chaos theory
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Woody's a Girl Vox 1: "Okay, okay," I tell my subconscious "I k…


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Most interesting comment from YouTube:

@rybec

Ok, so chaos theory is fascinating, and this video does a great job of explaining it.

If you want to go one better though, and simulate how quantum randomness can affect macroscopic effects over time, read on!

Back when I was a CS undergrad, some students in the CIT department suggested I talk to one of their professors about simulation, because I had a habit of writing particle simulations for fun. After around two years of this, I finally did it. At the time, I was planning to write a swam simulation in Haskell. He gave me some interesting advice. He suggested I not worry about state. Normally, in simulations like this, one would use a buffering technique, so that the state currently being generated is based purely on the previous state. If you don't do this, early changes can affect later changes, blurring state between frames. I took this advice to mean that I shouldn't concern myself with this, so I wrote a simulation that advances each particle based on wherever the others happen to be _at this moment_, instead of buffering state. The result was that behavior of the simulation was far more organic, lacking artificial looking patterns that tended to show up in the state buffered simulations I had previously written.

This isn't the really interesting part though. Another thing I did to achieve this indeterminate state was to based advancement on time passed, rather than progressing a set amount per "frame". Frames really only made sense when trying to preserve state integrity, and since I wasn't doing that, I fell back to an older method I used to use when writing video games.

So, here is how the simulation worked: I started with a list of particles. During each loop, I would advance the first particle in the list, based on the amount of time passed since I had last advanced that particle (and based on the positions of the other particles in the list). Then I would move that particle to the end of the list. (Using functional programming techniques in Haskell make this extremely easy.) This completely abandoned state integrity, which did manage to achieve very interesting results.

The most interesting result, however, was what happened with, on a whim, I ran two identical instances of the simulation side-by-side. Starting them at exactly the same time randomly placed the particles in the same places. To be clear, this is the only place in the simulation where randomness was used, and because the RNG was seeded based on system time, starting them simultaneously seeded them identically. So, they started with particles in exactly the same positions, and the particles then started moving in exactly the same patterns. Except, imagine my surprise when I noticed the simulations begin to deviate. They started in identical states. There was no additional randomness going into the simulations. So they should have matched perfectly.

That final assessment, however, was actually wrong. There was still some tiny amount of randomness going in. This randomness was processor scheduling. The difference between the two simulations was the time passing between iterations. Desktop operating systems assign processor cycles to programs using some algorithm. How cycles are assigned depends on a lot of factors, including how many processes are waiting for CPU time, the priority level of those processors, how much input and output is being generated, and so on. This means that even running the same program twice, at the same time, won't give them identical schedules. Anyhow, in the context of the simulation, this means that while each particle started in exactly the same state as its parallel in the other instance, it didn't always take exactly the same amount of time between iterations, and this allowed tiny differences due to floating point error to work their way in. But, modern processors are extremely fast, so the actual differences in time were infinitesimal, barely big enough to make any difference in the floating point time values being generated, and further, the floating point error created by these slight difference were also infinitesimal. So how, after only a few seconds, could deviation be seen?

The answer is chaos theory. One might be tempted to suggest that the tiny differences would add up to big ones, but the truth is, the tiny difference should be expected to just average out. If we are basing progression on time, the differences do average out. The cumulative time that has been applied to any particle will always be within a few milliseconds of the total time passed since starting the program. And floating point error doesn't tend more toward one direction than another, so over many iterations that will also average out.

So what was actually happening was that tiny differences (perhaps on a scale closer to quantum than macroscopic) produced from effectively random influences were having the impact of chaos. Maybe an easier way to think of this is considering each iteration as a "starting condition" for all future iterations. So, the first iteration has some infinitesimal difference between the two simulations. That difference, while initially imperceptible, results in growing deviation between the simulations. And this happens _on every iteration_.

Now, this might seem like some merely interesting theoretical stuff, but it's far more than that. What degree of impact does quantum randomness have on the macroscopic world? It's easy to write it off as having literally no impact, except when we are deliberately measuring quantum effects and acting based on them. Chaos theory suggests otherwise though, and my simulation demonstrates exactly how even infinitesimal differences being added into the system on a constant basis can have a significant impact in even a fairly short period of time.

So the truth is, not only would the future be unpredictable even if it was 100% deterministic, due to chaos, our universe isn't 100% deterministic, because the various sources of quantum randomness are constantly injecting random new information into the universe, and this means that even if we could know the initial conditions with infinite accuracy and precision, we still couldn't predict the future, because tiny state changes are constantly filtering in, and those are injecting new tiny differences that chaos will eventually amplify into enormous differences.



All comments from YouTube:

@andrejferdinand388

Having a bad math teacher at very young age, has the butterfly effect on the rest of your life; for example

@victorguzman4101

"The printer rounded to 3 decimal places whereas the computer calculated 6"


The ghost of significant figures

@premsagar8253

Having a bad math teacher at very young age, has the butterfly effect on the rest of your life; for example

@aadarshraghuwanshi7022

this hit me hard.

@Ira__L

At school I was really good at math until there was a very irritable algebra teacher with anger management issues. Her explanations were super short, if you zone out for several seconds - congrats, you understand nothing. I did ok, but I learnt to lay low. In high school and university I studied foreign literature and languages. When I was 20 - 21, during university practice, I went to my school to work with an English teacher, and I was assigned to this one class of pupils. Once I sat through their algebra lesson with that wonderful teacher. I think she didn't remember me but she felt OK to start shouting at these poor kids and shaking a dirty blackboard sponge right in their faces to make them think faster, I guess. She had also retained another beautiful habit of hers - gesticulating with both hands, but with one hand she would hold a piece of chalk, with another one - her glasses. She would use all her fingers except the middle ones, and she would raise both of her middle fingers and shake her hands to emphasize her point. She would basically give the double middle finger
to the whole class😂

@Ckdude100

Honestly that’s just an excuse and I’m sorry. My friends had the same poor math teacher at a young age for a good amount of years. One went on to be brilliant at math while the other was mediocre at best

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@mohibullah6215

He explained such a complex topic with so much simplicity that i am just speechless.

@hugoclarke3284

It IS simple. People can't help but complicate things.

@MouseGoat

@@Arrowaceofspades but hes right.
In fact everyting is simpil when looked at from the right angle.
Just look at flatwaters overcomplicating the world because they cant grasp the fundmetals of gravity. Stupid is when you over complicated things you dont undstand because you applying the wrong models to the data.

@hugoclarke3284

@@Arrowaceofspades Reading into my comment like that is only making a case for my point. Humans need to translate things into a language they can understand. My comment would sound pretentious to anyone, myself included, but it is entirely objective.

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