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Speaker 1: Hey, everybody.

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Speaker 2: Chuck here and welcome to our sciencey playlist. Super excited

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Speaker 2: about this one, and I'm going to kick it off

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Speaker 2: everybody with this episode on how chaos theory changed the universe.

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Speaker 3: Welcome to Stuff you Should Know from HowStuffWorks dot Com.

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Speaker 4: Hey, and welcome to the podcast. I'm Josh Clark with

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Speaker 4: Charles W. Chuck Bryant, and there's Jerry over there. So

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Speaker 4: this is Stuff you should Know, the podcast about chaos theory. Like,

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Speaker 4: have you ever seen Event Horizon?

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Speaker 1: I did not bad?

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Speaker 4: Great movie?

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Speaker 1: Are you crazy? Do you think it was great?

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Speaker 4: Oh? It was so imaginative.

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Speaker 1: I thought it was okay.

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Speaker 4: It was like a LOVECRAFTI and thing in outer space. Yeah,

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Speaker 4: I loved it.

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Speaker 3: It was all right.

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Speaker 4: I love crafted it.

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Speaker 3: Yeah, I liked it.

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Speaker 4: That's what I think of when I think of chaos.

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Speaker 4: You know, there's that one part where they kind of

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Speaker 4: give you like a glimpse behind, like the dimension that

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Speaker 4: this action is taking place in, to see the chaos underneath.

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Speaker 1: And I should check that out again.

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Speaker 4: Yeah.

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Speaker 2: I think about Jurassic Park and Jeff Goldblum as the

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Speaker 2: creep doctor Malcolm explaining chaos in the little auto driving.

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Speaker 1: Suv or whatever that was.

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Speaker 4: Yeah, that's what I was calling the script, the auto

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Speaker 4: driving suv scene.

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Speaker 2: Yeah, and you know what, he actually rewatched that scene

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Speaker 2: and it confirmed two things. One is that he actually

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Speaker 2: did a pretty decent job for a Hollywood movie of

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Speaker 2: a very rudimentary explanation of chaos.

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Speaker 4: And you watched it for this.

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Speaker 2: Yeah, yeah, just that scene. And then it also confirmed

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Speaker 2: of what a creep that character was. Yeah, if you

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Speaker 2: watch that scene, he's like, you know, he was all

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Speaker 2: gross and flirty with her right in front of her

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Speaker 2: ex but there's this you know, he's talking to her.

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Speaker 2: I didn't even know notice this at first. He like

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Speaker 2: he just like touches her hair out of nowhere for

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Speaker 2: no reason. Really, He's just talking to her and he

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Speaker 2: just like grabs her hair and touches it.

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Speaker 1: Hu And I'm like, what a creep.

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Speaker 4: I know, if you look closely, you can see the

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Speaker 4: hormones emerging through his chest hair.

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Speaker 1: Yeah, it's groady, And I love Jeff Goldblum. It's not

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Speaker 1: a reflection on him.

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Speaker 4: He was basically doing Jeff Goldbloom.

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Speaker 1: Well that's what he Yeah, sure he's Jeff Goldbloom, but

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Speaker 1: I don't.

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Speaker 2: Think that's how in the manner in which he speaks.

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Speaker 2: But I don't think he's a creep, do you.

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Speaker 4: Wow, I've got nothing against Jeff gold Okay, I think

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Speaker 4: he's a I think he's doing Jeff Goldblum.

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Speaker 2: It was also a sign of the times, like if

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Speaker 2: that movie were made today, doctor.

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Speaker 4: What was her name in the movie, at least Satler,

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Speaker 4: I think.

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Speaker 2: Yeah, doctor Satler would be like, it's very inappropriate to

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Speaker 2: stroke my hair, dude.

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Speaker 4: Yeah, like, don't touch me, right.

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Speaker 1: But this was the nineties or yeah, nineties. Free Wheeling

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Speaker 1: was eight, No, it was nineties.

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Speaker 4: It was the early mid nineties, I think, yeah, ninety

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Speaker 4: twee ninety four. The book came out in nineteen ninety

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Speaker 4: and in the book Ian Malcolm, who's a Chaotician.

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Speaker 1: Yeah, a cre chaotian, right he.

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Speaker 4: He goes into even more depth about chaos there. But

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Speaker 4: that was I mean, that was the first time I

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Speaker 4: ever heard of chaos theory was from Jurassic Part Yeah,

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Speaker 4: me too, probably, and it really it was really misleading.

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Speaker 4: I think the entire term chaos is very misleading as

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Speaker 4: far as the general public goes as from what I

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Speaker 4: researched in this for this article.

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Speaker 2: Well, yeah, I mean you hear the word chaos as

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Speaker 2: an English speaker and you think frenetic and crazy.

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Speaker 4: Out of control.

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Speaker 1: Yeah, and that's not what it means in terms of

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Speaker 1: science like this, right.

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Speaker 4: What it means, I guess we can say up front

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Speaker 4: is basically the idea that complex systems do not behave

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Speaker 4: in very neat ways that we can easily grasp, understand,

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Speaker 4: or measure.

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Speaker 2: Right, and not even simple systems don't. Sometimes it doesn't

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Speaker 2: always have to be complex. But I want to give

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Speaker 2: a shout out in addition to our own article to

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Speaker 2: when you know, when it comes to stuff like this,

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Speaker 2: the brain breaking stuff.

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Speaker 4: For me, man, this was a brain breaker.

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Speaker 2: You know. I always go to like blank blank for

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Speaker 2: kids because it always helps.

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Speaker 4: If there's a dinosaur mascot on the page, it's a

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Speaker 4: sure thing we can understand it.

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Speaker 2: But the best explanation for all this stuff that I

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Speaker 2: found on the internet was from a website called a

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Speaker 2: barm Aba Rim Publications, which turns out to be a

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Speaker 2: website about biblical patterns and sandwiched in the middle, there

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Speaker 2: is a really great, easy to understand series of pages

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Speaker 2: on chaos.

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Speaker 3: They're nice, So.

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Speaker 2: I was like, man, I get it now. I mean

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Speaker 2: in a rudimentary way.

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Speaker 4: Right, Well, yeah, yeah, I think even a lot of

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Speaker 4: people who deal with that display chaotic behavior, which I

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Speaker 4: guess is to say basically all systems, eventually, under the

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Speaker 4: right conditions, Yeah, don't necessarily understand chaos.

