1. I have a sister who is a nurse. In training she was told that every year about 30% of what she was taught would be revised and need to be relearned. She tells me that she didn't believe it then, but she does now since that is about what has happened since. So the problem of knowledge going stale does not just happen in computers.
2. I have always had a pretty good ability to pick factoids up. I wasn't always able to judge their accuracy. I have a brother who took it on himself to fix that, any time I repeated something he would make me figure out where I heard that and say whether I think that is a reliable source. It wasn't any fun, but it became a habit that has served me well. So the next time I spout some tidbit, ask me where I learned it, and when I respond, imagine me being lectured by my brother, "Now, Benny, you can't just believe everything you hear..."
3. The need to be able to accept ongoing feedback reminds me of what I was getting at in What you refuse to see, is your worst trap.
4. I hadn't realized that I had raised the point which started your post, but if you wish to credit me with good thoughts you had while reading something I wrote... ;-)

I've evolved a few simple rules:

• welcome mistakes and don't try to pretend they didn't happen - you learn more that way, and leave the bad thought process open to others to see the assumptions you made :)
• always be paranoid that there's a better/more secure/more efficient way of doing something
• accept that this is not the end of the world if you can still get the job done (you can always apply new stuff learned at a later date)

My general philosophy of coding is that you're doing well as a programmer if you know what question to ask. By understanding the question, you can work out the answer. It's when you don't know the question that things go wrong.

Basically, BrowserUK, I think you're suffering from programmers' paranoia. It's perfectly normal and I'm sure you'll feel better in the morning :)

.02

cLive ;-)

So, everything I know is a lie. I should always be prepared to eject any knowledge I have in light of some abstraction that covers more of the territory. It's all models, but some models are better than others.

-- Randal L. Schwartz, Perl hacker
Be sure to read my standard disclaimer if this is a reply.

Every abstraction that is not a tautology contains some lie, and is therefore only applicable to some subset of all cases.

Such as..."all premises, major or minor, involved in the construction of a syllogism are lies".

Hmmm. Granted, deductive reasoning is only a subset of logic.

:)

Matt

And logic is only a subset of real life ;-)

As you figured out, your correction was entirely proper and helpful. I dwimmed and was lucky that my shiny new perl dwimmed right along. I was only dimly aware of the bug, and didn't think of it in that note.

I'm educated as a scientist, and that helps me know that what I think must be tested. What I know must be questioned. To progress with all that doubt, I need to judge what principles are better than others. And I could be wrong. Anybody can tell me so, and if they respect the same values I have to listen.

Give me that over immutable truth, any day!

After Compline,
Zaxo

I find out-of-datedness less of a problem, as it will give you a sense of history in the end. Which will hopefully make you realize that all those nice things in Open Source didn't get there by itself, but were the result of hard work of countless, and often nameless, contributors. As long as it is clear which knowledge can be applied where (as in: in which version), I don't see a problem.

It will also make you realize that there is no "definitive" truth in programming (as anywhere else in the world for that matter). And that you should always verify. "Trust, but verify". There are even <plug>conferences</plug> about this.

Personally, I find that the combination of my 25 years experience in programming, combined with my programmers' laziness, tends to trip me up when I start making assumptions. Assumptions based on my experience, of course, but not necessarily having any foundation in "current" reality anymore. In that manner, I find the openness of Perl Monks, and the willingness to correct (in my case, mistakenly perceived) authority, refreshing.

So I can only concur with BrowserUk: "The only way I am ever going to know if what I think I know is correct is if people who know better correct me when I am wrong.

So please, correct me. I welcome your input. I may not directly accept it at face value, but if I don't, I'm not saying that you are wrong, I'm only saying that I am not sure that you are right and I would like further input to allow me to reach that state of grace.".

