# Algorithmic Information Theory

The field of algorithmic
information theory studies the computational complexity of
algorithms, where the resource being considered is the *length of the
program*. This is a good model of the scientific process: we want a
theory to *explain* our observations, and hence we want a
*program* which takes fewer bits to store than those
observations, but which will produce them as output when executed.
Gregory Chaitin claims that this compression *is* explanation: if
a program is as long as its output, it doesn’t explain anything.

When I came across that claim I immediately thought of the case where some long and tedious program is written to calculate a number, or even just a single bit true/false answer to a statement which is full of arbitrary or “random” parameters (i.e. difficult to compress). I’m thinking of examples like the software Appel and Haken used in their original proof of the four colour theorem.

I think there are a couple of ways to think about this:

The algorithmic complexity Chaitin is discussing assumes “logical omniscience”, i.e. when we read some logical statement/line of code (or add some bit to a program/theory) we immediately know all of the consequences. In such a setting, we never have to run a computer program to see what it produces: we know as soon as we’ve read the code. An compiler with such omniscience could optimise a long and tedious calculation by replacing it with the result, e.g. “4”, and hence prevent programs which are larger than their output. (Note that this isn’t actually possible in general, since we’d essentially be running the program at compile time, and we’d run into the halting problem, turning our compiler into a partial function).

In such cases the programs would actually be the same as their outputs, and hence don’t offer any “explanation” (compression), as Chaitin puts it. Mathematicians might agree with this, since it’s a common complaint that having an automated theorem prover tell us “true”/“false” isn’t considered very useful, even if it’s known to be correct (e.g. if we’ve verified the software).

This leads into the second perspective: that of “boolean blindness”. If we consider such programs from a Curry-Howard point of view, the output type corresponds to a theorem of which the program is a proof. If all we’re outputting is a boolean value, then the corresponding theorem can be thought of as “there exists a boolean”. If we think of our program as a proof of this statement, it’s massively over-complicated; we could just write “true” or “false” (or “0”/“1”, etc.), and that would be just as good a proof that a boolean exists.

The “blindness” is that trivial values like “true”, “false”, “0”, “1”, “2”, etc. tell us nothing about their intended meaning: the language/representation makes no distinction between the value of “1 < 5” and the value of “solve(fermatsLastTheorem)”.

We can change our program’s type, such that it encodes the problem we’re trying to solve, e.g. something like (in pseudo-Coq):

`forall (m : Map), exists(c : Colouring(m) | length(colours(c)) <= 4)`

This forces our program to output a more complicated value than simple “true” or “false” (if this were Coq, we’d have to construct a function from Maps to pairs, where the first element of the pair is a colouring of the given map, and the second element of the pair is a Natural number equal to the difference between the number of colours used in that colouring and 4).

This brings us back into the realm of interesting, non-trivial comparisons between program length and output length. There’s also an interesting distinction to be made between “proof objects” (programs which represent proofs of our theorem) and programs for constructing such proof objects. To be comparable with Chaitin, the language for constructing proofs should be turing-complete (e.g. like Coq’s “Ltac”); yet we might want to avoid having our proof object’s language be turing-complete, since that would permit unsound proofs.

I think it’s this distinction between turing-complete and non-turing-complete languages that is the key to Chaitin’s “explanations” (i.e. compression). An “elegant” (shortest) program in the proof-constructing language cannot be longer than an elegant proof-object, since we can just return that elegant proof object verbatim: the program will be as long as its output, and we have “explained” nothing, just like optimising a boolean program down to “true”.

Yet an elegant proof-constructing program can be shorter than an elegant proof-object! We can’t make the proof-object language total by rejecting only those programs which don’t halt, since the halting problem is undecidable; our criteria for accepting/rejecting must necessarily allow too many or too few programs. If we allow too many, we’re allowing invalid proofs, which we don’t want. We must choose to allow too few, and hence some perfectly valid proofs will get rejected.

By having our proof-constructing language be turing-complete, we don’t have to reject any valid proofs, and hence we can write proof-constructing programs which are not valid proof-objects. Some of those programs may be smaller than the smallest proof-object, and hence they “explain” part of the proof-object. For example, there may be some repetitive structure in the proof-object which the proof-object language does not allow us to optimise away. The proof-constructing language has no such restriction, allowing us to write a smaller program which iteratively builds up the proof-object.