Earlier quoted context omitted.
There's a hypothesis I came up with awhile ago and it seems to hold pretty true. If we talk about problems as O(n) where n is the causation distance[0] we've solved the vast majority of O(1) and O(0) problems. It makes sense that biologically we would be primed to think in this way because they are decent approximate solutions for small groups. But the world we live in now is much more complex and many events are cou…
I agree, I've been thinking along these lines for a while too; thank you for phrasing it so clearly. My current thinking led me to conclude that we don't have sufficiently good tools[0] for modelling O(n) problems with n > 2. Particularly when (what your simplification doesn't capture) there are feedback loops involved. Take this O(2) problem: x causes more y, y causes more z, z causes less y but much more x. Or in a…
> I argue that we lack both mental and technological tools to cope with this.
I do think we have the tools to solve these issues. I do not think the mental tools are in the hands of the average person (likely not even in most of your above average people because the barrier to entry is exceedingly high and trying to model any problem like this is mentally exhausting and it thus never becomes second nature). Many of the subjects broached here aren't brought up until graduate studies in STEM fields, and even then not always. An O(aleph_n) problem is intractable but clearly O(10) isn't. We should be arguing about what order approximation is "good enough" but ignoring all the problems that arises is missing a lot of fundamental problem solving. Good for a first go, but you don't stop there. I think this comes down to people not understanding the iterative process. 0) Create an idea. 1) Check for validity. 2) Attack and tear it down. 3) If something remains, rebuild and goto 2 else goto 0. I find people stop at 1 on their own ideas but jump to 2 (and don't allow for 3) for others ideas.
> Why aren't feedback loops taught in school?
I think 3 other things should be discussed as well. Dynamic problems (people often reduce things to static and try to turn positive sum games into zero sum. We could say the TeMPOraL component), probabilistic problems, and most importantly: an optimal solution does not equate to everyone being happy (or really anyone). Or to quote Picard:
> It is possible to commit no mistakes and still lose. That is not a weakness. That is life.
The last part I think is extremely important but hard to teach.
(I should also mention that I do enjoy most of the comments you provide to HN)