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Speaker 2: Yeah, And they defined a complex system as specifically. It

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Speaker 2: doesn't mean just like, oh it's complex, I mean it is, right,

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Speaker 2: but specifically they define it in a way that helped

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Speaker 2: me understand it's a system that has so much motion,

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Speaker 2: so many elements that are in.

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Speaker 5: Motion, moving parts.

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Speaker 2: Yeah, that it takes like a computer to calculate all

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Speaker 2: the possibilities of like what that could look like five

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Speaker 2: minutes from now, ten years from now. So before computers

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Speaker 2: came around, before the quantum mechanical revolution, it was it

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Speaker 2: was a lot more basic.

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Speaker 1: It was like what comes up must come down, stuff

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Speaker 1: like that.

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Speaker 4: Let's talk about that, Chuckers, because when you're talking about

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Speaker 4: chaos theory, it helps to understand how it revolutionized the

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Speaker 4: universe by getting a clear picture of how we understood

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Speaker 4: the uni versus leading up to the discovery of chaos. Right, So,

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Speaker 4: prior to the the scientific revolution, everybody was like, oh,

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Speaker 4: well it's God, the Earth is at the center of

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Speaker 4: the universe and God is spinning everything around like a top. Right, Yeah,

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Speaker 4: it was all a theistic explanation. Then scientific revolution happens

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Speaker 4: and people start applying things like math and making like

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Speaker 4: mathematical discoveries and and figuring out that there are there's order.

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Speaker 4: They're finding order in patterns and predictability to the universe

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Speaker 4: if you can apply mathematics to it.

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Speaker 2: Yes, specifically, if you can apply mathematics to the starting.

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Speaker 4: Point, right, right, So if you can, if you can

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Speaker 4: figure out how a system works mathematically speaking, right, you

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Speaker 4: can go in and plug in whatever coordinates you want

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Speaker 4: to and watch it go. You can predict what the

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Speaker 4: outcome is going to be. And what this is that

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Speaker 4: it's base don what at the time was a totally

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Speaker 4: revolutionary idea by Initially, I think Descartes was the first

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Speaker 4: one to kind of say cause and effect is a

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Speaker 4: pretty big part of our universe, right.

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Speaker 1: Yeah.

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Speaker 2: It was sort of like where this is sixteen hundred's

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Speaker 2: where early science met philosophy, Right, they kind of complimented

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Speaker 2: one another as far as something that's we're talking about determinism.

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Speaker 4: Right, So that was the kind of the seeds of

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Speaker 4: determinism was the scientific revolution, and like you said, where

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Speaker 4: philosophy and science came together in the form of Descartes.

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Speaker 3: Right.

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Speaker 1: Yeah.

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Speaker 4: And then Newton came along and we did a whole

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Speaker 4: episode on him.

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Speaker 1: Yeah, January of this year.

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Speaker 4: That was a good one.

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Speaker 1: It was really good.

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Speaker 4: Like I think you said in that episode that there's

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Speaker 4: possibly no scientists that's changed the world more than Newton has.

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Speaker 4: Maybe he's got.

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Speaker 2: Legs people shouted out others an email, but I'll just

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Speaker 2: say he's at the near the top for sure with

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Speaker 2: some other people.

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Speaker 4: The Cream. Yeah, so Newton came along and knew and said.

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Speaker 1: That was his name, Isaac the Kream Newton.

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Speaker 4: Right. I think anytime he dunked to be like cream. Yeah,

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Speaker 4: you just got creamed.

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Speaker 1: Oh I thought he was a boxer. He's a basketball player.

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Speaker 4: He was much more well known as a boxer, but

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Speaker 4: he definitely could dunk as a as a B baller.

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Speaker 1: Yeah.

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Speaker 4: So, man, that threw me off a.

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Speaker 5: Little bit, that's right.

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Speaker 1: The Cream.

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Speaker 4: Yeah, the Cream comes along and he basically says, watch this, dude,

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Speaker 4: does this cause and effect thing you're talking about? I

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Speaker 4: can express it in quantifiable terms. And he comes up

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Speaker 4: with all of these great laws and basically sets the

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Speaker 4: stage the foundation for science for the next three centuries

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Speaker 4: or so.

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Speaker 2: Yeah, these these laws that were so rock solid and

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Speaker 2: powerful that scientists kind of got ahead of themselves a

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Speaker 2: little and said, we're done, like done with Newton's laws.

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Speaker 2: We can predict we can predict everything if we have

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Speaker 2: a good enough beginning accurate value to plug into his equations,

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Speaker 2: and they weren't. I think there was a little hubris

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Speaker 2: and a little just excitement about like, will we figured

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Speaker 2: it all out right.

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Speaker 4: That you could take Newton's laws and if you had

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Speaker 4: accurate enough measurements you could predict what the outcome would

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Speaker 4: be of that system that you plug those measurements into

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Speaker 4: using these formula.

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Speaker 2: And at the time, a lot of this was like planetary, like, well,

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Speaker 2: we know that these planets are here and they're moving

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Speaker 2: and they're orbiting. So if we know these things, we

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Speaker 2: can plug it into an equation and we can figure

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Speaker 2: out what it's going to be like in a hundred

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Speaker 2: years exactly.

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Speaker 4: And they figured out and the basis of determinism is

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Speaker 4: what we just said, that if you have accurate measurements,

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Speaker 4: you can take those measurements and use them to predict

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Speaker 4: how a system is going to change over time using

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Speaker 4: differential equations. Right, Yeah, so this is what Newton comes

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Speaker 4: along and figures out that you can describe the universe

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Speaker 4: in these mathematical terms using differential equations. And like you said,

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Speaker 4: there was a tremendous amount of hubris, and well, I

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Speaker 4: think you said there were some hubris. I think there

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Speaker 4: was a tremendous amount of hubris where science basically said,

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Speaker 4: we've mastered the universe, We've uncovered the blueprint of the universe,

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Speaker 4: and now we understand everything. It's just a matter now

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Speaker 4: of getting our scientific measurements more and more and more exact. Yeah,

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Speaker 4: because again, the hallmark of determinism is that if you

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Speaker 4: have exact measurements, you can predict an outcome accurately, like

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Speaker 4: the pool queue example or the pool table example. Right.

