Comment by 317070
10 hours ago
On this topic, I can never end recommending Adam Curtis's series "All Watched Over by Machines of Loving Grace", and on this topic of "bounce back" the second episode in that series.
The concept of "natural equilibrium" is a 1950s cybernetic machine fantasy projected onto the natural world rather than an accurate reflection of ecological reality. We can model the world in the language of system theory, but the models we make there might not have much to say about the real world. We can claim that from system theory it follows that there must be an equilibrium (as somewhat of an expert, I can say that even that is not true) and that if we disrupt the system, things will eventually go back to that equilibrium. And if we don't see it, we just needed to wait longer.
The whole stack of logic is not really build on any proper reasoning. The parallel with economics based on the "homo economicus" is striking, also there you reason from an ideal model and try to make inductive statements about the real world, which in many places directly conflict with observation. The "eco-system" and "natural equilibrium" stem from this approach of applying system theory to ecology, and are ideas which are now _very_ ingrained in western thinking, but which are obviously off.
Here is the start of that second episode: https://www.youtube.com/watch?v=FgoereMDUJ0
A bit of caution with this line of thinking, because I was blindsided by it after more than decade of hand waving away "academics"
When you have the mind of "the models are wrong", you tend to be brought there by select counter examples, and in that space you only feed on counter examples. "The experts are wrong (and you can be right in 15 minutes if you consume this media!)" is abundant.
The actual reality is "the models work great until they don't". Not "the models don't work."
Ironically, the experts, the ones building the "wrong" models, tend to be the ones most aware of this.
> Ironically, the experts, the ones building the "wrong" models, tend to be the ones most aware of this.
This varies substantially by field, or by subfield. There is a vast decades-long intellectual wasteland of bad economics predicated on bad models that are only appealing on normative grounds. The entire field of behavioral psychology might be a scam. Etc etc. Science advances one funeral at a time hard part because people are unwilling to let go of their models, even when they have outlived their usefulness or have been simply proven too wrong to be useful even in their original purpose.
> The entire field of behavioral psychology might be a scam
I studied cognition and came to the very same, sad conclusion. There’s a reason there was a reproduction crisis and it’s far from over. I remember vividly how one of my professors, during a paper debate, would end criticism with: “but it’s been published” as if that handwavy gesture could explain away serious research gaps that even graduate students saw through right away. Shoddy statistics in a system that rewards quantity can do a lot of damage.
Until this moment, I hadn't even thought that economics might be a scientific field. Do they really see themselves as one? It hasn't seemed like one from the outside.
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It's because the models have different purposes in decision making, in ways which decision makers don't often appreciate.
Models are still great at helping us understand the world and are in many ways the best thing we have. The problem is that today we are overly relying on them to make real policy interventions on an ecology (e.g. what is a "healthy" amount of animals to cull or fish) based on an ecosystem, which is just a poor model.
If we could get rid of this idea of "stability" and "equilibrium" in economics and ecology, I would be a happy man.
> Ironically, the experts, the ones building the "wrong" models, tend to be the ones most aware of this
Exactly why I'm here :-)
Decision making is risk management, and is often simpler than making predictions. You need to think in terms of utility functions and you need to stay far away from the catastrophic regions. You often don't need super accurate predictions for that.
One example is pandemics. The thing is pandemics are extremely fat tailed in terms of fatalities. You need a lot of data to fit any outbreak model and no one has time for that. But the reaction to diseases out breaks is very simple. Is it super deadly and contagious? If yes, shut everything down! This is essentially what Asian countries like Vietnam and Taiwan did during Covid and they handled it way better than the West. I remember arguing about outbreak models, which is a completely useless activity.
Maybe the best way to illustrate our point is to use weather models, as it's pretty tangible for regular people.
Weather models are ridiculously advanced running on super computers crunching a massive global network of real time data. But they still aren't anywhere close to perfect.
