In the last post, we have familiarized ourselves with some basic notions of algorithmic information theory. Most notably, we have seen how randomness emerges when formal systems or computers are pushed to the edges of incompleteness and uncomputability.
In this post, we'll take a look at what happens if we apply these results to the idea that, like computers or formal systems, the physical world is just another example of a universal system -- i.e. a system in which universal computation can be implemented (at least in the limit).
First, recall the idea that information enters the description of the physical world through viewing it as a question-answering process: any physical object can be uniquely identified by the properties it has (and those it doesn't have); any two physical objects that have all the same, and only the same, properties are indistinguishable, and thus identified. We can thus imagine any object as being described by the string of bits giving the answers to the set of questions 'Does the object have property x?' for all properties x; note that absent an enumeration of all possible properties an object may have, this is a rather ill-defined set, but it'll serve as a conceptual guide.
In particular, this means that we can view any 'large' object as being composed of a certain number of 'microscopic', elementary objects, which are those systems that are completely described by the presence or absence of one single property, that may be in either of two states -- having or not having that particular property. Such a system might, for instance, be a ball that may be either red or green, or, perhaps more to the point, either red or not-red. These are the systems that can be used to represent exactly one bit of information, say red = 1, not-red = 0. Call such a system a two-level system or, for short, and putting up with a little ontological inaccuracy, simply a bit.

Information through the Lens of Computation: Algorithmic Information Theory

Up to now, previous discussions about information and computation have been rather informal -- I have neglected to give detailed mathematical introductions to concepts like Shannon entropy, Turing universality, codes etc. for the simple reason that for our purposes, a qualitative understanding has been fully sufficient.
However, if we want to make any quantitative statements about the notions of information and computation, it will benefit us to take another look at the subject in a slightly more formal context.
To this end, the most efficient framework seems to me to be that of algorithmic information theory, introduced and developed mainly by the mathematicians Ray Solomonoff, Andrey Kolmogorov, and Gregory Chaitin in the 1960s. According to Chaitin, AIT is "the result of putting Shannon's information theory and Turing's computability theory into a cocktail shaker and shaking vigorously"; as such, it will aid us in achieving a better understanding, in a quantitative way, of both subjects.
In a way, AIT is concerned with a concrete realisation of the somewhat abstract-seeming notion of information or information content within the realm of computation. To this end, its central concept is the so-called Kolmogorov complexity, which, loosely speaking, provides a measure of the information content of an object -- say, a string of binary digits -- by considering the shortest program, on a given computer, that outputs this object. This might seem at first like a rather arbitrary definition: different computers generally will have different programs, of different lengths, that generate the string, leading to different complexities for one and the same object. But it turns out that despite this ambiguity, Kolmogorov complexity is useful independent of the concrete computational implementation it is based on.