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.
This doesn’t solve your problem of who decides it is? Or isn’t?
I like the former argument that you should question “experts”.
After all, why use the “Trust the experts model” at all? Which experts? How many? What is a model? What does “works” mean? What not? And who decides about the decision-making process itself?
The former poster has merits because it is a battle of models: and again, research and new findings must not be tied to obscure academic titles.
Two academics of the same level come to opposite conclusions - now what?
And a scoring system still doesn’t change the fact that nature doesn’t care about anthropomorphic models.
I studied law, math and computer science and I was especially disappointed by mathematics, after all this is an anthropological abstraction by human beings and Cantor is the best example of shunning a man’s work, which was ground breaking, just because academic consensus opted against him and didn’t accept his work.
Only many year after his death his system became a standard that appears so clear that you wonder what was wrong with the committee back then.
So mathematics is far from being this strict, stringent and logical profession, as it is branded. Instead there are human beings using a model that abides to certain rules.
Useful, but no proof at all, whether there is a better one, like with Newton and Einstein.
Cantor by the way quit mathematics due to the rejection of his work, which was accompanied by hostility.
You be the judge here.
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.
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.
So it's science inasmuch as anything with n=1 repeatability can be science
It may have to do with the overall complexity being easier to effectively model. Psychology has such big error terms that you either give up or get comfortable with a lot of hand-waving and dogma. A huge proportion of psychologists are just activists and have no real interest in being scientists (beyond the prestige).
Do they believe it's a science? Assume that Upton Sinclair's famous quote applies.
Eco: house, household, or dwelling place.
The -logy suffix is 'science'. So ecology is: the scientific study of how living organisms interact with one another and with their physical surroundings.
The -nomy suffix is a field or system of something.
Economy is the ecology of finance. Or, Economy is a field of science that predicts financial systems. Any scientific field helps you make predictions, once you know the material. But useful predictions require useful measurement.
Logos in particular is my favorite word. The logy suffix derives from it, and one approximation is "thoughts" or you might also see "word". The Bible begins with "In the beginning there was the Logos". We translate that as "Word", even though the connotation is also "thoughts". It might be more accurately understood as "the patterns, rules, logic of the universe". For the Greeks, thought was not something one "had", in the same sense we think, but rather something innate in nature that one participated in.
So the Bible verse might be better understood as "in the beginning there was the same working logic in the universe that we observe and participate in now, the same innate rules governed the cosmos that do now". That's actually a much more useful and honest interpretation than "in the beginning was God's words".
When one studies their particular -logy, biology, ecology, etc, they are not just looking at things describing them, they are more intrinsically bound up in a truth discovery exercise about something whose patterns and working can be figured out.
Natural Science could be conceptualized as a logology - literally studying how the Logos works in its various dimensions.
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 :-)
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.
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)
Of course, they never convinced ME! Silly, wannabe flat earthers.
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 :(
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.
https://web.williams.edu/Mathematics/sjmiller/public_html/23...
- a model is primarily judged by its predictive power
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.
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.
In fact, such a distinction might characterize the difference between pessimism and optimism.
That's exactly how we got humans rather than the objectively better rulers of the planet, the dinosaurs.
There is a reason why after WW2 so many theories of nature came to light. It was a cultural shift from day to day survival to being taken care of by a system that more than allowed to handle humans’ most basic needs.
After all we are the result of many 100.000 years of evolution. The modern time is roughly 100 old - compare these spans: from the perspective of an anthropologist 100 years is not worth to be stacked against x times 100.000 years.
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.
>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...
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.
But this was a fantasy.
(Curtis's entire ouevre can be summarized in 18 seconds: https://www.youtube.com/watch?v=3YSwCJIpSXQ )
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.
"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."
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.
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.
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.
umm, that is not what the word equilibrium means. Obviously no system is stable to every and all disruption.
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.
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.
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...