[pron]

> is either going to be in terms of programming language theory

Programming language theory does not study "advantages;" it's simply not a question it asks, let alone answers. The theory is concerned with what properties certain formal systems have. It cannot, nor does it attempt, to assign those properties a value, just as mathematics does not ask or answer whether prime numbers are better than composite numbers.

> None of these things is straightforward to control for, and I'd be extremely skeptical of any quantifiable metric someone shows me that purports to show clear wins in real-world software development based on language paradigm of all things.

Maybe, but that doesn't matter. You cannot claim that you're providing a significant benefit and in the same breath say that it isn't measurable. An advantage is either big or not measurable; it can't be both. If you say that the benefit of the language is offset by the bad tooling, then you're not really providing a big benefit. If and when the tooling catches up, then it's time to evaluate.

But maybe not. I find it very dubious that significant differences are not measurable in an environment with such strong selective pressures for two reasons: 1. it doesn't make sense from a theoretical perspective -- adaptive traits should be detected in a selective environment, and 2. it doesn't fit with observed reality. We observe that technologies that truly provide an adaptive benefit are adopted at a pace commensurate with their relative adaptability; often practically overnight. The simplest explanation from both theory and practice to why a technology does not show a high adoption rate is that its adaptive benefit is small at best.

[crimsonalucard]

>Programming language theory does not study "advantages;" it's simply not a question it asks, let alone answers. The theory is concerned with what properties certain formal systems have. It cannot, nor does it attempt, to assign those properties a value, just as mathematics does not ask or answer whether prime numbers are better than composite numbers.

I think we can go deeper than this. There are properties of well designed programs that can be measured to be numerically higher or lower than poorly designed programs. Under this mentality "better" is simply a word with no meaning that is describing a number. It is the human that has the opinion that the higher (or lower) number is a "good design."

The question is what is that number and how do you measure it? For example one number off the top of my head: lines of text. Another better number is the amount of functions. Both of these numbers have flaws so maybe a better number is given a (high level language) and a (program written in assembly language); what is the largest number of high level language primitives you can use to recompose an identical program?

(high level language primitives)/(low level language primitives)

As the ratio approaches 1 we are achieving maximum flexibility as the high level language is injective to the low level primitives. As the ratio approaches zero we are reducing complexity at the cost of flexibility (we reason about less primitives). If the ratio exceeds one then we are creating excess primitives.

Maybe the better designed language/paradigm can has primitives that can used to drive that ratio back and forth from 0 to 1. A poorly designed language is one with a ratio of 4.5 or 0.0001.

So something more advanced but along the lines of this rudimentary and rough outline is certainly possible in my mind.

>But maybe not. I find it very dubious that significant differences are not measurable in an environment with such strong selective pressures for two reasons: 1. it doesn't make sense from a theoretical perspective -- adaptive traits should be detected in a selective environment, and 2. it doesn't fit with observed reality. We observe that technologies that truly provide an adaptive benefit are adopted at a pace commensurate with their relative adaptability; often practically overnight. The simplest explanation from both theory and practice to why a technology does not show a high adoption rate is that its adaptive benefit is small at best.

Yeah I agree. Additionally we're not dealing with the real world here with billions of variables. This isn't a computer vision problem. Assembly language, FP and OOP have a countable amount of primitives. It is amenable to theory and measurement.