I can talk a bit about how I distinguish "the problem is difficult" vs "I suck at the problem".
Problems of different difficulty feel different.
I have spent at least a month of full time effort each directly tackling P vs NP, large integer factorization, and the Collatz conjecture. All of those are problems which enough very-smart people have spent enough time on that I can be confident they're hard for the rest of humanity, not just for me. Having done that, I know what those sorts of problems qualitatively feel like.
I have also spent at least a month of full time effort each studying human aging and interp on a vision net. These are problems which are unsolved, but which people do make unambiguous progress on from time to time. And they feel qualitatively different from hard problems like P vs NP or Collatz.
I have also spent at least a month of full time effort each on proving a theorem about stability of a class of biological signalling systems and on building a cold-start recommendation system for social events. Each of these were hard enough that the first try didn't work well, the first ten tries didn't work well, I had to find some nontrivial new-to-me insights along the way to making them work. But they were tractable, and I eventually solved them very well. So I know what that difficulty level feels like.
And of course I have spent many months in school on homework problems and projects which require some work but are ultimately straightforward. Anyone with a technical degree probably knows what that feels like.
By working on problems which span the difficulty spectrum, I have a calibrated intuitive sense of the difficulty of different problems. I have some idea of what it feels like when I'm just not smart enough, vs what it feels like when no human out there is smart enough.
The other thing which helps to distinguish "the problem is difficult" vs "I suck at the problem" is to have already studied every field and subject which seems at all relevant to the problem. I have studied a lot of material across a lot of fields. Every time someone comes along and says "maybe <subfield> has relevant ideas", I at least look into <subfield> enough to form my own judgement of whether it has relevant ideas, and if so I study it. I have read through multiple course catalogs cover-to-cover, and I have read through every title in several years' archives of Nature and of certain Arxiv tags, in search of any relevant obscure subfield I might be missing.
Both of those methods - calibration on problems of various difficulty and studying everything at all relevant - take a lot of time and effort. They are not cheap. They are not tricks you can just whip out next time you're wondering whether the problem is difficult or you suck.
The good news is that both of them are capital investments. The cost is mostly one-time, upfront cost. It's years worth of upfront cost, but once it's paid, you can keep reusing the things you've learned cheaply in-the-moment for a long time to come.
they may learn the lesson that the problem is difficult instead of I suck at the problem
That the problem is difficult is a correct lesson to learn from failing to solve it, you just need to be calibrated about how difficult it implies that problem to be, not to get overconfident about the hypothesis that it's more difficult than it actually is. By failing to solve a problem, you did just get evidence that it's not very easy, that you didn't have before attempting it. This is why it's also important to do at least some work in the direction of solving a problem (that could escalate to success if the problem is easy) before declaring it beyond your reach (not that declaring it beyond your reach is necessarily an easy call even after that).
And being bad at a problem (or the problem being difficult to solve) depends enormously on the currently available intellectual and technical tools. A lot of problems that were enormously difficult in the 19th century are trivial to solve in 2020s, and it's not because people in 2020s are smarter, or that the same problem can get inherely easier over time. It can be useful to work on developing such tools if the problem can't be attacked directly.
Core
Arrogance of the Humbled: When someone tries something and fails, they may learn the lesson that the problem is difficult instead of I suck at the problem, leading them to confidently claim furthermore the problem is difficult therefore no one will succeed at it, even incorrectly so, and be even more confident in their authority to make that claim for having failed at the problem.
Paradoxically, one might consider that failing at a problem should make you less confident in your ability to predict its resolution, and attribute yourself less authority on the relevant topics. Thus, arrogance.
Context
Eliezer yudkwosky, in Cat-Belling Problems, writes:
and
Sabien's article can be found here, and it is both easy and fun to read. I recommend it. Since the thesis is clear in the article, I presume that Yud could find value in (1) rewriting it in his own style, (2) calling the attention of his readership to it or (3) expanding the idea, going deeper.
I cannot do (1) or (2), which makes me confident that it's impossible; therefore, all future effort on Arrogance of the Humbled should focus on tackling (3).
Personal-suckiness vs objective-difficulty
There are many ways to fail at solving a problem. Sometimes, you discover the cat-belling step and fail at it; then, you at least gain the insight that a solution to the problem can be productively investigated by considering how it tackles the cat-belling step.
Sometimes, you mostly learn that you suck at the problem - maybe you fall in depression, figure out that your work ethic is terrible, that you need more background knowledge or something. Then, you do not gain insight into how other people may fail at the problem (except insofar as they suck as much as you for the same reasons as you).
The missing insight, in both Sabien's piece and Yud's is: how do you know what direction to update on the personal-suckiness vs objective-difficulty axis?
I don't have a good, principled, fully general answer. Ideally, I'd want something like "keep track separately of what failing teaches about your thinkoomph in the topic, and what it teaches about the difficulty of the problem".
But critically, the two are probably not independent in your self-modeling. How often do you use objective metrics when thinking of how difficult it is to solve a problem cognitively?[1]
Sabien gives as an example of Arrogance of the Humbled, which I'll investigate because it's fun:
For heavier-than-air flight (as opposed to e.g. balloons, which are kosher), the standard impossibility argument went:
Therefore, the airplane won't fly.
Then, the author in the example goes wrong by claiming that we'll need millions of years to get materials as good as bird (to do as well as them). That's just a straight-up bad take that indicates they didn't understand which parts of the impossibility argument were load-bearing (or they were grasping for a punchline, or whatever other reason people have to make bad arguments).
But tell me, knowing what the author knew, what flaw could you have found in the impossibility argument?
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Well, as far as I can tell, the argument worked pretty well, I don't see any single core flaw that invalidates it. Airplanes did in fact address the problem as a whole, at every step of the impossibility argument:
(and more)
Presumably, the author could have noticed that the impossibility argument was not an unaddressed cat-belling step, if they had interrogated Langley on how he was trying to better than birds. But that requires deep familiarity with the topic (here, Langley's prototype, not the impossibility argument).
So (1) If you are unfamiliar with the topic, don't assume you know a cat-belling step others are ignorant of.
If you have very confident priors in your superior thinkoomph, then I can do nothing for you, sorry. Yud has tried addressing epistemic modesty before, but not to my satisfaction; hopefully if he does write an article on Arrogance of the Humbled, he'll rephrase his position as someone with confidence in his superior thinkoomph.
Other sufficient or necessary conditions for updating on the personal-suckiness vs objective-difficulty axis are left as an exercise for the reader.
Having spent a couple hours thinking about this topic and finding no cat-belling step, no difficulty besides my own willingness to keep writing this article, I conclude that conditional on someone writing a proper article on Arrogance of the Humbled aiming to solve the issue of deconfusing personal-suckiness vs objective-difficulty, they'll probably find a satisfying solution. Good luck!
Example of some metrics to help you deconfuse how much the problem is objectively difficult vs how much you personally suck at the problem:
- "How much on a scale of 1-10 would an average person say that they struggle to solve this problem?" (although your perception of the "average person" is probably correlated with your own thinkoomph by selection effects)
- "How long would domain experts take before giving up, if I asked them to solve this problem at dinner?"
- "What fraction of all papers, weighted by author strength, on this topic have I read?"