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0 pointsRhapso1y ago0 comments

Evolution is a satisficer not optimizer.

All major trophic level breakthroughs are powered by evolving a reserve of efficiency in which mult-step searching can occur.

multicellular life, collaboration between species, mutualism, social behavior, communication, society, civilization, language and cognition are all breakthroughs that permitted new feature spaces of exploration that required non-locally-optimal transitions by the involved systems FIRST to enable them.

Trust is expensive and can only get bought in the presence of a surplus of utility vs requirements.

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bulletninja1y ago

Wow, very well put. Any suggestions for academic papers, books, or even online resources on these topics would be greatly appreciated.

RhapsoOP1y ago

This is related, and it is the paper that lives constantly rent free in my head. I think it will retroactively be viewed as revolutionary: https://www.alexwg.org/publications/PhysRevLett_110-168702.p...

Basically, intelligent behavior is optimizing for "future asymptotic entropy" vs maximizing any immediate value. How intelligent a system is then become a measure of how far in the future it can model and optimize entropy effectively for.

(updated with pdf link)

programjames1y ago

Great paper! There are some similar ideas to this in game theory and reinforcement learning (RL):

[1]: Thermodynamic Game Theory: https://adamilab.msu.edu/wp-content/uploads/AdamiHintze2018....

[2]: piKL - KL-regularized RL: https://arxiv.org/abs/2112.07544

[3]: Soft-Actor Critic - Entropy-regularized RL: https://arxiv.org/abs/1801.01290

[4]: "Soft" (Boltzmann) Q-learning = Entropy-regularized policy gradients: https://arxiv.org/abs/1704.06440

RhapsoOP1y ago

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9372954/

zb31y ago

I didn't say that evolution finds the optimal state, just wanted to highlight how far it was able to go, much farther it seems.. (like evolution of the eye)

But your comment was refreshing, could you briefly expand on the "multicellular" life part? Did you mean that it enabled more non-locally-optimal transitions, or that it required them to appear?

RhapsoOP1y ago

I think cooperation is never a locally optimal strategy. Somebody allways gets to pick second at the prisoner's dilemma table, and locally optimal behavior is to eat the trusting idiot.

Takes a lot of luck to evolve cooperation multiple times at once, much more likely to happen in a situation where the selection pressure is lower, not higher.

dasil0031y ago

Now you get into the definition of "locally". Gene pool local or individual local? I think it's evident that cooperation has proven highly effective at the gene pool level. Will it prove to only be effective short-term local and flame out over longer-time spans remains to be seen. Will there be anyone to document it? Not sure, but it's been a helluva ride.

corimaith1y ago

Pretty sure those non-cooperative strategies quickly burn themselves to extinction though. The selection pressure itself would be regulated towards an equilibrium.

The thing about evolution is that you are sampling many times in different directions. So "luck" isn't that hard to achieve.

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jfoutz1y ago

You quoted this part,

> Take chances, make mistakes, and get messy.

But then seemed to indicate evolution disagrees.

I might be misunderstanding your point, but it sure seems like, evolution tries a bunch of stuff, and whatever reproduces kinda wins.

That seems like, take chances, make mistakes, get messy. That seems like the core of evolution.

Could you clarify or refine what you’re saying? The two seem at odds.

zb31y ago

So that was a bad quote, I only wanted to address the part that mentioned monotonic-only improvement, since to me, evolution has achieved more than I'd imagine, evolving organs like the eye incrementally.

I got inspired by this article: https://writings.stephenwolfram.com/2024/05/why-does-biologi...

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bulletninja1y ago

Wow, very well put. Any suggestions for academic papers, books, or even online resources on these topics would be greatly appreciated.

RhapsoOP1y ago

(updated with pdf link)

programjames1y ago

Great paper! There are some similar ideas to this in game theory and reinforcement learning (RL):

[1]: Thermodynamic Game Theory: https://adamilab.msu.edu/wp-content/uploads/AdamiHintze2018....

[2]: piKL - KL-regularized RL: https://arxiv.org/abs/2112.07544

[3]: Soft-Actor Critic - Entropy-regularized RL: https://arxiv.org/abs/1801.01290

[4]: "Soft" (Boltzmann) Q-learning = Entropy-regularized policy gradients: https://arxiv.org/abs/1704.06440

RhapsoOP1y ago

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9372954/

zb31y ago

I didn't say that evolution finds the optimal state, just wanted to highlight how far it was able to go, much farther it seems.. (like evolution of the eye)

But your comment was refreshing, could you briefly expand on the "multicellular" life part? Did you mean that it enabled more non-locally-optimal transitions, or that it required them to appear?

RhapsoOP1y ago

I think cooperation is never a locally optimal strategy. Somebody allways gets to pick second at the prisoner's dilemma table, and locally optimal behavior is to eat the trusting idiot.

Takes a lot of luck to evolve cooperation multiple times at once, much more likely to happen in a situation where the selection pressure is lower, not higher.

dasil0031y ago

corimaith1y ago

Pretty sure those non-cooperative strategies quickly burn themselves to extinction though. The selection pressure itself would be regulated towards an equilibrium.

The thing about evolution is that you are sampling many times in different directions. So "luck" isn't that hard to achieve.

1 more reply

jfoutz1y ago

You quoted this part,

> Take chances, make mistakes, and get messy.

But then seemed to indicate evolution disagrees.

I might be misunderstanding your point, but it sure seems like, evolution tries a bunch of stuff, and whatever reproduces kinda wins.

That seems like, take chances, make mistakes, get messy. That seems like the core of evolution.

Could you clarify or refine what you’re saying? The two seem at odds.

zb31y ago

I got inspired by this article: https://writings.stephenwolfram.com/2024/05/why-does-biologi...

1 more reply

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