That being said, just because it rains on your birthday when it was said to be sunny, doesn't mean you delete your weather app, call meteorology pseudoscience, and start a substack of "the forecast was wrong again" blog posts.
People intuitively grasp this foolishness because they constantly interact with weather models. But for things they have almost no contact with, it's easy to write it off on a single "bad forecast"
(I'll also admit the caveat that not every model is as good (or bad) as weather models, another dimension at play to throw a wrench in peoples gears)
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> When you have the mind of "the models are wrong", you tend to be brought there by select counter examples, and in that space you only feed on counter examples.
Box's (possibly apocryphal) aphorism, "all models are wrong, but some are useful", is the better mindset.
Obviously we can never perfectly model nature, but we can often get close enough to form useful predictions. A minor predictive failure does not necessarily mean you throw the model, and all of its predictions, out entirely.
> "The experts are wrong (and you can be right in 15 minutes if you consume this media!)" is abundant.
Social media, sadly, preys on the feeble-minded or willingly-deceived :(
the unspoken permanent wrench in the gears lies in chaos theory. models always have some level of consolidation of reality that gets treated in blocks. While this has intuitive inaccuracies like rounding errors, edge cases, and such, we also know reality often shows emergent behavior from chaotic interactions in a way only understandable after it's actually happened. these could be erorrs that could completely break the models relevance
After observing the emergent behavior, we can often then incorporate it into the model by breaking up prior consolidations into more parts, but the fundamental problem still remains. This makes models for things that arent part of a relatively rapid feedback loop for model improvement (like climate change) to be very vulnerable unknowns. thats not to say they are completely useless, but it can very much take away the weight behind any specificity of the models results, and should open up a second conversation about the direction in which the model might be expected to fail.
Additionally, when creating models that involve human behavior you can appeal to game theory, psychology, sociology, statistics, etc but ultimately chaos will be in full force. now with AI it is going to become a factor amongst automation as well in a way it previously was not. there is no practical way to model how the weights of neural networks might act unexpectedly in various contexts.
all this to say models are not often that great at giving useful predictions as much as they are great at building foundations of understanding of relationships in complex/complicated and what "clean" situations might look like, which can then be taken into consideration of what reality is likely to look like and happen. this distinction is important because currently, automation tools and AI lack that final chaotic adjustment that astute humans are able to apply. Ai has gotten very good at making complicated models but it ultimately is, by design imo whether intentional or not, limited in the same way models themselves are.
that final adjustment to make real world decisions and have personal accountability is more rooted in beliefs/feelings than it is in model outputs, albeit the model outputs help to refine it.
Same thing said by Isaac Asimov in 1986: "The Relativity of Wrong"
https://web.williams.edu/Mathematics/sjmiller/public_html/23...
I was always a fan of what a former senior colleague ingrained in me early in my career:
A model that has no predictive utility is a bad model, but every model hits its limits in terms of predictive power. Some models have a very steep cliff in accuracy at the "edges", and some gradually lose fidelity like an out-of-focus photograph.
Most anti-science criticism I see is completely ignorant of what it really means to model a system or phenomenon: the challenges, the limitations, the end goal, the criteria for success, etc. It's sort of a statistical / scientific ignorance that's deeply cross-discipline. To perform science in a lot of ways is to engage with what it means to build a model, and in some ways, interrogate the universe.
If you're both an idiot and overconfident, it's tempting to throw your hands up and not engage with something as complex as "modeling", and just decide almost out of whimsy what the universe should truly be. But at least some people who find modeling too intellectually taxing at least admit that there are things that they'll never understand.
> We can claim that from system theory it follows that there must be an equilibrium (as somewhat of an expert, I can say that even that is not true) and that if we disrupt the system, things will eventually go back to that equilibrium. And if we don't see it, we just needed to wait longer.
Even if we do make that claim, from practice in systems theory we should know that some systems have multiple potential equilibrium. If a system is perturbed, it may come to rest at a different equilibrium that is much less desirable.
