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)
Exactly. The lede buried here is, as you say,
accurate prediction is not better understanding
Which has a statistician counterintuition
Less "accurate" model can lead to better prediction
Therefore (in my understanding)
A better understanding encodes more info about how much more it can be improved, when compared to a less good understanding
Maybe understanding should be related to wisdom rather than intelligence? Like Socrates. AGW?
Explained by this wonderful series
> This reminds me of the statistician's aphorism: 'All models are wrong, but some are useful.'
It reminded the authors of this too, since they quote and source it
Yes, but isn't since exactly about those models? If you want to calculate how much that steel truss is going to bend when loaded, you need basic mechanics. Sure you could go deeper and think about what actually happens to the metallic structure on an atomic level, you could think about the whole thing in relativistic terms, etc. But this is not going to give you a better bridge.
More accurate theories are important once your requirements are so extreme that without them your prediction is off.
Understanding is about knowing these mental models at the different levels, how they connect to each other and where these models have weird gaps and/or disconnects. Since is and always has been about understanding the best current explaination of the things we observe. Whether it is exactly as you say, or some more elaborate hidden structure is beneath it, is not something you can tell apart, unless you run into the actual limitations of your model.
If you want to land on the moon, you use science, even if it doesn't know everything down to the last particle.
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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
However, for a freelance programmer like me who has to model the world the client wants, this might actually be a useful problem. LOL
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
This applies way beyond the sciences. So many people now think they understand something because they can prompt an AI to give them the right answer. Thats literally this same illusion just with a new interface on top. Getting correct outputs isnt understanding.
Is it a probability that the authors understood the notion of Understanding all wrong?
;).
this is extremely long and repetitive.
"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?
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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.
Science uses maps of metaphors to cover observable space. Math is one of them.
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.
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.
So, to summarise, consistency is the virtue of a narrow mind?
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
Popper writes the philosophy of science in a Platonic micro-descriptor fetch, which is 20:20 recursion.
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)
Exactly. The lede buried here is, as you say,
Which has a statistician counterintuition Therefore (in my understanding) Maybe understanding should be related to wisdom rather than intelligence? Like Socrates. AGW?Explained by this wonderful series
> This reminds me of the statistician's aphorism: 'All models are wrong, but some are useful.'
It reminded the authors of this too, since they quote and source it
Yes, but isn't since exactly about those models? If you want to calculate how much that steel truss is going to bend when loaded, you need basic mechanics. Sure you could go deeper and think about what actually happens to the metallic structure on an atomic level, you could think about the whole thing in relativistic terms, etc. But this is not going to give you a better bridge.
More accurate theories are important once your requirements are so extreme that without them your prediction is off.
Understanding is about knowing these mental models at the different levels, how they connect to each other and where these models have weird gaps and/or disconnects. Since is and always has been about understanding the best current explaination of the things we observe. Whether it is exactly as you say, or some more elaborate hidden structure is beneath it, is not something you can tell apart, unless you run into the actual limitations of your model.
If you want to land on the moon, you use science, even if it doesn't know everything down to the last particle.
[dead]
[dead]
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
However, for a freelance programmer like me who has to model the world the client wants, this might actually be a useful problem. LOL
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
This applies way beyond the sciences. So many people now think they understand something because they can prompt an AI to give them the right answer. Thats literally this same illusion just with a new interface on top. Getting correct outputs isnt understanding.
Is it a probability that the authors understood the notion of Understanding all wrong?
;).
this is extremely long and repetitive.
"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?
Do you have an extension which automatically skips cookie banners? Try disabling it for this site.
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.
Science uses maps of metaphors to cover observable space. Math is one of them.
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.
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.
So, to summarise, consistency is the virtue of a narrow mind?
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
Popper writes the philosophy of science in a Platonic micro-descriptor fetch, which is 20:20 recursion.