[RichardChu]

I worry that the way modern society is structured disincentivizes deep understanding.

1. Industry cares more about concrete results, quick execution, and bias for action.

2. Academia cares more about positive results, quantity of published papers, and small achievable experiments over big experiments that might fail.

Where are the institutions that care about deep understanding?

[catwind7]

i agree. I think this is why I find companies like tesla and spacex exciting. They seem to have set up incentive structures that encourage _both_ quick execution and innovation (which requires deep understanding). One thing he's said that really struck me is that it's _really_ difficult to produce innovation if you tie punishment to failure. People tend to be conservative if they are punished / think they will be punished harshly for trying and failing. But if you want to innovate, failure has to be an acceptable outcome

hopefully we see more companies go in this direction

[jmchuster]

well that's where the whole mantra of "move fast and break things" comes from.

Putting it out there and failing also accelerates you faster to the right answers. If you release it today, it'll take 6 more months of iteration to really get it right. Or maybe you spend an extra 2 years of development to get it "right", but then once you release, you'll still have to spend 3 more months of iteration anyways to get it right.

[catwind7]

yeah, this hits home for me because my team just spent a couple of years trying to get a product right and now it's on the verge of being replaced.

I think I've typically associated that mantra with pure software companies, so it's surprising when a company does this with rockets

[wenc]

I would say the IAS is one, as are other pure research institutes like Perimeter.

I would also say that a deep understanding isn’t usually necessary to make progress in many fields of human endeavor. Fields like engineering work on the basis of empiricism — theoretical understanding usually comes later. I could be all wrong here but my gut feeling is that the majority of great breakthroughs in engineering have come through tinkering and luck rather than a principled application of science — the latter is used more for refining the execution.

Even in recent times, neural network models have been shown to work without any deep understanding apart from the basics. It’s only recently that a lot of new theory have come out.

Deep understanding is a worthy goal in order to deconstruct things to learn how to make them fundamentally better or to build a foundation for further progress.

But as humans, we do very well surviving in a world that we largely do not deeply understand for the most part — I’d argue we are able to do so through heuristics (see Gigerenzer). We definitely should not let the lack of deep understanding prevent us from taking the initiative to do things.

[LoathsLights]

Unfortunately deep understanding doesn't put food on the table, concrete actions do.