Not sure what you mean by "things are stable" in this context, could you elaborate?

You get the same patterns in music: https://pure.uva.nl/ws/files/67945245/000016_2_.pdf

And I started asking, "What are the important problems of your field?" And after a week or so, "What important problems are you working on?" And after some more time I came in one day and said, "If what you are doing is not important, and if you don't think it is going to lead to something important, why are you at Bell Labs working on it?" ... If you do not work on an important problem, it's unlikely you'll do important work.

It is the main idea behind his lecture/essay "You and your Research", which is worth reading: https://www.cs.virginia.edu/~robins/YouAndYourResearch.pdf or watching: https://www.youtube.com/watch?v=a1zDuOPkMSw

Yeah, I think we just missed each other since your name sounds familiar.

One classic result from biophysics is that, if all of your decisions have a fixed level of difficulty, then what the author suggests is (mathematically) proven to be suboptimal: if a decision feels more difficult, you actually should spend more time on that decision. (Keywords to search for: drift diffusion model[2], sequential probability ratio test[3])

The technical term for what the author is suggesting one should do (not spending so much time on decisions with equal outcomes) is an "urgency signal", or just "urgency" for short. If you are using an urgency signal, then you will spend less time on difficult decisions (i.e. ones with near equal outcomes) than you would without an urgency signal, but still more than you would easy decisions. (For easy decisions, you spend approximately the same amount of time regardless of whether you have an urgency signal.) In the extreme case, for an infinitely strong urgency signal, you will spend equal time on both easy and difficult decisions. (See Paul Cisek's work, e.g., [1].) Conceptually speaking, you need time to detect that the current decision has near-equal outcomes.

It was only recently (mathematically) proven that if you have different levels of difficulty in your decisions, it is optimal to use an urgency signal [4,5]. So since most sequences of decisions aren't all equally difficult (as in the study referenced here), in practice, people will use an urgency signal in decision-making.

Of course, both the desired accuracy and the urgency signal depend on how the decision is being evaluated: if your goal is to make an accurate decision (e.g. buying a house), then you will have a weaker urgency signal and require more evidence before you make a decision. By contrast, if you prioritise decision speed, you will show more urgency signal and require less evidence to make a choice. (The technical term is "speed-accuracy tradeoff".)

Current work suggests that when you are making some decision for the first time, you will not use an urgency signal, but as you become an expert at making those decisions, you will gradually develop an urgency signal [6]. This makes sense conceptually: if you know approximately how hard you can expect these decisions to be, you will recognise the situation where they have approximately the same utility and adjust your strategy accordingly.

[1] https://www.jneurosci.org/content/29/37/11560

[2] https://en.wikipedia.org/wiki/Two-alternative_forced_choice#...

[3] https://en.wikipedia.org/wiki/Sequential_probability_ratio_t...

[4] https://psycnet.apa.org/fulltext/2017-16730-001.pdf

[5] https://link.springer.com/article/10.3758/s13423-017-1340-6

[6] https://www.sciencedirect.com/science/article/abs/pii/S00100...

And responding to the other commenters, no, this kind of technology is definitely not (and never will be) a toy...

My implementation ended up being about 10 lines of code and ran slightly faster (!!!) than the LAPACK function. (I think this is because LAPACK provided numerical stability for special cases which didn't apply to my use case.)