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To boil it down simple: People are vaguely good and competent, they leave systems in a locally-optimal state. In general only changes that are "one step" are
by Rhapso 2y ago
To boil it down simple:
People are vaguely good and competent, they leave systems in a locally-optimal state.
In general only changes that are "one step" are considered, and they allways leave things worse when you are currently in a locally optimal state.
A multi-step solution will require a stop in a lower-energy state on the way to a better one.
Monotonic-only improvement is the path to getting trapped. Take chances, make mistakes, and get messy.
- zb3 2y ago> Monotonic-only improvement is the path to getting trapped. Take chances, make mistakes, and get messy. The evolution disagrees.
- Rhapso 2y ago<gif="blinks in Cambrian Explosion"/>
- Rhapso 2y agoEvolution 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 agoWow, very well put. Any suggestions for academic papers, books, or even online resources on these topics would be greatly appreciated.
- Rhapso 2y agohttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9372954/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9372954/
- Rhapso 2y agoThis 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 agoGreat 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 agoI 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 agoYou 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 agoSo 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 agoBasically, 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.
- furyofantares 2y ago> The evolution disagrees. Ah yes monotonic-only improvement by way of making every small, messy mistake possible and still probably going extinct is definitely the way to go
- roncesvalles 2y agoWhat makes you think we're not trapped in some really mediocre local optima? Tree dust makes me sick.
- marginalia_nu 2y agoEvolution regularly ends up in local optima that it struggles to get out from. Species go extinct all the time when there's no evolutionary path that solves its problems.
- deleted 2y ago[deleted]
- zb3 2y agoI didn't mean to say that evolution avoids local optima, but I wantend to say that it doesn't have to get "trapped", in the sense that it was able to produce such complex organisms as humans..
- marginalia_nu 2y agoRight, but it could well be that there is some other greatly superior organization of life toward which there exists no evolutionary path.
- gizmo 2y agoBecause of the incremental nature (it can't think moves ahead) only a tiny percentage of all viable life forms can be produced by evolutionary processes. Species forged by their ruthless struggle for survival. Maybe humanity will be the first species to escape evolutionary constraints, but maybe humanity is like the many other species that burn brightly but briefly. The universe seems to be cold and empty and devoid of life. Perhaps evolution is very good at producing cockroaches and not that good at creating intelligent life.
- magicalhippo 2y agoAnd on the flip side, a sufficient abundance of resources and/or lack of predators mean non-optimal species can procreate, and thus find other local optima.
- marginalia_nu 2y agoIn terms of evolution, the fitness of a species is defined by its ability to reproduce. In the circumstances you describe, selection pressure exists for the species that can reproduce the fastest. Predators or resource constraints are not a requirement for evolution.
- jptv 2y ago>> Monotonic-only improvement is the path to getting trapped. Take chances, make mistakes, and get messy. > The evolution disagrees. It’s not an either/or. Vast modularized localized improvement allows for the ability to prune and select what does and doesn’t work. https://hbr.org/2020/01/taming-complexity https://hbr.org/2020/01/taming-complexity
- airstrike 2y agoTL;DR you can't make an omelette without breaking eggs?
- Rhapso 2y agoYou can always make it with blood instead.
- richrichie 2y agoLOL
- Jensson 2y agoWhich is why facebook had the motto "move fast and break things", you need to break bad abstractions to get to good abstractions and solve problems.
- Rhapso 2y agoI decided i would prefer to quote Ms Frizzle instead of this.
- deleted 2y ago[deleted]
- adrianN 2y agoUnfortunately it happens often enough that you manage to sell the step where you make things worse, but then you never get management buy-in for the step that makes things better again.
- m463 2y agoI think "better" is ambiguous. Better for developers? Better for users? Better for speed? Better for maintenance? Better license? Better software stack? Better telemetry? Better revenues through subscriptions?
- throwAGIway 2y agoAt a business, all of these - a good engineer/architect has to find the right balance.
- tremon 2y agoDifferent engineers can have different interpretations of "right balance" -- and many of them may be correct. Which makes "better" again ambiguous.
- throwAGIway 2y agoIt's not ambiguous, it's a collective decision between engineers and business stakeholders. The ambiguousness comes from engineers not having full information.
- prerok 2y agoI think this is actually addressed in the article: > The key question for the designer is, “What would the system’s structure need to be so that <some feature> would be no harder to implement than necessary?” (It’s a bit surprising when designers don’t ask this question, instead simply asking, “What should the design look like?”—for what purpose?) During my career, I have been in many situations where the SW architects tried to answer the second question: as if the architectural cleanliness was the goal unto itself. Software design patterns were misused, unneeded abstractions abounded everywhere, class hierarchies were created 15+ levels deep. There it was often brought up which is better and nicer and cleaner because the metric was aestetics. Most of those arguments, however, are quickly brought to a stop, if we are actually asking the first question: how hard is it to add these new features? That said, I was frequently unable to convince coworkers in my past employments, that aestetics of the design is not the goal. They simply clung to it, to somewhat religious extent, identifying themselves with their "artwork".