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Speaker 2: Right, So if you've got a pool table, let's say

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Speaker 2: you're playing some nine ball, right, so you have that

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Speaker 2: beautiful little diamond. Yeah, set up, you got your cueball,

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Speaker 2: you put that que ball, and you crack it with

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Speaker 2: the queue. And if you are super accurate with your

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Speaker 2: initial measurements, you should be able to mathematically plot out

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Speaker 2: via angles where the balls will end up.

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Speaker 4: Right exactly, Like you can say, this is what the

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Speaker 4: table will look like after the break, if you know

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Speaker 4: the force, the angle, all those little.

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Speaker 2: Variable temperature, if there's wind in the room, sure, like

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Speaker 2: the felt on the table, like everything. The more specific

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Speaker 2: you are, the more accurate your end result will be.

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Speaker 5: Right.

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Speaker 4: And then one of the other hallmarks of determinism is

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Speaker 4: that if you take those exact same initial conditions and

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Speaker 4: do them again, the table, the pool table will look

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Speaker 4: exactly the same after the break.

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Speaker 2: Yeah, which is pretty much impossible for like a human

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Speaker 2: to do with their hands.

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Speaker 4: Sure, but the idea at the time of science was

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Speaker 4: that if you could build a perfect machine, sure that

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Speaker 4: could recreate these conditions, it will happen the same way

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Speaker 4: every time, right.

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Speaker 2: Yeah, And this, I mean this led to they had hubris,

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Speaker 2: but you could understand it when like literally in eighteen

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Speaker 2: forty six, two people predicted Neptune would exist.

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Speaker 5: Yeah, within months of that would exist.

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Speaker 1: But does exist?

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Speaker 5: Right?

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Speaker 2: And this is not by looking up in the sky

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Speaker 2: like they did it with math, right, and they were right. Yeah,

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Speaker 2: So imagine in eighteen forty six when that happens, they're like, yeah,

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Speaker 2: we kind of we've got the math down, so we're

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Speaker 2: pretty much all knowing well.

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Speaker 4: Plus also, for the most part, these not just with Neptune,

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Speaker 4: they were finding that this stuff really panned out. It

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Speaker 4: held true for everything from you know, the investigation into

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Speaker 4: electricity to new chemical reactions and understanding those, and it

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Speaker 4: laid the scientific revolution, laid the basis for the industrial

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Speaker 4: revolution and just the change that came out of the

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Speaker 4: world like that. It definitely it is understandable how science

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Speaker 4: kind of was like we got it all figured out.

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Speaker 2: Well, and like you said, they even Galileo was smart

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Speaker 2: enough to know there's uncertainty in these measurements, like the

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Speaker 2: precision is key. So they spent what does the article say,

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Speaker 2: a lot of the much of the nineteenth and twentieth

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Speaker 2: century just trying to build better instrumentation to get more

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Speaker 2: and more smaller and smaller and more precise measurements.

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Speaker 4: Right, That was like basically the goal of it, right.

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Speaker 2: Yeah, which was the right direction, and that's like exactly

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Speaker 2: what they should have been doing.

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Speaker 5: Yeah.

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Speaker 4: The problem is they, like you said, Galileo knew that

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Speaker 4: there was some sort of there there are gonna be

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Speaker 4: some flaws and measurement that we just didn't have those

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Speaker 4: great scientific instruments yet.

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Speaker 1: Right, Yeah, it's called the uncertainty principle. Okaybi of its accuracy.

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Speaker 4: Right, But the idea is that if you have a

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Speaker 4: good enough instruments, you can overcome that, and that the

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Speaker 4: the more you shrink the error in measuring the initial conditions. Yeah,

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Speaker 4: the more you're going to shrink the error in the

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Speaker 4: outcome be proportionate.

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Speaker 1: Right. They were correct.

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Speaker 4: The thing is they were also aware but ignoring in

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Speaker 4: a lot of ways some out standing problems, specifically something

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Speaker 4: called the N body problem.

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Speaker 1: Yet, you know what, I'm so excited about this. I

283
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Speaker 1: need to take a break.

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Speaker 4: I think that's a good idea.

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Speaker 1: I need to go check out my end body in

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Speaker 1: the bathroom.

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Speaker 3: Okay, and we'll be back, all.

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Speaker 4: Right, Chuck, We're back. So there's some there's some issues

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Speaker 4: right with determinism. There's some some weird problems out there

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Speaker 4: that are saying like, hey, pay attention to me because

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Speaker 4: I'm not sure determinism works. Right, And one is the

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Speaker 4: N body problem.

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Speaker 1: Yeah.

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Speaker 2: How this came about was in eighteen eighty five. That

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Speaker 2: was King Oscar number two of Sweden and Norway.

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Speaker 1: Yeah, don't want to leave out Norway.

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Speaker 2: Both he said, you know what, let's offer a prize

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Speaker 2: to anyone who can prove the stability of the Solar system,

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Speaker 2: something that has been stable for a long time before that.

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Speaker 2: And a lot of the most brilliant minds on planet

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Speaker 2: Earth got together and tried to do this with mathematical proofs,

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Speaker 2: and no one could do it. And then a dude

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Speaker 2: name Henri. You got to help me there with that? Oh,

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Speaker 2: say the whole thing.

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Speaker 5: Henri pon Care very nice.

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Speaker 2: He was French, believe it or not, and he was

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Speaker 2: a mathematician, and he said, you know what, I'm not

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Speaker 2: going to look at this big picture of all the

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Speaker 2: planets in the Sun and all their orbits.

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Speaker 4: You'd have to be a fool to try that.

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Speaker 2: Sure, he said, I'm going to shrink this down, like

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Speaker 2: we talked about shrinking that initial value, right, you know,

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Speaker 2: and that initial condition. He shrunk it down. He said,

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Speaker 2: I'm going to look at just a couple of bodies

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Speaker 2: orbiting one another with a common center of gravity, and

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Speaker 2: I'm going to look at this and this was called

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Speaker 2: the end body problem.

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Speaker 4: Yeah, which was smart to do because the more variables

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Speaker 4: you factor into a nonlinear equation like that, just the

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Speaker 4: harder it's going to be, so he shrunk it down.

