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- Hacker News
- got me at "Most often scientists believe they understand more than they do, making their belief an illusion." but why is it still bothering me? 1. feels unfalsifiable in spirit 2. somewhat restates "all models are wrong, but some are useful" less cleanly 3. doesn't really offer like, what can we do as science people? tomorrow morning perspectiveby galsapir
- 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?
by pazimzadeh - Some of the figures are downright atrocious quality and are found in better quality on Wikipedia. It’s almost an insult to paying subscribers and contributors of Springer.
- Do you have an extension which automatically skips cookie banners? Try disabling it for this site.by in_a_hole
- 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.by skyberrys
- 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.by satisfice
- 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.
- Is it a probability that the authors understood the notion of Understanding all wrong?
;).
by dbacar - You jest but are right.
There is nothing new in the article and has already been covered well by some of the greatest Scientists/Mathematicians. We must be careful that articles/papers like these are not used by the anti-scientific crowd to promote their talking points and agendas.
Notably, Henri Poincare (https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9 and https://henripoincarepapers.univ-nantes.fr/en/) wrote three philosophy of science books; viz. 1) Science and Hypothesis 2) The Value of Science and 3) Science and Method.
These were published together under the apt title, The Foundations of Science which is available here - https://www.gutenberg.org/files/39713/39713-h/39713-h.htm and here (ebook versions) - https://archive.org/details/foundationsscie01poingoog
Details of the works;
1) Science and Hypothesis (1902) - https://en.wikipedia.org/wiki/Science_and_Hypothesis
2) The Value of Science (1904) - https://en.wikipedia.org/wiki/The_Value_of_Science
3) Science and Method (1908) - pdf at https://henripoincarepapers.univ-nantes.fr/chp/hp-pdf/hp1914... At the minimum read this completely.
See also;
a) History of Scientific Method - https://en.wikipedia.org/wiki/History_of_scientific_method
b) Scientific Method - https://en.wikipedia.org/wiki/Scientific_method
by rramadass - Who is this for? This section, which is fairly close to a central thesis, contains no citations:
> Illusions of understanding can take several (overlapping) forms. Some that are commonly encountered are: (1) Illusions of explanatory depth (we think we personally understand things in more detail than we do). (2) Illusions of explanatory completeness (even if we don’t think we fully understand it ourselves, we think the best experts do). (3) Illusions resulting from understanding something other than the goal (e.g. we believe we understand the formation of memories because we understand the anatomy of the brain site, the hippocampus, that is needed for such learning). (4) Illusions due to simple statements giving a feeling of insight (such as when tautological statements seem insightful because they are framed in a reductionist manner). (5) Illusions (as described earlier) that one understands the cause of phenomena because there exists a model or procedure that predicts well. (6) Illusions of causal strength (attending to an observed relation makes one believe the causal connection is stronger than it is). (7) illusions that one can describe causes simply. (8) Illusions by the explainer that the recipient understands what the communicator intends. (9) Illusions by the recipient of an explanation that the communicator understands well and that the explanation is correct and complete.
Are we to infer that these observations are unsupported by evidence? Are we to assume that the research work is so poorly constructed that they did not do research to find evidence of the existence of the classifications in existing research?
by smarm52 - The authors of this paper have not studied what historians and philosophers of science have written. They just use 'induction', 'validity', etc. They reinvent the wheel. They write "Of course the validity of that induction depends on a host of other assumptions.". Duhem-Quine thesis is better than this way of formulation, as the latter doesn't use 'validity'.
If authors ever come to this forum, please read Duhem-Quine thesis, over/under determination, inference to the best explanation, Goodman's paradox, also how various theories in philosophy of sciences: from Popper to Kuhn, Lakatos, Laudan, etc.
by raincom - More predictive power is always a good goal, full stop. This is orthogonal to whether the model producing prediction helps with "understanding" directly. Predictability encodes understanding in a strict information theoretic sense, regardless of our ability as humans to access that understanding.by chermi
- > More predictive power is always a good goal
But in some cases it is not good enough. If you look for a better explanation and chose gradient descent as your strategy, then you'll come to a local maximum eventually, but not for another explanation.
Arguably, it is hard to look for better explanation if the current one doesn't have a backtrack of failed predictions. One of the possible ways out of this situation is to search for the predictions that fail.