I've worked with mechanical and computer systems where the ordering and timing of applied loads makes a significant difference to observed behavior. Sometimes you have to stop and restart the whole system when it ends up at an undesired equilibrium.
In the natural environment, stopping and restarting in a controlled manner is not something entirely in our control.
I don't like being the "to be fair ..." guy, but if your argument is that sometimes, when perturbing a system, it may come back to rest at a different equilibrium that is much less desirable, it suggests that sometimes it may come back to rest at a different equilibrium that is much more desirable.
In fact, such a distinction might characterize the difference between pessimism and optimism.
> If a system is perturbed, it may come to rest at a different equilibrium that is much less desirable.
That's exactly how we got humans rather than the objectively better rulers of the planet, the dinosaurs.
Chickens will rise again!
I don't really know anyone serious who thinks that ecological systems have a single stable equilibrium state.
Indeed, even the trivial systems you learn about at school, like predator-prey relationships are dynamic equilibria rather than static ones. Experiments clearly demonstrate trophic cascades can shift ecological systems from one meta-stable state to another (e.g. remove crabs from a rockpool, and the herbivore population explodes until it runs out of seaweed, at which point it crashes).
Evolution is obviously a progression between many quasi-stable states.
I think a stronger claim is that _economics_ doesn't recognise this; local incentives and the price feedback structure aren't enough to avoid pushing natural systems out of their current quasi-equilibria state into degraded states that are long-term also poorer for humans (e.g. collapsing fish populations). This is also pretty broadly understood, and why market interventions like fishing quotas exist. But in the conflict between "extract more more now" and "long term health of the ecosystem", "extract more now" is the economic default and "long term health" requires special pleading, so unsurprisingly the former tends to win.
Curtis's stuff is all good fun but I'm skeptical that:
>The concept of "natural equilibrium" is a 1950s cybernetic machine fantasy
Looking at https://en.wikipedia.org/wiki/Balance_of_nature :
>The concept that nature maintains its condition is of ancient provenance. Herodotus asserted that predators never excessively consume prey populations and described this balance as "wonderful". Two of Plato's dialogues, the Timaeus and Protagoras myths, support the balance of nature concept...
Rather than the first two minutes, here's the whole thing: https://archive.org/download/BBC.All.Watched.Over.by.Machine...
This isn't me being pedantic, actual question:
Are we talking about whether nature is ever at equilibrium, or about how stable that equilibrium is?
A ball on the crest of a saddle is at equilibrium, but it isn't a very stable equilibrium.
Basically, are you saying the reality is that nature is always in flux, or that it doesn't take much to knock it toward a different stable state.
This is the story of how one user spent too much time writing why he became disenchanted with Adam Curtis, how he had been a longtime fan. But eventually he saw through the same smoke and mirrors. He learned to see it all as uncharitable nonsense to convince others that technocracy is unassailable. He thought the parody was funny.
But this was a fantasy.
(Curtis's entire ouevre can be summarized in 18 seconds: https://www.youtube.com/watch?v=3YSwCJIpSXQ )
That's a good parody.
I like the Curtis stuff because it hits like old internet-core. It is entertainment that makes you think, but entertainment. Otherwise it would have been written work. But damn the guy can weave a story with some old videos and a soundtrack. As a genre, it's extremely approachable to make your own.
Sometimes it seems like shorts and tiktoks are like a bad Adam Curtis film. 30s highlights stitched together with no relation, garing transitions, ads.
Maybe I can put together a greasemonkey script to make YT shorts Adam Curtis themed. Maybe I won't feel as guilty, maybe it will all make sense.
I have seen these parodies before, and while they are accurate in style, they seem to misunderstand or misrepresent Curtis' message. A message which is not that hard to understand, except that it falls comfortably outside of the Overton window:
"Driven by the seductive post-war fantasy that human beings and nature are self-regulating machines -- a cybernetic delusion shared equally by California tech-utopians, holistic ecologists, and free-market economists -- we willingly surrendered political power and moral responsibility to automated, network-driven systems, only to find ourselves trapped in a static, dehumanizing loop that cannot envision an alternative future and leaves the existing, unaccountable hierarchies of power completely intact."