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Speaker 4: So the n body problem has to do with three

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Speaker 4: or more celestial bodies orbiting one another. So Plonkarre said,

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Speaker 4: let's just start with three.

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Speaker 1: Yeah, smart.

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Speaker 4: And what he found from doing his equations for this

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Speaker 4: king Oscar the sequel prize, was that shrinking the initial

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Speaker 4: conditions measurements or rate of error, right, yeah, did not

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Speaker 4: really shrink the error in the outcome, which flies in

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Speaker 4: the face of determinism. What he found was that just

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Speaker 4: very very minute differences in the initial conditions fed into

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Speaker 4: a system produced wildly different outcomes. Yeah after a fairly

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Speaker 4: short time.

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Speaker 2: Yeah, Like, let me just round off the mass of

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Speaker 2: this planet at like the eighth decimal point, and you.

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Speaker 1: Know who cares? Who cares at that point?

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Speaker 2: I mean just round that one to a two, and

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Speaker 2: that would throw everything off at a pretty high rate.

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Speaker 2: And he said, wait a minute, I think this contest

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Speaker 2: is impossible.

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Speaker 4: Right, He said, there is no way to prove the

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Speaker 4: stability of the Solar System because he just uncovered the

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Speaker 4: idea that it's impossible for us to predict the rate

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Speaker 4: of change among celestial bodies.

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Speaker 2: Yeah, it's such a complex system. There are far too

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Speaker 2: many variables that it's impossible to start with something so

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Speaker 2: minute to get the equation whatever the that you want.

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Speaker 5: Right, well, not only that, but the result.

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Speaker 4: Not only that. And this is what really undermined determinism

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Speaker 4: was that he figured out that you would have to

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Speaker 4: have an infinitely precise measurement, which even if you build

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Speaker 4: a perfect machine that could take the infinitely or a

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Speaker 4: perfect machine that could take a measurement of like the

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Speaker 4: the movement of a celestial body around another, it's literally

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Speaker 4: impossible to get infinite an infinitely precise measurement, which means

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Speaker 4: that we could never predict out to a certain degree

356
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Speaker 4: the movement of these celestial bodies. Like he was saying, like, no,

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Speaker 4: you can't get you can't build a machine that gets

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Speaker 4: measurements enough that we can overcome this, Like determinism is wrong,

359
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Speaker 4: Like you can't just say we have the understanding to

360
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Speaker 4: predict everything. There's a lot of stuff out there that

361
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Speaker 4: were not able to predict and he uncovered it trying

362
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Speaker 4: to figure out this n body problem.

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Speaker 2: Yeah, and King Oscar the sequel said you win, Yeah,

364
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Speaker 2: bring me another rack of lamb and here's your prize. Yeah,

365
00:19:11,040 --> 00:19:13,960
Speaker 2: and he won by proving that it was impossible, which

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Speaker 2: is pretty interesting.

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Speaker 4: And they utterly and completely changed not just math, but

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Speaker 4: like our understanding of the universe and our understanding of

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Speaker 4: our understanding of the universe, which is even more kind

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Speaker 4: of earthshaking.

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Speaker 2: Yeah, he discovered dynamical instability or chaos. And they didn't

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Speaker 2: have supercomputers at the time, so it would be a

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Speaker 2: little while, about seventy years at MIT until we could

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Speaker 2: actually kind of feed these things into machines capable of

375
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Speaker 2: plotting these things out in a way that we could see, right,

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Speaker 2: which was really incredible.

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Speaker 4: So there was this dude seventy years later named Edward

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Speaker 4: Lawrence or Lorenz.

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Speaker 5: Yeah.

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Speaker 2: Well, first of all, we should set the stage the

381
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Speaker 2: reason this guy he was a meteorologist, yeah, and scientists, right,

382
00:20:05,400 --> 00:20:08,440
Speaker 2: not that those are not the same thing, right. He's

383
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Speaker 2: a scientist who dabbled a meteorology. He was a mathematician, yeah,

384
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Speaker 2: but he was really into meteorology because it was there

385
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Speaker 2: was a weird juxtaposition at the time where we were

386
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Speaker 2: sending people into outer space but we couldn't predict the weather.

387
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Speaker 4: Yeah, and it was it was definitely a blot on

388
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Speaker 4: the field of meteorology. People were like, do you guys

389
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Speaker 4: know what you're doing? Yeah, And meteorologists, you're like, you

390
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Speaker 4: have no idea how hard this is? Yeah, Like, yeah,

391
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Speaker 4: we can predict it a couple of days out, but

392
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Speaker 4: after that, it's just it's totally unpredictable. It drives us mad.

393
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Speaker 4: And it wasn't just their their reputations that were at stake,

394
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Speaker 4: like people were losing their lives because of it.

395
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Speaker 2: Right, Yeah, nineteen sixty two there were two notorious storms,

396
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Speaker 2: one on the East coast and one on the west,

397
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Speaker 2: the ash Wednesday storm in the East and the big

398
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Speaker 2: blow on the West that killed a lot of people,

399
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Speaker 2: cost hundreds of millions of dollars and damage, and people

400
00:21:02,040 --> 00:21:03,320
Speaker 2: were like, you know, we need to be able to

401
00:21:03,320 --> 00:21:05,560
Speaker 2: see these things coming a little more, right, because it's

402
00:21:05,920 --> 00:21:06,520
Speaker 2: a problem.

403
00:21:06,680 --> 00:21:08,800
Speaker 4: And meteorologists were like, why did you do it?

404
00:21:08,840 --> 00:21:09,040
Speaker 5: Then?

405
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Speaker 2: So they thought the key was these big supercomputers. Remember

406
00:21:14,800 --> 00:21:18,080
Speaker 2: the supercomputers. When they came out the big rooms full

407
00:21:18,119 --> 00:21:21,920
Speaker 2: of hardware, it was amazing, and they were finally able

408
00:21:21,920 --> 00:21:24,440
Speaker 2: to do like these incredible calculations that we could never

409
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Speaker 2: do before.

410
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Speaker 4: I know, they were able to like crunch sixty four

411
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Speaker 4: bytes a second.

412
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Speaker 1: Yeah, we had the abacus and then the supercomputer. There's

413
00:21:31,480 --> 00:21:32,240
Speaker 1: nothing in between.

