5 ms·

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

by Rhapso 2y ago

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.

bulletninja 2y ago
Wow, very well put. Any suggestions for academic papers, books, or even online resources on these topics would be greatly appreciated.
- Rhapso 2y ago
  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9372954/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9372954/
- Rhapso 2y 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.pdf 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)
  - programjames 2y 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.pdf https://adamilab.msu.edu/wp-content/uploads/AdamiHintze2018.... [2]: piKL - KL-regularized RL: https://arxiv.org/abs/2112.07544 https://arxiv.org/abs/2112.07544 [3]: Soft-Actor Critic - Entropy-regularized RL: https://arxiv.org/abs/1801.01290 https://arxiv.org/abs/1801.01290 [4]: "Soft" (Boltzmann) Q-learning = Entropy-regularized policy gradients: https://arxiv.org/abs/1704.06440 https://arxiv.org/abs/1704.06440
zb3 2y 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?
- jfoutz 2y 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.
  - deleted 2y ago
    [deleted]
  - zb3 2y 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-biological-evolution-work-a-minimal-model-for-biological-evolution-and-other-adaptive-processes/ https://writings.stephenwolfram.com/2024/05/why-does-biologi...
    - Rhapso 2y ago
      Basically, the root disagreement was "monotonic improvement". Evolution is awesome, but it couldn't work with only monotonic improvement. I used to do an "optimization" on my genetic algorithms. I'd ensure the highest scoring genome of the last population was a member of the new one. It made sure every single generation improved or stood still. It was a good idea to keep a copy of the "best" genome around for final output, but by keeping it in the search space, I was damaging the ability of the algorithm to do it's job by dragging the search space constantly back to the most recent local optima.

by Rhapso 2y ago

bulletninja 2y ago

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

Rhapso 2y ago
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9372954/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9372954/
Rhapso 2y 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.pdf 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)
- programjames 2y 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.pdf https://adamilab.msu.edu/wp-content/uploads/AdamiHintze2018.... [2]: piKL - KL-regularized RL: https://arxiv.org/abs/2112.07544 https://arxiv.org/abs/2112.07544 [3]: Soft-Actor Critic - Entropy-regularized RL: https://arxiv.org/abs/1801.01290 https://arxiv.org/abs/1801.01290 [4]: "Soft" (Boltzmann) Q-learning = Entropy-regularized policy gradients: https://arxiv.org/abs/1704.06440 https://arxiv.org/abs/1704.06440

zb3 2y 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?

jfoutz 2y 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.
- deleted 2y ago
  [deleted]
- zb3 2y 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-biological-evolution-work-a-minimal-model-for-biological-evolution-and-other-adaptive-processes/ https://writings.stephenwolfram.com/2024/05/why-does-biologi...
  - Rhapso 2y ago
    Basically, the root disagreement was "monotonic improvement". Evolution is awesome, but it couldn't work with only monotonic improvement. I used to do an "optimization" on my genetic algorithms. I'd ensure the highest scoring genome of the last population was a member of the new one. It made sure every single generation improved or stood still. It was a good idea to keep a copy of the "best" genome around for final output, but by keeping it in the search space, I was damaging the ability of the algorithm to do it's job by dragging the search space constantly back to the most recent local optima.