Liz

++ to you, BrowserUK and welcome to Epistemology Monks! ;)

There are a few groundrules here:

• You can Super Search, but keep in mind that knowledge is constantly growing and what may be true today is not necessarily true next week. Anyway we don't know if anything else really exists anyway, so what's the point, really?
• It's not so much about what you know, but rather about your basic ability to acquire new knowledge and apply it. (I see some others have mentioned this.) Being intelligent helps, but also conditioning one's mind to think efficiently plays a huge role.
• It follows that Perl Monks (our sister site) doesn't just expose people to things they don't know, but also teaches them to think in different ways in order to solve a problem. (map, baby! That's all I have to say.)
• Hedge a lot. People who are used to participating in scientific/academic discourses tend to qualify the things they say a lot, using "academic caution"^*. Academic caution is what results from knowing that you know something about something, but still knowing that your knowledge is potentially incomplete, so you go up one level of abstraction and say that the work reflects general principles, even though the experimental data used are quite case-specific.
• In Perl Monks, the knowledge bit seems to flow freely. Most of the hedging done is social hedging---trying not to hurt someone's feelings. That is very important because people won't ask questions and learn anything if they feel they are being attacked. (Just adding my voice to your last couple of points.)

OK, all jokes aside, I also feel these things are important, so thanks for the post.

Cheers,

--
Allolex

* Academics can be vicious.

You can 'correct' your own opinions, but only in the light of new knowledge or clarification of existing knowledge, but only if you engage in discourse. There are very few that can arrive at new knowledge, conclusions or opinions on the basis of study or thought in isolation. Isolation engenders dogma and extremism.

Go away. I'm thinking.

;)

I'm presuming that by 'discourse' you also mean reading and scholarly study (not that direct interaction with others is equivalent...but I'd hate to exclude one over the other).

Perhaps a decent analogy is between knowledge and a field or crop -- the seeds must be sown, the soil must be fertilized, the plants must be tended...and eventually harvested. Repeat.

On a more traditional note, one phrase I appreciate and embrace is "the more you learn, the more you realize how little you know".

To coin my own paraphrase:

You can't outrun the horizon.

Something to ponder unless you're a Flat-Earther.

Cheers,
Matt

The "know" has two levels (for which I lack good terminologies):

• abstraction, theory ("black box"), model, attitude, perception, etc. and
• data (input), information, fact (instance of abstraction), etc.

People had had very good and pretty correct and accurate astronomical data for a long while even during the ancient times but their theories and perceptions about the universe were still "incorrect" (incorrect by today's standard).

In programming if you get some factual, syntactic or logical things wrong, your program may not even run. But if you got your model or design wrong, it might take a while before you'd figure out. If the goal or business criteria for a program are fuzzy, you might hardly know whether the program itself is correct or not.

Attitude and perception are hard to change. Maybe that's partly why major theories and models propagate slowly (a macro stability, so to speak), albeit data and facts kept pouring in (a micro race). Galois theory in math, quantum mechanics in physics, input-output analysis in economics, for instance, they all took decades to be generally accepted. Once accepted, the (micro) race to applying them began.

Sometimes I feel a CS student learnt so much about algorithms and stuff, but not enough about "analysis" or problem modeling, which is partly art, partly science. A manifestation would be like, someone could write a fairly efficient query but fail to design an efficient and correct database structure that effectively solves the real life problem.

It is like being right at the micro level but wrong at the macro level.

...People had had very good and pretty correct and accurate astronomical data for a long while even during the ancient times but their theories and perceptions about the universe were still "incorrect" (incorrect by today's standard)...

To further humble ourselves: today's "standard" will be incorrect/corrected tomorrow. Check out: Higgs Field and realize that the concept of "ether" may not have been that wrong after all. ;-)

Liz

This is certainly inline with "You don't know even what you don't know". ie.. You are simply not aware of the other facts that can have influence in this matter, forget about knowing its influence in the matter.

artist.

This was very thought provoking read, and thought provocation doesn't occur with me that often, so thanks and ++ to You Browser_uk sir.

There are no scientific "laws". Scientific laws are just statements that science makes that seem to agree with our observations. Because we have accumulated a vast amount of observations about, say, how the pressure of a gas relates to it's volume, we get Boyle's Law. But it's not a law ... just a statement that fits our observations and has held true for a large set of observations. Tomorrow we may make some observation which conflicts with Boyle's Law, and so we will have to throw out Boyle's Law and come up with something that fits the observations we have.

So it is in the Monastery.