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Comment by yeoyeo42 | Hacker News Reader

no offense, but it just sounds like you don't understand local maxima in high dimensional spaces.

a suboptimal trait evolving that still provided an overall population benefit (via survival or reproduction) with further improvement close to impossible without a multitude of large changes is literally just a local maximum in that high dimensional space that maps traits to whatever success metric you're measuring.

the infinite wall you describe is not a wall in that space. actually I originally wrote its more like a wall in the derivative space but its not quite right either, those just point straight down from the maximum.

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Perhaps not no, please enlighten me, where is my lack of insight?

Edit, thanx for the addition. Perhaps my issues with looking at it as "local maxima in high dimensional spaces" (even though we think of minima usually, as in a marble will find the minimum in a landscape with gravity pointing down, sorry I'm a biologist) is that with every mutation and every change in environment the landscape changes (so basically with time), is it still a useful way to think about it like that, at that point? So it not a "landscape in a high dimensional space", but rather a "high dimensional dynamic surface", like a sea?

... In fact, because te landscape is so dynamic and can sometimes change within 1 to a few generations, having a brain as the primary store for behavior/customs/models/information is super advantageous compared to using DNA, as the brain is a flexible neural network and can adapt much, much quicker. No doubt this is why we are successful (ie, a dung beetle will die trying to push a stuck piece of dung, because the behavior is hard-coded and it cannot model the world and model solutions to encountered problems, and it therefore cannot pas solutions on to future generations, as we can. Perhaps a dung beetle will someday mutate to alter it's behavior when stuck, but you can see how suboptimal the whole process.)

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