> This is not sociology, it is mathematics.  Transforming one set of
> binary states to another set of binary states.  Yes, there a number of
> different methods for doing a given a transformation, but those are
> all the same kind of mathematics and understanding the tradeoffs
> between those methods is also the same kind of mathematics.  And
> choosing a method is *not* arbitrary -- see the part about "tradeoffs".

Right, but an important part of the design of any programming language
is how easy it will be for other programmers to use.  Otherwise, we'd
still be using assembly language.  Designing a language that is easy
for others to use is much more of an art than a science.

> Mathematics does not work differently based on cultural context.
> There is not a lot of room for whimsy if economical results matter.
Right, but different cultures understand mathematics differently.  For
example, the romans had a really strange and inefficient numerical
system.  Despite the fact that they were the economic power of their
day, they still didn't abandon an inefficient system when other more
efficient systems existed elsewhere.  There could be a more efficient,
easier to understand programming paradigm that people aren't adopting
for the same reasons the romans stuck with their numerical system.





> Or maybe after she has actually studied theoretical computer science,
> this female minority understands the subject well enough to realize
> that there is no such thing as this mythical culturally sensitive
> programming language so many people are pining for.
Where is your evidence of this?  What did I miss out in my theoretical
computer science class?


> This is a recurring theme, that Holy Grail programming language that
> requires no knowledge of computer science to use well.  These
> arguments are based entirely the desire to create a language that can
> turn a thoroughly ambiguous and contradictory specification into a
> perfectly working program, without grokking that programming languages
> are *by necessity* non-ambiguous and require consistent constraints --
> explicit and implicit -- if you want a useful result.

No, I am not aware of anybody that wants to create such a language.
We just want languages that are better than the ones that exist today,
there is a lot of room for improvement.


> Your above argument is handwaving.  There was a reason I was looking
> for a specific example -- a minority friendly lambda calculus language
> -- because I've heard your claim made repeatedly for many, many years
> and have yet to see a single shred of evidence that such a language
> would not look virtually identical to one of the thousands of existing
> languages.

The absence of evidence is not the evidence of absence.

>We have many dozens of languages that were expressly
> designed to make the underlying concepts as easy to grasp as possible
> for a non-geek.
Ahh, that's the key word "We".  Have you done any actual field work to
see what sorts of difficulties and misunderstandings people have in
understanding your languages?


> Uh, what kind of programming do you do that you would assume that
> almost the entire software universe is working in some kind of linear
> scripting environment?

I don't, I just don't think it's necessary to construct
multi-dimensional graphs in my head.  Perhaps when I am programming I
am doing something equivalent, but by making such a claim you are only
reinforcing my point... there are many different ways to program and
claiming that one must do a certain thing to program only prevents
people from entering the field.

> What on earth do you think code is?  The only difference between code
> and people-talk is that code requires precision and non-ambiguity
> since incorrect results are generally considered unacceptable.
Ok, so code is communication between human and computer, I know that.
But usually when somebody says communication I assume they mean
communication with a human.


> Because I've never seen anyone learn it, ever; experience changes a
> lot, but the ability to handle complex abstract models doesn't seem
> to.  I've known many software engineers with careers that span decades
> and bucketloads of experience that really don't grok graphs beyond a
> certain complexity
Do you have any objective measures?  Can you mathematically describe
the degree of complexity of graphs or models that certain people can't
understand?


>-- it is a bit like you reach a certain description
> threshold where pushing more bits into the model makes other bits fall
> out.  That threshold varies from individual to individual, and it is
> difficult to not notice that the correlation between really bright
> software designers and people who are quite apparently able to
> atypically work with complex models in their heads.  I've worked on
> more than one software project where there were members of the team
> that quite obviously never grokked the dynamic characteristics of a
> system even after many months of intimate experience with it, whereas
> others grokked it quickly.  It had nothing to do with education or
> experience or even desire to learn in many cases.

Maybe it had to do with how similar their brains were to your brains?
Perhaps if you thought of programming in terms other than
multi-dimensional graphs you'd be able to explain it better to people
who thought differently.


> There is a lot of anecdotal and some literature evidence for this even
> if you restrict yourself to the pool of pasty white male software
> geeks.  It is also probably why software has the unique feature that
> half the really brilliant people working in it do not come from a
> traditional CS background; it was not education per se that made them
> great.  The noted correlations with neurological structures is likely
> not coincidental either.
What correlations with neurological structures?

>
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-- 
Robin Gane-McCalla
YIM: Robin_Ganemccalla
AIM: Robinganemccalla

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