EDIT: FFS why does this subject always get people frothing at the mouth before they even read the main point stated, only to go on and accidentally agree with it eventually? Pls read first before getting mad at stuff that I explicitly argued against.

EDIT 2: OK apparently there's still miscommunication, and I think the 1st edit somehow made it worse. When I say "useful" I put it in scare quotes on purpose and as I clarify in the 1st, 4th and 5th paragraps, it is NOT about value but about practical/technological utility.

I originally posted this on R*ddit to an audience of math nerds (so be warned that it is written with reddit STEMlords in mind) because there was a relevant convo going on and it would be fun to also have it here.

Sure, there is a lot of modern math that is practically useful, but the majority of pure math really isn't "useful' in any way, shape or form for now, and probably won't be any time soon, possibly forever. Like, even areas which are apparently "useful", like computer science, is full of things that have absolutely 0 practical utility and are solely of academic interest. Whether P does or doesn't equal NP doesn't really matter to anyone doing practical work. People wouldn't get upset about their discipline getting slighted or whatever if this stupid idea that scientific research should have "practical application" (which generally means "someone can sell it for money") hadn't proliferated, starting from schools.

Even when someone finds an "application" through some kind of far fetched (or not so far fetched) reasoning, it's some application to, like, highly theoretical physics that may or may not actually have something to do with the real world, and even if it does, it is only relevant in extremely niche experimental circumstances to the extent that it can't ever conceivably lead to technological progress. And even IF it does, sometimes it's just progress relevant only to more research about more stuff without application.

So even then you have to resort to saying something like "the result is not useful but maybe one of the methods used to prove it can be used for something else", and then that something else turns out to also not be useful but again "maybe one of the methods used to find that something else is useful for another something else and that other something else is useful for another other something else and then that other other something else has a practical application that is only relevant to research, but then maybe that relates to some other other other...", etc and it gets kind of silly. That or someone says something abstract like "it's useless now but it may be useful some time!". Maybe. Or maybe not.

In the end of the day the same arguments could be used to justify anything being useful via some contrived butterfly effect style conjecture. This of course is usually done because otherwise people can't get grant money otherwise, governments demand that research will produce results they can use to blow up people or sell stuff. Also the result of a bad educational system that emphasizes this kind of "usefulness", which therefore renders it unable to convince students that something is worth learning unless it is "useful". Of course "why should I learn this if it's not useful to me" is a very valid concern of students, but the problem is somewhere else. First, schools DON'T really teach any of the stuff that is useful and interesting to most people. If they did, then math would get a lot less attacks on that front. Schools teach with 30% of the students in mind, the ones who will really apply the things they learned. The other 70% can just go to prison or whatever as far as the educational system is concerned. Second, schools are very boring and antagonistic towards kids and since kids are miserable learning stuff, they need extra justification to learn them. Third, the schools themselves teach kids to think like that so it's no surprise that they do. Fourth, school math mostly sucks and is super boring for most people.

So yes, most modern pure math is indeed "useless". That is not the issue. The issue is, why does this matter? Why is it bad? Should it be bad? I don't think so. It's a false idea that gets perpetuated at many levels starting from school. But then there is the issue of mathematics being very exclusionary and distant from most people, which makes it harder for them to care, which brings us to the issue of outreach but whatever, that's a different matter.

  • a_dog [any,he/him]
    ·
    edit-2
    4 years ago

    P=NP is monumentally important to practical work. If it’s true, all problems are easy to solve.

    that doesn’t necessarily follow right? like what if all NP problems are solvable in n^googolplex steps or somethinh

    • sysgen [none/use name,they/them]
      ·
      4 years ago

      Even n^googolplex is still sub-exponential time, and in practice ridiculously shitty poly time algorithms can often be reduced in magnitude, whereas if you have an exponential time algorithm you need to find something completely different.

      • a_dog [any,he/him]
        ·
        4 years ago

        it sounds like you’re saying they can’t necessarily be reduced though

        • sysgen [none/use name,they/them]
          ·
          4 years ago

          Yes, not necessarily, but they've pretty much always been up until now. There's like, three or four practical algorithms in n^10.

          All of them are approximations of NP-class problems, suboptimal, or literally invented to be intractable.

          • Pezevenk [he/him]
            hexagon
            ·
            edit-2
            4 years ago

            There’s like, three or four practical algorithms in n^10.

            That's usually because the longer ones aren't practical. Or because they can't find them. But I did a google search and there's some algorithms which are theoretically useful for... something, and they're in like n(10100).

            • sysgen [none/use name,they/them]
              ·
              4 years ago

              The algorithms I found for n10100 are just approximations of non-polytime algorithms.

              And by that I mean that they don't solve a practical problem, not that they aren't practical to use.

              • Pezevenk [he/him]
                hexagon
                ·
                4 years ago

                There was one which was about a neat little word problem which had to do with hanging a picture or something and it was like n^500000 or something. The rest I wasn't sure what they were supposed to be.

                • sysgen [none/use name,they/them]
                  ·
                  4 years ago

                  The one for hanging a picture was published to a journal about silly computer science solutions, it's a problem that was invented to have a ridiculously stupid polytime solution

                  The rest were either estimations, or they had to do with combinatorics.

                  • Pezevenk [he/him]
                    hexagon
                    ·
                    4 years ago

                    it’s a problem that was invented to have a ridiculously stupid polytime solution

                    Well many of those kinds of problems are "silly".

                    • sysgen [none/use name,they/them]
                      ·
                      4 years ago

                      Well yeah but the problem was invented just to make the solution harder, NP-hard problems are encountered often.

          • a_dog [any,he/him]
            ·
            edit-2
            4 years ago

            All of them are approximations of NP-class problems...

            Then is it unreasonable to bet that if NP problems are actually P problems, their best algorithms might be n^(something huge)?

            • sysgen [none/use name,they/them]
              ·
              4 years ago

              It's not, because fundamentally they are trying to approximate non-polynomial algorithms. We saw this play out for other algorithms before a polytime solution was found.

              And even if they could, then one could approximate those high exponent algorithms and have a huge speedup.