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.