How much of the world is it possible to model?
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"About six inches to the mile."
""Only six inches!"exclaimed Mein Herr. "We very soon got to six yards to the mile. Then we tried a hundred yards to the mile. And then came the grandest idea of all! We actually made a map of the country, on the scale of a mile to the mile!"
"Have you used it much?" I enquired.
"It has never been spread out, yet," said Mein Herr: "the farmers objected: they said it would cover the whole country, and shut out the sunlight! So we now use the country itself, as its own map, and I assure you it does nearly as well."
(From Sylvie and Bruno Concluded by Lewis Carroll, first published in 1893.)
I wonder whether the above is applicable to modeling the world and its behaviour in other ways; fully understanding everything of the complexity of the world would require the power of the world itself.
To take a semi-long term example. If you go carve your name in a tree, when you come back 50 years later it better show your name distorted over those 50 years. If you cut open the tree. It better show a record of every weather event over those 50 years. Every piece of the world you inspect will need the simulation run and not just for it but for all the things that affected it and all the things that affected the things that affected it etc etc recursively until you've simulated every in the universe.
Minecraft is an example of the latter: if you leave a chunk, mobs despawn and decay and growth stops.
But a simulation need not work that way. E.g. instead of continuing to run a simulation, it can simulate macro-indocators, and continue to simulate micro-details with a strong bias towards reverting to a prediction from the macro-indicators, and each element that gets there can be discarded.
To go back to the Minecraft example, you set a "decay rate" for mobs that reflects a believable rate of dispersal, or simulate changes to vegetation towards what your generating function will produce until they match.
Then when the player re-enters at t+a_lot, you just run your generating function where time is one parameter, and apply whatever reduced delta is still relevant.
Yes, you'd still need to simulate a lot, but you'd be able to prune most things.
So the question is meaningless without defining your requirements.
The macro characteristics of entity dynamics in the world is that of a sparse metastable graph. There is considerable compression possible at the computational level, since most entities in the world are fungible in every discernible aspect, but the data model is not similarly reducible because what can be compressed away is context dependent. Since the computation is less tractable part, this makes the modeling problem eminently viable albeit still extraordinarily difficult. To model the effects of the relatively small number of nodes and entities that can shift the equilibrium you need to compute over the true graph, not some aggregate and summarized roll-up of the graph. There is a real tension between anonymization/privacy (and reducing computational costs) and the ability to build models of human systems that remotely reflect real-world dynamics. Models based on anonymized aggregations are notoriously rubbish when ground-truthed.
It is also important to treat these models as sparse metastable graphs. I am always slightly horrified by the number of serious academic disciplines that often default to modeling human systems as if they were a computational fluid dynamics (CFD) problem. Global human systems don’t have CFD-like structure but CFD is attractive because it is easy to reason about computationally and widely understood. A lot of scientific research on models of human dynamics (e.g. epidemiology) that were ultimately debunked had a root error in the use of CFD-like assumptions about how humans interact and behave.
It is technically possible to build a far higher fidelity model of physical world dynamics than I think most people would expect or intuit. Unfortunately, that is not the hardest part of making it useful.
…In that Empire, the Art of Cartography attained such Perfection that the map of a single Province occupied the entirety of a City, and the map of the Empire, the entirety of a Province. In time, those Unconscionable Maps no longer satisfied, and the Cartographers Guilds struck a Map of the Empire whose size was that of the Empire, and which coincided point for point with it. The following Generations, who were not so fond of the Study of Cartography as their Forebears had been, saw that that vast Map was Useless, and not without some Pitilessness was it, that they delivered it up to the Inclemencies of Sun and Winters. In the Deserts of the West, still today, there are Tattered Ruins of that Map, inhabited by Animals and Beggars; in all the Land there is no other Relic of the Disciplines of Geography.
— Suarez Miranda, Viajes de varones prudentes, Libro IV,Cap. XLV, Lerida, 1658
This is actually a solved problem now with Augmented Reality, whereby you can walk in some field and get an overlay with the additional layers you care about at that moment.
I heard one argument against Climate Change being that it is 'just an estimate', that reality is more fine grained. How can you trust a model since the model is just an estimate of reality that leaves out a lot of details? They were calling into question all models, in order to debunk Climate Change. Not sure they realized how much of the technology is based on models.
https://www.climatehubs.usda.gov/hubs/northwest/topic/basics...
Or more to point, it isn't like there was one-run, we take that as gospel, then run on past data, and failed, so models are wrong forever. They are always being refined.
And some of the longest range calculations, that were done decades ago, are matching. So what model are you specifically saying failed?
Now a new evaluation of global climate models used to project Earth’s future global average surface temperatures over the past half-century answers that question: most of the models have been quite accurate.""
https://climate.nasa.gov/news/2943/study-confirms-climate-mo...
Once a year the New Yorker rings up Dan Rockmore and asks him to try and explain something to do with mathematics to people who have tweed mousepads and a Haruki Murakami screensaver.
Sticking knives in your eyes, sitting at the bottom of a dark well, and completely trashing your place of residence but leaving a few beers untouched all work well.