414
00:21:33,560 --> 00:21:36,240
Speaker 4: I looked up the computer that Lareen's was working.

415
00:21:36,320 --> 00:21:37,000
Speaker 1: Was it the Whopper?

416
00:21:37,240 --> 00:21:39,360
Speaker 4: A Royal McBee? What was the Whopper?

417
00:21:39,520 --> 00:21:40,119
Speaker 1: War Games?

418
00:21:40,760 --> 00:21:41,719
Speaker 4: Was it called the Whopper?

419
00:21:41,800 --> 00:21:42,200
Speaker 3: Yeah?

420
00:21:42,440 --> 00:21:45,600
Speaker 1: Wopright, I can't believe they called it that.

421
00:21:46,760 --> 00:21:48,680
Speaker 4: So the guy just nicknamed it Joshua.

422
00:21:49,560 --> 00:21:55,399
Speaker 2: No, Joshua was the h the software falcon was the

423
00:21:55,760 --> 00:21:58,000
Speaker 2: old man who designed all this stuff, and his son

424
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Speaker 2: was Joshua. And that was the password to get in.

425
00:22:00,640 --> 00:22:03,480
Speaker 4: Oh, that was the password. Yeah, I guess I was

426
00:22:03,520 --> 00:22:05,600
Speaker 4: too young to understand what a password was.

427
00:22:05,800 --> 00:22:08,240
Speaker 1: Yeah, okay, you didn't even there weren't passwords at the time.

428
00:22:08,400 --> 00:22:11,879
Speaker 4: No, shouted it at the computer and they're like, okay,

429
00:22:12,000 --> 00:22:12,840
Speaker 4: access granted.

430
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Speaker 3: Yeah.

431
00:22:14,000 --> 00:22:16,879
Speaker 1: Still that movie holds up, does it really? Oh? Totally?

432
00:22:16,880 --> 00:22:17,600
Speaker 4: You got to check it out.

433
00:22:17,680 --> 00:22:21,119
Speaker 1: Yeah. Still very very fun. Young Ali sheety boy had

434
00:22:21,160 --> 00:22:22,800
Speaker 1: a crush on her from that movie.

435
00:22:22,840 --> 00:22:23,440
Speaker 4: She was great.

436
00:22:23,680 --> 00:22:23,960
Speaker 1: Yeah.

437
00:22:24,080 --> 00:22:27,520
Speaker 4: What else was she in recently? Wasn't she in something?

438
00:22:27,680 --> 00:22:27,880
Speaker 3: Well?

439
00:22:27,920 --> 00:22:30,000
Speaker 2: I mean she kind of went away for a while

440
00:22:30,240 --> 00:22:32,920
Speaker 2: and then had her big comeback with that indie movie

441
00:22:33,000 --> 00:22:35,200
Speaker 2: High Art, But that was a while ago.

442
00:22:35,880 --> 00:22:37,480
Speaker 4: Has she been in anything else? Recently.

443
00:22:37,840 --> 00:22:41,480
Speaker 2: Sure, I think I saw something and something recently and

444
00:22:41,520 --> 00:22:44,440
Speaker 2: I didn't realize that was her. Oh rightly, she looks familiar.

445
00:22:44,520 --> 00:22:45,840
Speaker 2: I was like, oh, that's Ali sheety.

446
00:22:47,960 --> 00:22:50,439
Speaker 1: I don't know all right, I could look it up,

447
00:22:50,440 --> 00:22:53,359
Speaker 1: but I won't. It doesn't matter anyway. I still crush

448
00:22:53,400 --> 00:22:53,680
Speaker 1: on her.

449
00:22:54,000 --> 00:22:58,880
Speaker 4: So the the Royal McBee was not quite the whopper.

450
00:22:58,920 --> 00:22:59,920
Speaker 4: You could actually sit.

451
00:22:59,760 --> 00:23:01,360
Speaker 5: Down the Royal McBee.

452
00:23:01,359 --> 00:23:02,119
Speaker 4: That's the name of it.

453
00:23:02,200 --> 00:23:03,520
Speaker 1: That sounds like a hamburger too.

454
00:23:03,840 --> 00:23:07,840
Speaker 4: It was by the Royal Typewriter Company, and they got

455
00:23:07,840 --> 00:23:10,760
Speaker 4: into computers for a second. And this is the kind

456
00:23:10,760 --> 00:23:14,040
Speaker 4: of computer that Lawrence was working with, and it was

457
00:23:14,720 --> 00:23:19,159
Speaker 4: a huge deal, Like you were saying, Abacus supercomputer. Yeah,

458
00:23:19,240 --> 00:23:22,560
Speaker 4: but it was still pretty dumb as far as what

459
00:23:22,680 --> 00:23:24,960
Speaker 4: we have today is concerned. But it was enough that

460
00:23:25,040 --> 00:23:28,440
Speaker 4: Lawrence was like, Lawrence and his ilk were like, finally

461
00:23:28,840 --> 00:23:32,800
Speaker 4: we can start running models and actually predict the weather. Yeah,

462
00:23:32,880 --> 00:23:34,160
Speaker 4: he started doing just that.

463
00:23:34,320 --> 00:23:38,040
Speaker 2: He did, so he started off with a computational model

464
00:23:38,080 --> 00:23:43,760
Speaker 2: of twelve meteorological meteorological I liked how you said it calculations,

465
00:23:43,760 --> 00:23:49,480
Speaker 2: which is very basic because they're infinite meteorological calculations. Probably, yeah,

466
00:23:49,600 --> 00:23:50,880
Speaker 2: depending to say it wrong again.

467
00:23:50,920 --> 00:23:53,760
Speaker 4: No, no, like it sounds like you're about to say it

468
00:23:53,760 --> 00:23:55,600
Speaker 4: wrong and then you pull it out at the last second.

469
00:23:55,680 --> 00:23:56,919
Speaker 4: Maybe it's really impressive.

470
00:23:57,000 --> 00:23:58,959
Speaker 1: But so that's very basic.

