I want to explore the notion of quantifying the amount of *succintness* a programming language provides.  That is, the amount a high-level language reduces the complex. 

This idea of "simplification" is a factor of **text-wise reduction** (fewer characters needed to express a complex concept, à la Algorithmic Information Theory) and another, less easy-to-quantify concept of **maintainability**. Fleshing out this latter concept, it is clear it has to do with how easily one can establish programmer consensus for the given task (i.e. how many programmers of the language would put it back the same way you've expressed it or otherwise agree on the best implementation for a given problem?).

I will define the "Kolmogorov Quotient" so that higher numbers for a given language denote a *reduction* in the complexity of solving the problem in the given language.

Once the basic premise and a methodology above is agreed to, it is only a matter of a rough constant of difference for any specific implementation. (That is, as long as the implementation is the same across all measurements, the number should be valid and comparable.) 

But it could go something like this: Pick a language "close to the machine", like C or Assembly, and measure the amount of bytes of machine code it used to implement a standard suite(*) of common, non-threaded programming tasks (base_language_count). Then code the exact same functionality in the language you are wanting to measure (without using external libraries) and count the number of bytes of source code (test_language_count).

KQuotient = base_language_count / test_language_count. 

This *should* always be greater than 1.0.

(*) "standard suite of common programming tasks...":  I see two main categories:

 1. Data-processing suite limited to simple text I/O *(computation towards the machine)*
 2. GUI suite *(computation towards the user)*