Looking at the paper, the core message is 'that even scientists harbor the illusion of understanding more than they actually do'.
In reality, science operates much like a mental model. The paper argues that just because a model predicts future values more accurately, it doesn't mean the model explains the actual causal structure. Yet, the fact that outcomes fall within the predicted range reinforces the illusion that one has truly 'understood' it.
This reminds me of the statistician's aphorism: 'All models are wrong, but some are useful.' Science itself, in a way, is a mental model—a simplification created for humans because the world is a complex system that is cognitively impossible to fully comprehend. Within that framework, certain facts reinforce the mental model, while others weaken it. While mental models vary from person to person, in a broad sense, we are commonly taught to view the macroscopic world through the Newtonian model and the microscopic world through the quantum mechanics model.
Reading this makes me reconsider what 'understanding' truly means. I believe the starting point of genuine understanding is acknowledging that perfect prediction is ultimately impossible, and that when viewing the world through our mental models, what matters is defining what we consider to be acceptable 'lossy information' (or information we can afford to lose)
This is kind of interesting, but I predict that it pleases almost nobody. Philosophy of science types will be kind of annoyed at the preoccupation with statistics, ML people will be annoyed at too much philosophy of science, etc.
I totally support a goal to get those groups talking more but something tighter is probably better. And why isn't it tighter? Without big original contributions, the goal does seem to be a survey
Like thinking LLMs aren’t magic* because you utter “it’s just predicting the next token!” I’d argue, only slightly tongue in cheek, that thinking of LLMs as magical leads to more effective use than the predicting-next-token explanation.
See also Frank Keil’s “illusion of explanatory depth.”
* magic not as “unreal,” but in the classical conception of a living magic world where mental intentions can manifest physical realities
This is a classic case of overthinking.
Induction should not yield new knowledge because nothing new is discovered, but it does.
Deduction likewise also cannot establish new knowledge, yet it does.
Empirical science is flawed on extremely many levels but it works because on average, over time, many converging observations can build refined and accurate causal theories.
It’s a matter of practicality that things cannot be proven fully. Judging from the state of modern medicine, engineering and the sciences, the system works ok regardless
What is a model anyways? There are so many answers to say you that. The models are almost the same models, but at a different abstraction away from the original experienced in reality.
A model is an idea, activity, or object that represents some other idea, activity, or object. A good model is one that helps you understand or manipulate the thing that it represents.
"the sciences" is very broad. in biology there are established methods for establishing causality (i.e. Koch's postulates, etc), and even then conclusions are generally qualified. not sure about the other fields, but I wish they had more concrete and recent examples of what they are talking about. this was painful to even skim.
also for some reason i cant click on anyting on the site or select text?
In reality, science operates much like a mental model. The paper argues that just because a model predicts future values more accurately, it doesn't mean the model explains the actual causal structure. Yet, the fact that outcomes fall within the predicted range reinforces the illusion that one has truly 'understood' it.
This reminds me of the statistician's aphorism: 'All models are wrong, but some are useful.' Science itself, in a way, is a mental model—a simplification created for humans because the world is a complex system that is cognitively impossible to fully comprehend. Within that framework, certain facts reinforce the mental model, while others weaken it. While mental models vary from person to person, in a broad sense, we are commonly taught to view the macroscopic world through the Newtonian model and the microscopic world through the quantum mechanics model.
Reading this makes me reconsider what 'understanding' truly means. I believe the starting point of genuine understanding is acknowledging that perfect prediction is ultimately impossible, and that when viewing the world through our mental models, what matters is defining what we consider to be acceptable 'lossy information' (or information we can afford to lose)
Explained by this wonderful series
It reminded the authors of this too, since they quote and source it
I totally support a goal to get those groups talking more but something tighter is probably better. And why isn't it tighter? Without big original contributions, the goal does seem to be a survey
See also Frank Keil’s “illusion of explanatory depth.”
* magic not as “unreal,” but in the classical conception of a living magic world where mental intentions can manifest physical realities
;).
The math in science isn't provable, objective, or self-consistent, and mathematicians who look at physics regularly have "Wait a minute..." moments.
But scientific math is a useful toolbox of techniques that create useful metaphors where the maps and the experiences coincide, to a useful extent.
Science is really a process of inventing and trying out metaphor maps and keeping the ones that match experience.
Reality itself is likely unknowable, because our experience of it is too limited to provide enough information to get down to the bedrock mechanisms.
So we have these intermediate models that get some way there, but clearly have gaps and edges where the parts don't fit together.
Everything starts at human-scale and works outwards.
"the sciences" is very broad. in biology there are established methods for establishing causality (i.e. Koch's postulates, etc), and even then conclusions are generally qualified. not sure about the other fields, but I wish they had more concrete and recent examples of what they are talking about. this was painful to even skim.
also for some reason i cant click on anyting on the site or select text?