No, I understand his message.
And again, this is exactly what I meant by "uncharitable nonsense to convince others that technocracy is unassailable." He regularly claims his job isn't to provide solutions or that solutions are increasingly unimaginable, yet that's the only thing that does matter.
If knowing more about how civilization got to this point suggests nothing to Curtis about how to escape from it and hasn't for three decades, despite his supposedly privileged epistemic position, why listen to him in the first place? The man obviously isn't interested in proposing or "raising awareness" of solutions, only problems.
I don't need another six hours explaining why I'm right to be dissatisfied. I already am. Tell me what you've learned that might make me less powerless, not more.
Doesn't the premise of bouncing back imply an ideal state to bounce back to?
And there we have it: ideal state for one species? the most possible species? longevity for individuals or the species?
The myth of some "natural" (the word literally means without human influence) state that is desired/better is somewhat ironic, right?
Any ideal state brings with it subjective criteria.
> Doesn't the premise of bouncing back imply an ideal state to bounce back to?
No, it's an "equilibrium" state, i.e. "a state of balance where opposing forces ... are equal" (from the MW definition.)
The general concept is that a stable system has at least one equilibrium state, in which no processes go out of control and potentially destroy the system.
A simple(?) example would be global warming - if the atmosphere keeps accumulating carbon, temperatures keep going up, the planet becomes uninhabitable, everything dies. Of course, that's subjectively an undesirable outcome for us - not an "ideal state", as you say - but the point is that any state in which no equilibrium can be reached (or at least approximated) exists is subject to such risks.
You could say that equilibrium is a necessary condition for an ideal state, but it's not sufficient. There can also be equilibrium states that are decidedly non-ideal, like the "everything dies" one I mentioned.
I agree, and I've answered the same question before much as you have. I don't disagree with the value of equilibrium.
Our bodies are in a sort of equilibrium when healthy, and cancer is an example of emergence of destabilizing phenomena in the system.
Consider the Carboniferous–Permian oxygen maximum; it probably took tens of millions of years for oxygen levels to reach troublesome concentrations for plants, then tens of millions of years to return to "equilibrium."
Our current atmospheric concerns have been produced much faster, right? And I think we hope to improve the situation somewhat faster than ten million years.
My main point is that equilibrium is inherently subjective, bringing along time scales and traits that the observer cares about.
I supposed you could develop a "equilibrium for the most species at once" approach...
The “natural equilibrium” model originated in physics. It’s well applied and tractable in chemistry. It was then brought into economics and ecology (much more stochastic domains) as familiar metaphor from more deterministic systems.
> if we disrupt the system, things will eventually go back to that equilibrium. And if we don't see it, we just needed to wait longer.
Even if that's true, quite a bit rides on how long "longer" is. If "longer" is a million years, that's not exactly good news.
But if you apply the logic that led to the conclusion of "natural equilibrium", truth of the matter is that we wouldn't be able to say if the waiting will be over before or after the heat death of the universe.
Even the "million years" is an overoptimistic view, and probably reflects the idea that the ecosystem was in some kind of messianic state of equilibrium before man arrived, which happened roughly in the last million years. But even that is broadly a fiction. Ecology is not a system, and it is not stable even without human intervention.
Yeah, it's a bit absurd on its face to speak of equilibrium in the context of evolution, which is basically a litany of innovations and equilibrium breaks. Nature regularly throws itself out of whack, like when cyanobacteria appeared I recall, and then when nature made us, obviously. Whatever equilibriums existed through time are rarely returned to once broken.
> We can claim that from system theory it follows that there must be an equilibrium (as somewhat of an expert, I can say that even that is not true) and that if we disrupt the system, things will eventually go back to that equilibrium.
umm, that is not what the word equilibrium means. Obviously no system is stable to every and all disruption.