471
00:23:59,000 --> 00:24:01,919
Speaker 2: But he wanted to start out, you know, with something attainable,

472
00:24:02,280 --> 00:24:05,320
Speaker 2: so he narrowed it down to twelve conditions, basically twelve

473
00:24:05,400 --> 00:24:09,879
Speaker 2: calculations that had you know, temperature, wind, speed, pressure, stuff

474
00:24:09,920 --> 00:24:14,240
Speaker 2: like that, started forecasting weather. And then he said, you know,

475
00:24:14,280 --> 00:24:16,640
Speaker 2: it'd be great if you could see this, So I'm

476
00:24:16,640 --> 00:24:20,200
Speaker 2: going to spit it into my wonder machine, the McWhopper,

477
00:24:20,920 --> 00:24:24,480
Speaker 2: the Royal McBee, and I'm going to get a print

478
00:24:24,480 --> 00:24:28,240
Speaker 2: out so you can visualize what this looks like. So

479
00:24:28,320 --> 00:24:29,879
Speaker 2: things were going well and he had this print out

480
00:24:29,920 --> 00:24:33,800
Speaker 2: and everyone was amazed because these these calculations never seemed

481
00:24:33,800 --> 00:24:35,359
Speaker 2: to repeat themselves.

482
00:24:35,760 --> 00:24:41,200
Speaker 4: He was making like like like word art. You remember that,

483
00:24:41,240 --> 00:24:43,360
Speaker 4: Like that was the first thing anybody did on a computer.

484
00:24:43,640 --> 00:24:45,280
Speaker 4: Oh yeah, yeah, it was to make word art like

485
00:24:45,280 --> 00:24:46,159
Speaker 4: a butterfly or.

486
00:24:46,119 --> 00:24:48,960
Speaker 1: Something, right, you would print out. Yeah, I never could

487
00:24:49,040 --> 00:24:49,280
Speaker 1: do that.

488
00:24:49,760 --> 00:24:50,400
Speaker 4: I couldn't either.

489
00:24:50,800 --> 00:24:52,879
Speaker 2: Like you have to be able to visualize things spatially.

490
00:24:53,960 --> 00:24:56,159
Speaker 2: You have to have that right kind of brain.

491
00:24:55,960 --> 00:24:57,919
Speaker 4: For that, right or you have to be following a

492
00:24:57,960 --> 00:24:58,400
Speaker 4: guide book.

493
00:24:59,440 --> 00:24:59,680
Speaker 3: True.

494
00:25:00,240 --> 00:25:02,919
Speaker 4: Have you ever seen me? You and everyone? We know?

495
00:25:03,480 --> 00:25:04,359
Speaker 1: Yeah? I love that movie.

496
00:25:04,400 --> 00:25:05,160
Speaker 4: That's a great movie.

497
00:25:05,200 --> 00:25:05,360
Speaker 1: Yeah.

498
00:25:05,440 --> 00:25:07,879
Speaker 4: Those little kids in there, they were doing that. Oh

499
00:25:08,000 --> 00:25:12,320
Speaker 4: yeah yeah, the forever back and forth poop boy.

500
00:25:12,280 --> 00:25:13,800
Speaker 1: I haven't I haven't seen that since it came out.

501
00:25:13,840 --> 00:25:14,480
Speaker 1: It's been a while.

502
00:25:14,520 --> 00:25:15,560
Speaker 4: Oh you got to see it again?

503
00:25:15,640 --> 00:25:17,679
Speaker 1: Yeah, great movie, good movie.

504
00:25:17,560 --> 00:25:20,160
Speaker 4: Ali Sheet he's not in it. No, it's a Miranda

505
00:25:20,160 --> 00:25:20,480
Speaker 4: in July.

506
00:25:20,800 --> 00:25:22,520
Speaker 1: Right, and she like wrote and directed too. Right.

507
00:25:22,720 --> 00:25:25,159
Speaker 4: She did a great job. It's like it's one of

508
00:25:25,200 --> 00:25:30,359
Speaker 4: those rare movies where like there's just the right amount

509
00:25:30,400 --> 00:25:35,400
Speaker 4: of whimsy, because whimsy so easily overpowers everything else and becomes.

510
00:25:35,080 --> 00:25:38,080
Speaker 1: Like yeah, yeah, yeah.

511
00:25:37,320 --> 00:25:39,840
Speaker 4: This is like the most perfectly balanced amount of like

512
00:25:40,119 --> 00:25:41,760
Speaker 4: whimsy you I've ever seen in a movie.

513
00:25:41,840 --> 00:25:44,840
Speaker 2: Yeah, there's too much whimsy. I just like terrible Garden State.

514
00:25:44,880 --> 00:25:46,080
Speaker 2: I just want to punch it in the face.

515
00:25:46,280 --> 00:25:48,840
Speaker 4: Terrible. Although I like Garden State, but I haven't seen

516
00:25:48,880 --> 00:25:49,440
Speaker 4: it since.

517
00:25:49,240 --> 00:25:49,800
Speaker 5: It came out.

518
00:25:49,920 --> 00:25:52,600
Speaker 1: It hasn't aged well. Yeah, it's just when you look

519
00:25:52,600 --> 00:25:55,000
Speaker 1: at it now, it's just so cutesy and whimsical.

520
00:25:55,119 --> 00:25:56,520
Speaker 3: Oh yeah, it's like come on.

521
00:25:56,880 --> 00:26:01,720
Speaker 2: Yeah, boy, We're do a lot of movies today.

522
00:26:01,760 --> 00:26:03,080
Speaker 4: Oh yeah, well we're stalling.

523
00:26:03,359 --> 00:26:06,200
Speaker 2: We haven't even talked about butterfly effect yet, which is coming.

524
00:26:07,520 --> 00:26:10,919
Speaker 4: I'm dreading it. That's why I'm stalling, all right.

525
00:26:10,960 --> 00:26:11,600
Speaker 1: So where were we?

526
00:26:11,720 --> 00:26:16,360
Speaker 2: He was running his calculations, printing out his values so

527
00:26:16,400 --> 00:26:19,240
Speaker 2: people could see it, and then he got a little

528
00:26:19,280 --> 00:26:23,760
Speaker 2: lazy one day in nineteen sixty one. This output he

529
00:26:23,880 --> 00:26:26,560
Speaker 2: noticed was interesting, so he said, you know, I'm going

530
00:26:26,600 --> 00:26:30,080
Speaker 2: to repeat this calculation see it again, but I'm going

531
00:26:30,119 --> 00:26:32,080
Speaker 2: to to save time. I'm just going to kind of

532
00:26:32,080 --> 00:26:35,040
Speaker 2: pick up in the middle, and I'm not going to

533
00:26:35,119 --> 00:26:39,080
Speaker 2: input as many numbers, but I'm still using the same values,

534
00:26:39,119 --> 00:26:41,680
Speaker 2: just I'm not going out to six decimal points.

