[aeim]

> "what if we’re pouring more and more effort into research in an area, because our primary abstraction is wrong?

Absolutely; perhaps "thinking of knowledge in terms of physical environments" also helps to illustrate why – sometimes the only way to get to where we want to be, is to redefine where-and-how we begin

Concretely, looking up a unconquered mountain: it may seem as though many approaches are viable, but when attempt proves otherwise, the only option might be to return to base, and consider a new origin for approach

I think this also helps illustrate why "progress by accrual alone" is so inherently flawed. Sometimes you must begin again, in whole, but also in part – because "you can't get to anywhere from anywhere"

In fact, for almost all domains other than science, beginning again is such an optimal remedy to progress dead-ends, it's operational

Writers know what's up – kill your darlings. Software engineers too, famously – measuring software progress by lines of code added is counter productive – we learn to be unsentimental, delete and reconsolidate often. This is the continual redefinition of the "origin" of where we continue-on-from

Though it does require a change in perspective: written words, and lines of code – not as asset, but liability. Redundant words, code or literature, is contrived complexity. Unnecessary, and after a while, prohibitive to progress. I think science hasn't done this yet? – we know that naming things is hard, but perhaps for science, calling every artifact 'literature' doesn't help...

I also think that science catastrophically misunderstands "beginning again". Finding new origins is easiest for those who haven't been conditioned to only see one. It doesn't take magical genius, just (selective) ignorance. This is why (non-trivial) revolutionary paradigms often come from outsiders (or historical scientists who did not complete the endurance test of the modern day burden)

I can't help but think that scientific genius is based on "eh, fuck that, i'm sure there's a better way"

It's as if, when Kuhn wrote "Structute", a million normies cried out in pain, and because they'd already been conditioned to one origin, and couldn't stand the idea of "newbs" getting credit, vowed to prove that "progress by iterative accrual alone" was all that was necessary. And here we are

[Ar-Curunir]

> This is why (non-trivial) revolutionary paradigms often come from outsiders (or historical scientists who did not complete the endurance test of the modern day burden)

Can you give me an example of this in the last century? Or even historically?

[aeim]

Why artificially limit the (historical) set-of-all non-trivial revolutionary paradigms to the last century?

Current academia seem to treat the phd (implicit modern) as a minimal barrier to credibility (which is curious for several reasons). But given the modern phd is a relatively recent invention, this heuristic ought to exclude every paper written by those who predate this new form/ qualification. Of course, historic progress seems to be revered most of all

The point isn't to suggest that the modern form was beyond historical figures (though I suspect given the nature of earlier scientific progress many might choose to not endure the modern form), but more frame the question "had they (historical figures) been conditioned to think narrowly, within the contrived 'lanes' of modern isolated disciplines, would they still have thought as they did?"

All of Hofstadters "analogies which shook the world" (surfaces and essences) necessarily depend upon thinking across arbitrarily plural scopes of phenomena, and deeply — with commitment to the premise that the deepest understanding of our universe which that they might contribute to the endeavour, frames an implicit "universal general domain" of characteristic forms which apply universally. This is the opposite of isolated special-domain thinking/ stay in your lane/ etc

Education includes learning and conditioning. I wonder if the degree to which we stress test conditioning impacts ongoing learning, specifically open learning, learning across (artificial) boundaries of special-domains