mattfenwick

I think the word syntax is ambiguous. Is homoiconicity about concrete syntax? Abstract syntax? Both? Some other kind of syntax? Does parsing have something to do with homoiconicity?

I don't know the answers to these questions.

This is my understanding of homoiconicity. There's a good chance that I'm wrong. Comments and clarifications welcome!

-----

Is homoiconicity about concrete syntax? I think the answer is no , but I also think that this confuses lots of people because it's not explicitly stated.

Then is homoiconicity about abstract syntax? Let's say we have some arbitrary language, for which there's an eval function (input: abstract syntax tree, output: some data structure) whose job is to execute/evaluate a program in that language. Homoiconicity, then, is the case where the output data structure is also an abstract syntax tree, or conversely, that an abstract syntax tree is expressed in data structures of the language.

So for homoiconicity, the input and output of your eval function are of the same type (of course the type may be a complicated algebraic sum type etc.). Thus the input and output have the same domain. This means that you can take the output from eval, and throw it back into eval again without getting a type error.

If the input and output types of the eval function don't match, then the language isn't homoiconic.

Is this correct?

What I don't get is whether the eval function is available from inside or outside the language, or both.

I'm also unsure of whether homoiconicity is a property of a language, or of an implementation of a language.

My suggestion is to use "side effects" -- it seems to me that most people have an intuitive understanding of that term.

... but I may be wrong about that.

Sure, we can't rule out those possibilities ... but to me it just sounds like wild speculation.

Oops, instead of "reality" I meant "experimental observations". Good catch.

> You arbitrarily claim to be able to make accurate assertions of reality

Nope.

> while at the same time agreeing that the essence of reality is unknowable.

Yep. I guess? All I think is that it's okay if we don't know the true essence; science is still useful[0].

> Stop using the word good without making your case for righteousness.

Stop hijacking common words.

> You can't substitute it with practical or useful either, both infer benefit at the personal degree.

I don't know what that means.

> Just because an expectation proved to be useful once, remember that the past does not predict the future.

Depends what you mean by predict the future. Absolute truth? No. Useful[0] forecasts? Yes.

----

[0] dictionary.reference.com:

> useful: being of use or service; serving some purpose; advantageous, helpful, or of good effect

At no point did I touch on morality or ethics. I do not know why you think that I did so.

> I agree with you that experimentation is futile in the absence of ethics.

I do not agree with that.

--------

Dictionary.reference.com:

> useful: being of use or service; serving some purpose; advantageous, helpful, or of good effect

> good: having admirable, pleasing, superior, or positive qualities; not negative, bad or mediocre

I urge you to reevaluate your understanding of the words "good" and "useful". Don't you understand that I'm not talking about morality?

I'm not sure that I agree. I think that once you get into the realm of "absolute truth" (which is what I'm interpreting your post as saying -- apologies if I'm mistaken), you've left science behind. IMHO, science cannot (and does not aim to) deliver certain knowledge. Instead, it produces useful approximations.

An alternative viewpoint: everything's easy after it's been done, but it's hard up until then.

> after centuries of science as a profession, pretty much all the easy stuff has already been done.

But with the benefit of all that has already been done, shouldn't we be able to do things now that were previously impossible? In other words, "easy in 2014" != "easy in 1200".

> It's actually an argument for more cojones in science-- being willing to do bold stuff and explore "crazy" hypotheses.

Maybe doing whatever's easy is the most efficient (by time, money) way to make progress?

> If I say that an airplane functions on fairy dust and you make an argument about propulsion and lift, and you claim that my assertion is wrong because ...

The only reason "science" would claim that your assertion is wrong is if your explanation doesn't agree with reality.

However, if you collect a bunch of data that you say supports your theory, but your experimental technique or data analysis is not good, then it's perfectly reasonable to point out the problems and say that your result is not supported by your procedure. This is not the same as saying that your assertion is wrong.

Another important concept that you're missing is usefulness: your assertion is unlikely to be useful (this is totally different from whether it's correct). Let's say you want to ensure that planes don't fall out of the air: how would you take advantage of your assertion to do this? (My proxy for usefulness is falsifiability -- un-falsifiable theories often are useless)

> Philosophically speaking you can never verify anything through experimentation

This depends on what you mean by "verify". You seem to mean "prove that something is absolutely true".

That is not what I meant. I should have clarified that I meant something along the lines of "we've collected a bunch of data that don't contradict <something>".

I've edited my meaning into the post because I don't want my stupid equivocation to distract from more significant issues.

I do agree with you that science doesn't show causation, but I think your interpretation of science is incorrect:

> Therefore, all scientific analysis is unverifiable.

I disagree. It's verified by experiment. (Here I'm using "verify" to mean that contradictions have not (yet) been found by experimental observation).

> Knowledge of the world is completely unjustified.

I disagree. Again, it's justified by experiment. If that's not enough for you, too bad, because that's all we have.

> Our immense presumption of consistency in world phenomena when we really have no basis for asserting uniformity of nature.

I disagree. This is only asserted insofar as it's 1) useful, and 2) justified by observation.

I don't think you understand the significance of scientific results. They don't say "at a fundamental, irreducible level, this is how <some system> operates." Rather, they're saying something more along the lines of "as far as we can tell, this is a description of how <some system> operates, but it's entirely possible that we're wrong. However, we don't have any data that shows that we're wrong (yet), and because this description is useful for understanding <some system>, we're going to use it."

We don't need to know how a system works at a fundamental level, if understanding it at a less-detailed level is useful. I do agree with you that we can't determine "actual causality", but we don't need to, if we can instead find a bunch of really good correlations. The problem is that people find lots of crappy correlations, and/or can't tell the difference between good and crappy correlations.

> it's fair to say that experiment shows causation

No, that experiment doesn't show causation.

> I do think 'perhaps' we could use correlation more to assume causation. I think we are too cautious

Why do you think that? Is it because you think that science is moving too slowly (thus wasting time and money)? Is it because you think that good results are mistakenly discarded?

"using correlation more to assume causation" should produce results more quickly, both correct and incorrect. Do you understand the costs of incorrect results? First, incorrect results can have negative consequences for the consumers: for example, drugs that have harmful impacts and no beneficial ones for the people taking them (in addition to the cost of the drug, and the opportunity cost of not doing something else known to be beneficial). Second, time is wasted following up incorrect results and work based on incorrect results would be worthless. Third, the incorrect results would have to be overturned (and this communicated to everybody who had taken them as correct).

I think deciding whether to "use correlation more to assume causation" needs to take into account the costs and benefits of incorrect results, as well as the costs and benefits of correct results. Do you have any data on this? (unfortunately, I do not)

> certainly the haters always bring in correlation to stop science articles they think don't follow their beliefs

Can you support this accusation with examples of it happening to articles that actually do a good job of showing their result is something more than a correlation? Or are you saying that this happened to articles that just present a correlation?