535
00:26:41,760 --> 00:26:45,320
Speaker 4: So the print out he had went to three decimal points. Yeah,

536
00:26:45,359 --> 00:26:48,040
Speaker 4: so he was working from the print out and didn't

537
00:26:48,040 --> 00:26:51,320
Speaker 4: take into account that the computer accepted six decimal points.

538
00:26:51,359 --> 00:26:53,920
Speaker 4: So he was just putting in three correct and expecting

539
00:26:53,960 --> 00:26:55,080
Speaker 4: that the outcome would be the same.

540
00:26:55,119 --> 00:26:57,359
Speaker 1: Right, Yes, but the outcome was way different, right, And

541
00:26:57,400 --> 00:26:57,879
Speaker 1: he went.

542
00:26:58,040 --> 00:27:00,280
Speaker 5: Whoa, whoa what?

543
00:27:00,600 --> 00:27:04,320
Speaker 1: Yeah, he's like, what's going on here? It was a

544
00:27:04,359 --> 00:27:04,800
Speaker 1: big deal.

545
00:27:05,000 --> 00:27:06,680
Speaker 2: I mean, someone would have come up with this eventually,

546
00:27:06,680 --> 00:27:09,600
Speaker 2: probably yeah, but I sort of accidentally came upon it.

547
00:27:09,600 --> 00:27:11,719
Speaker 4: It's neat that this guy did this because it changed

548
00:27:12,119 --> 00:27:15,000
Speaker 4: his career. I think he went from an emphasis on

549
00:27:15,080 --> 00:27:20,600
Speaker 4: meteorology to an emphasis on chaos math to stud scientists basically.

550
00:27:21,160 --> 00:27:24,080
Speaker 4: So I mean, the guy's got an attractor named after him,

551
00:27:24,080 --> 00:27:24,600
Speaker 4: you know what I mean.

552
00:27:24,920 --> 00:27:26,239
Speaker 1: Yeah, well, let's get to that.

553
00:27:26,560 --> 00:27:29,920
Speaker 4: So Lorenz starts looking at this and he's like, wait

554
00:27:29,920 --> 00:27:32,720
Speaker 4: a minute, this is this is weird, this is worth investigating,

555
00:27:33,280 --> 00:27:38,760
Speaker 4: and like, what was his name, puon Carre. Yeah, he said,

556
00:27:38,920 --> 00:27:41,600
Speaker 4: I need fewer variables. So I'm not going to try

557
00:27:41,600 --> 00:27:46,119
Speaker 4: to predict weather with these twelve differential equations that you

558
00:27:46,200 --> 00:27:48,680
Speaker 4: have to take into account. I'm just going to take

559
00:27:48,840 --> 00:27:53,199
Speaker 4: one aspect of weather called the rolling convection current, and

560
00:27:53,240 --> 00:27:56,320
Speaker 4: I'm going to see how I can write it down

561
00:27:56,359 --> 00:27:59,280
Speaker 4: in formula form. So a rolling convection current, chuck is

562
00:27:59,280 --> 00:28:04,160
Speaker 4: where you know, how the wind is created, where air

563
00:28:04,200 --> 00:28:08,200
Speaker 4: at the surface is heated and then starts to rise

564
00:28:08,800 --> 00:28:12,000
Speaker 4: and suddenly cool air from higher above comes in to

565
00:28:12,040 --> 00:28:16,119
Speaker 4: fill that vacuum that's left, and that creates a rolling

566
00:28:17,920 --> 00:28:20,359
Speaker 4: or vertically based convection current.

567
00:28:20,600 --> 00:28:21,760
Speaker 3: Yeah, okay, you could.

568
00:28:21,800 --> 00:28:23,760
Speaker 1: I would describe it as oven.

569
00:28:24,520 --> 00:28:29,200
Speaker 4: Oven, boiling water, cup of coffee. Wherever there's a temperature

570
00:28:29,200 --> 00:28:33,919
Speaker 4: differential based on a vertical alignment, you're going to have

571
00:28:33,960 --> 00:28:35,280
Speaker 4: a rolling convection current.

572
00:28:35,320 --> 00:28:38,840
Speaker 2: Okay, yeah, it sounds complex, but he just picked out

573
00:28:38,880 --> 00:28:41,560
Speaker 2: one thing, basically one condition, right, and this is the

574
00:28:41,560 --> 00:28:42,160
Speaker 2: one he picked out.

575
00:28:42,200 --> 00:28:45,440
Speaker 4: But had you seen my hands moving listeners, you would

576
00:28:45,480 --> 00:28:46,240
Speaker 4: be like, oh, yeah.

577
00:28:46,120 --> 00:28:46,280
Speaker 3: I know.

578
00:28:47,160 --> 00:28:48,720
Speaker 1: He made little rolling motions.

579
00:28:48,840 --> 00:28:51,920
Speaker 4: So he's like, okay, I can figure this out. So

580
00:28:52,120 --> 00:28:56,280
Speaker 4: he comes up with three three formulae that kind of

581
00:28:56,360 --> 00:29:00,360
Speaker 4: describe a rolling convection current, and he starts trying to

582
00:29:00,400 --> 00:29:06,080
Speaker 4: figure out how to describe this rolling convection current right, correct,

583
00:29:06,320 --> 00:29:08,480
Speaker 4: And so, like I said, he got these three formula

584
00:29:08,520 --> 00:29:11,920
Speaker 4: which were basically three variables that he calculated over time,

585
00:29:12,160 --> 00:29:15,480
Speaker 4: and he plugged him in and he found three variables

586
00:29:15,600 --> 00:29:18,520
Speaker 4: that changed over time. And he found that after a

587
00:29:18,560 --> 00:29:21,720
Speaker 4: certain point when you graph these things out, and since

588
00:29:21,760 --> 00:29:25,320
Speaker 4: they're three, you graph them out on a three dimensional graph.

589
00:29:25,360 --> 00:29:26,200
Speaker 4: So X, Y and Z.