They’re not bold faced lies because it’s really the best we have about consistently explaining reality but I think we should let kids understand that even the latest and greatest reduction theories of reality won’t unlock truth everywhere, just allows you to peek in at some places and perform some degree of interpretation and prediction. It’s still quite valuable but we need to stop telling everyone we have the answers because we don’t. We almost masquerade around like a religion to some degree and people use that in the same way, “well science says” or “according to this data and paper” —- well yea, maybe, but also probably not.
How did we go from:
Our simplified model Ignored friction.
to
We have no idea what is real, throw away all technology and lets live in the trees.
Just search 'anti-science' in the US.
There are literally hundreds of articles, and studies slicing the data by any metric you desire.
How can you have missed the US turning against science. It pre-dates Covid by a few years, and Covid just ramped it up. We are having measles outbreaks. Polio is coming back. The US is turning more towards a religious Fundamentalism, and science just doesn't fit their world view.
(of course, i'm assuming you were making a comment about my internal model being that the US is turning anti-science, and based on models being imperfect, maybe i'm over-stating it.)
And marvel at the pseudoscientific claims, that purely coincidentally get a free pass from science.
Don't forget though: the claim is "We have no idea what is real, throw away all technology and lets live in the trees". I am anti-science, but I am not the strawman described above - I wonder how many others are like me, and I also wonder how many in the pro-science camp actually care about such details, despite how important interpersonal relations are when it comes to people's beliefs (something fairly well studied by science, ironically).
> It pre-dates Covid by a few years, and Covid just ramped it up.
Was it the virus itself, or might it have been the mass psychological phenomenon surrounding covid that got people all worked up?
> We are having measles outbreaks. Polio is coming back. The US is turning more towards a religious Fundamentalism, and science just doesn't fit their world view.
That's right! I recommend scientists take off their fundamentalist hats and put their science hats back on, lest even more misfortune befall them, and us.
> (of course, i'm assuming you were making a comment about my internal model being that the US is turning anti-science, and based on models being imperfect, maybe i'm over-stating it.)
"I have a bee in my bonnet" is probably a decent description of my ire lol. Joking aside though: I am literally serious about what I say, and I propose it is very far from guaranteed that I do not have a valid and causally important point.
They developed a measles vaccine. Measles was almost eradicated. Deaths gone.
Anti-Science proponents stopped taking the vaccine.
Those people started getting sick with Measles again. And dying.
That seems pretty clear. A->B.
You don't have to invoke any mystical aspect of a belief in science that is causing misfortune.
Same with Covid. Red states/counties that are more anti-science, did not take as much Covid vaccine. Their per-capital adoption was lower, and they had more sickness and death per-capita.
You can graph it and look with your own eyes. No mystical beliefs happening.
Not in that text, but you cleverly avoided addressing my actual complaint, which I indicated by quoting the specific text of yours I was agreeing with before posting my disagreement.
There is a mystical belief here though:
>> What is up with people throwing out science these days?
>> How did we go from:
>> Our simplified model Ignored friction.
>> to
>> We have no idea what is real, throw away all technology and lets live in the trees.
I asked in response:
> How many people actually do this though, as opposed to how many people's models contain other people doing this?
Your reply:
>> Was going to add some citations, but really there are too many.
>> Just search 'anti-science' in the US.
>> There are literally hundreds of articles, and studies slicing the data by any metric you desire.
Here you are speaking as if ALL returned hits (ALL instances of anti-science) match your prior framing: "We have no idea what is real, throw away all technology and lets live in the trees."
So, you dodged my criticism, while representing that science, and pro-science thinkers, are better at thinking...while demonstrating otherwise.
That most any pro-science person I engage with tends to follow this methodology is a part of the reason for my anti-science stance: the combination of deceit/disingenuousness combined with implied superiority (times the percentage frequency of this behavior in my experience) bothers me.
Because scientist test their theories.
If the test fails, then that become part of the known false things.
If the test passes, then they become known true.
There are no ''things that are believed true because of science as some pseudo religious belief'', they must all be proved true.
So to be anti-science, you are automatically saying that you would ignore true results. That truth does not matter. Or be willing to believe false things.
This happened a lot during Covid. 'Scientist' would offer very narrow results on exactly what was tested and known. And people really didn't like that, and either wanted to read too much into the results, or mad the results conflicted with their belief.
And not sure how to? Where to start?
Sure, scientific research has its issues.
Models aren't always as exact as 'reality'.
Testing methodologies could be flawed.
Various assumptions could be incorrect. And even proved wrong in future testing.
The point is, it is a process, and all results can be re-evaluated, re-tested.
But nobody is believing something that was proved false. Something was proved false, but "I'm going to ignore the science and just believe what I want". Which is what a lot of Covid and Climate conspiracy theorist do.
Yes, which is consistent with the scientific method, by definition at least.
> And not sure how to? Where to start?
I have a very long list of ideas, the vast majority of which I've borrowed from various domains, science being one of them (the very idea of the scientific method, abstractly, is very powerful, and can be applied in a variety of ways over and above what science currently does).