But what I want to say is explanations are not just for prediction. They are needed to build a mental model that then can drive the research. And new model can be built (theoretically) from the first principles. I can't find clean examples for it though. If we look at Einstein for example, he started with a failure to predict. But what he came up at first was Special Relativity which failed utterly with the gravity. Einstein spent like 10 years rewriting gravity to make it work with SR? Failed predictions of his new shiny theory didn't stop him, and it is considered to be good.
> Predictability encodes understanding in a strict information theoretic sense, regardless of our ability as humans to access that understanding.
But it doesn't necessary implies the possibility to move forward. I'm not sure if an analogy with compressed data is a good one, but you don't work with compressed data, you unpack it, and maybe unpack some more and convert to a very inefficient format with regard to the disk space used.
Compressed theory is good to apply it as is, but to refine it you should probably prefer something else.
by ordu - It's not arguing that predictive power is bad. Just that people often mistakenly believe some phenomenon is understood more deeply than it really is, because a model can fit data and generate accurate predictions.by zigzag312
- It's funny when you think you know something pretty much thoroughly. Then you learn a bit more and realize that your understanding was a bit simplified, had gaps or there was a whole other level to it.
The feeling is a strange mixture of disappointment, awe, annoyance and excitement.
- Y'know it's funny how, at least in my experience, education worked.
We're handed a bunch of simplified models then build on them. The consequence being that the landscape is very narrowly revealed.
This itself is a consequence of the architecture, at least in the US we don't really specialize until college/university.
Nobody really comprehends the depth of things until then, and troublingly enough we don't understand that until we're 2-3 years in.
For instance we have DNA, right? It gets replicated in cells via mitosis and in specialized cells called gametes via meiosis, the latter form is used for sexual reproduction. That's the gist you get in High School, you might talk about the polymer, nucleotides, nucleotide construction (which are superficial)
That isn't even close to the end, just off the top of my head there are elements in the DNA that code for several processes like methylation, allow interfacing with proteins which have myriad effects like up or downregulating transcription, they can also change the local topology forming loops that facilitate the process. There's a bunch of crazy shit that happens at the histone level.
And any one of those Substituent parts is probably worth discussing for a week in lecture at least but they're glossed over for more advances classes. For instance those regulatory molecules, in sufficient concentrations, can overpower histones that keep genes turned off, and that has downstream consequences in general regulation and cell differentiation.
I think that in the long run it compromises mental rigor. It also allows people to carry with them a sense of complete understanding when it's superficial and shallow. Having never experience the depth that the real world goes to kind of limits the horizons of people that aren't naturally curious and never shows them the potential for how deep shit can really get and when it does it is still superficial by the dint of the expectation placed on students being so narrow.
by brnaftr361 - It also leads to a carefullness and lawyerization of language, that the laymen mistake for a lack of confidence.
"All evidence points towards x under the constraints y, z and q."
vs
"Its like this: x"
by warumdarum - 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
- You are just making the mistake of using two incompatible definitions of the word 'knowledge' here and then acting like it's a contradiction.by ltbarcly3
- 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
by ian_j_butler - However, for a freelance programmer like me who has to model the world the client wants, this might actually be a useful problem. LOLby jdw64
- 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)
by jdw64 - Seems like a trivial realization written about many decades ago. Join the church of instrumentalism, and just live with it as a fact of daily life. Focus on your predictions and mental models of the world, hone them, and that's about it.by abc123abc123
- Exactly. The lede buried here is, as you say,
Which has a statistician counterintuitionaccurate prediction is not better understanding
Therefore (in my understanding)Less "accurate" model can lead to better prediction
Maybe understanding should be related to wisdom rather than intelligence? Like Socrates. AGW?A better understanding encodes more info about how much more it can be improved, when compared to a less good understandingExplained by this wonderful series
by vi_sextus_vi - > The paper argues that just because a model predicts future values more accurately, it doesn't mean the model explains the actual causal structure.
Yes. Celestial navigation was based on a universe which spun around the earth, which is wrong, but it worked for navigation.
by lelanthran - > 'All models are wrong, but some are useful.'
And beyond that: models become most interesting at the point they fail, because that's where you learn something.
by pfdietz - 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.
by atoav - > 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
by ian_j_butler