> Sure, scientific research has its issues.
> Models aren't always as exact as 'reality'.
Agree, but there is a true/false binary representation, and then there is the degree to which these things are true, and more! This tends to not be of interest though.
> But nobody is believing something that was proved false.
I am opposed to practicing logic using technically misinformative figures of speech like this.
> Something was proved false, but "I'm going to ignore the science and just believe what I want". Which is what a lot of Covid and Climate conspiracy theorist do.
Agreed[1], but here is something everyone does: believes that what seems true, is true. Our culture teaches us to think in binary, which is a dangerous form of logic when applied to non-binary domains, like reality.
[1] Though, how much worse are they, in fact, compared to those who suffer from normative cognition? And perhaps even more important: how many individuals are in each set? Which set causes more harm? Where should our critical attention be?
I agree, not everything is always binary True/False, and culture can influence these. But in todays world, it is hard to discuss something with a lot of gray area.
Also. Seems like you might be playing at language games some. Really, if we pick everything apart enough, nothing means anything.
See the distinction between murder and manslaughter (is it me who is playing language games?).
> Really, if we pick everything apart enough, nothing means anything.
Which seems counterintuitive to me, since science's ability and willingness to dig ever deeper (even years after any success has been achieved) is what sets it apart from most other disciplines. What's funny is that the very same methodology applied to the metaphysical realm can cause opinions on whether that is a good idea to flip, as is happening here and is getting me into trouble.
Catholicism even celebrates various “mysteries,” which are things like the trinity which they do not think can be understood fully by mortals.
this vagueness is a feature of surviving religions the more details you add the more your predictions become falsifiable. this leave room to theologians to speculate and when they are wrong it's not the religions that false it's the person who made that specific inference and when it's right, well it was already written.
this is not an /r/atheism checkmate post just pointing out the evolutionary dynamics, since even if there was a religion that described things in great details and all of it were true the people at its inception would just violently reject it without expanding efforts to verify the claims, since they don't have the means.
for example image you go thousand(s) years in the past and preach the standard model (lets ignore all the pesky details about how you'll probably die or become a slave regardless of ethnicity since you have no social backing in the era).
Gates, perfection, eternal peace and happiness, etc. but the catch-all for modeling reality or beyond reality in most religions tends to be that the supreme deity fixes or resolves any inconsistency or unknown. So, the afterlife is whatever your supreme deity decides and makes it, you just don’t know about it. It’s modeled (which is partly why it’s so successful) it’s just that you may not know the details explicitly, a sort of black box model you don’t get to peek inside of.
"An unbroken description of reality would be simultaneously the truest and most useless thing in the world, and it would certainly not be science. If we want to make reality and therefore truth useful to science, we must do violence to reality. [...] In nature, everything is equally essential. By seeking out the relationships that seem essential to us, we order the material in a surveyable way at the same time. Then we are doing science."
I appreciated how this quote emphasizes that science and modeling are inextricably connected.
I've sort of increasingly suspected that some domains might involve an intrinsic amount of stochasticity or uncertainty that is baked into things. As in, an amount that becomes nonignorable relative to what you're trying to predict.
There's some math and comp sci papers that basically argue (in a proof sense) you there must be a limit to modeling. It's been awhile since I read the papers but my recollection is that the modeling process itself creates (necessitates?) a certain gap between the model and the real world that creates hard boundaries. For example, if the modeling step requires an amount of time A, some information is lost in that time.
These are a couple of the papers:
https://arxiv.org/abs/0708.1362 https://www.jstor.org/stable/10.4169/amer.math.monthly.121.0...
I suspect as the system you're trying to model is more complex or has more dependencies it gets worse.
The math-based decomposition of non-convex processes into coverage using multiple convex models presumes that the span of the full model is appropriately segmented and conditioned. Its sanity check is to reassess those segments and conditions periodically. But do we have comparable means of revalidating ML-based (induction) models, given our lack of understanding of their black-box innards? Given that AI models can and will fail in time due to model AND data disintegration, must we simply wait until each AI model's prediction accuracy goes to hell to then build an all-new ML model and renewed dataset -- experimentally starting over from scratch each time because we never understood the limits of the AI model and its dependency on its training data to begin with?
Lewis Mumford for instance in The Myth of the Machine all the way in 1970 wrote much better about the limitations of this way of thinking, that didn't do the heavy philosophizing of Baudrillard.
The fact that models are necessary constructs, but are merely heuristics was one of Kant's main riffs (as much as I could understand), so maybe we've actually gotten worse at perceiving the limitations of models since we've lost touch with the real world over the last decades.
The only problem with living so far in the future is when you get back to reality its all repeats.
The doctor strange scene in avengers was pretty realistic for those who get access to the tech
https://m.youtube.com/watch?v=eGKPfZTXHsc&pp=ygUaYXZlbmdlcnM...
Now try to simulate the entire planet and you end up missing a ridiculous amount of variables and ignoring complex feedback loops.