590
00:29:26,440 --> 00:29:29,040
Speaker 2: Again, he wanted to just be able to visualize this, right,

591
00:29:29,080 --> 00:29:30,640
Speaker 2: because it's easier for people to understand.

592
00:29:30,720 --> 00:29:31,800
Speaker 4: He was a very visual guy.

593
00:29:32,000 --> 00:29:32,400
Speaker 1: Totally.

594
00:29:32,560 --> 00:29:35,880
Speaker 4: All of a sudden, it made this crazy graph that

595
00:29:36,400 --> 00:29:39,920
Speaker 4: where the line as it progressed forward through time, went

596
00:29:40,040 --> 00:29:42,040
Speaker 4: all over the place. It went from this axis to

597
00:29:42,120 --> 00:29:45,080
Speaker 4: another axis, to the other axis, and it would spend

598
00:29:45,080 --> 00:29:47,480
Speaker 4: some time over here, and then it would suddenly loop

599
00:29:47,520 --> 00:29:50,320
Speaker 4: over to the other one. And it followed no rhyme

600
00:29:50,400 --> 00:29:55,520
Speaker 4: or reason. It never retraced its path. And it was

601
00:29:55,600 --> 00:29:59,600
Speaker 4: describing how a convection current changes over time, right.

602
00:29:59,720 --> 00:30:02,080
Speaker 1: Yeah, And Lorenzo.

603
00:30:01,680 --> 00:30:05,520
Speaker 4: Is looking at this, he was expecting these three things

604
00:30:05,560 --> 00:30:09,600
Speaker 4: to equalize and eventually form a line. Yeah, because that's

605
00:30:09,640 --> 00:30:12,280
Speaker 4: what determinism says, things are going to fall into a

606
00:30:12,320 --> 00:30:16,200
Speaker 4: certain amount of equilibrium and just even out over time.

607
00:30:16,400 --> 00:30:18,800
Speaker 4: That is not what he found. No, and what he

608
00:30:18,840 --> 00:30:22,320
Speaker 4: discovered was what point care A discovered, which was that

609
00:30:23,600 --> 00:30:30,120
Speaker 4: some systems, even relatively simple systems, exhibit very complex, unpredictable behavior,

610
00:30:30,480 --> 00:30:31,920
Speaker 4: which you could call chaos.

611
00:30:32,480 --> 00:30:32,760
Speaker 1: Yeah.

612
00:30:32,760 --> 00:30:35,160
Speaker 2: And when you say things were going all over like

613
00:30:35,200 --> 00:30:37,640
Speaker 2: if you look at the graph, it it's not just

614
00:30:37,760 --> 00:30:41,120
Speaker 2: lines going in straight lines bouncing all over the place randomly,

615
00:30:41,280 --> 00:30:44,360
Speaker 2: like there was an order to it, but the lines

616
00:30:44,400 --> 00:30:46,800
Speaker 2: were not on top of one another. Like let's say

617
00:30:46,800 --> 00:30:49,560
Speaker 2: you draw a figure eight with your pencil and then

618
00:30:49,600 --> 00:30:51,280
Speaker 2: you continue drawing that figure eight.

619
00:30:51,320 --> 00:30:51,760
Speaker 1: It's going to.

620
00:30:51,800 --> 00:30:56,280
Speaker 2: Slip outside those curves right every time unless you're a robot.

621
00:30:56,560 --> 00:30:59,280
Speaker 1: Sure, And that's what it ended up looking like.

622
00:30:59,440 --> 00:30:59,600
Speaker 2: Yeah.

623
00:30:59,640 --> 00:31:04,800
Speaker 4: Yeah, never retraced the same path twice. Ever, it had

624
00:31:04,840 --> 00:31:08,080
Speaker 4: a lot of really surprising properties, and at the time

625
00:31:08,440 --> 00:31:11,840
Speaker 4: it just fell completely outside the understanding of science, right.

626
00:31:12,040 --> 00:31:12,280
Speaker 1: Yeah.

627
00:31:12,720 --> 00:31:15,960
Speaker 4: Luckily this happened to Lawrence who was curious enough to

628
00:31:15,960 --> 00:31:19,080
Speaker 4: be like, what is going on here? And again he

629
00:31:19,120 --> 00:31:21,200
Speaker 4: sat down and started to do the math and thinking

630
00:31:21,200 --> 00:31:25,080
Speaker 4: about this and especially how it applied to the weather, right, yeah,

631
00:31:25,120 --> 00:31:27,920
Speaker 4: and he came up with something very famous.

632
00:31:28,320 --> 00:31:34,040
Speaker 2: Yes, the butterfly effect. Yes, A, this thing kind of

633
00:31:34,040 --> 00:31:36,920
Speaker 2: looked like butterfly wings a little bit. Yeah, And b

634
00:31:37,360 --> 00:31:41,640
Speaker 2: when he went to present his findings, he basically had

635
00:31:41,640 --> 00:31:44,000
Speaker 2: the notion He's like, I'm gonna I'm gonna wow these

636
00:31:44,040 --> 00:31:47,160
Speaker 2: people in the crowd in nineteen seventy two. It's a

637
00:31:47,200 --> 00:31:50,160
Speaker 2: conference that I'm going to and I'm gonna I'm gonna

638
00:31:50,160 --> 00:31:53,000
Speaker 2: say something like, you know, the seagull flaps his wings

639
00:31:53,000 --> 00:31:55,920
Speaker 2: and it starts a small turbulence that can one that

640
00:31:55,920 --> 00:31:58,400
Speaker 2: can affect weather on the other side of the world. Right,

641
00:31:58,720 --> 00:32:01,120
Speaker 2: the small little thing will just grow and grow and

642
00:32:01,200 --> 00:32:04,400
Speaker 2: snowball and effect things. And he had a colleague was like,

643
00:32:05,720 --> 00:32:09,120
Speaker 2: seagull wings, that's nice, and he said, how about this,

644
00:32:09,280 --> 00:32:12,160
Speaker 2: And this is the title. They ended up with, predictability

645
00:32:12,280 --> 00:32:15,640
Speaker 2: Colon does the flap of a butterfly's wings in Brazil

646
00:32:16,160 --> 00:32:20,840
Speaker 2: set off a tornado in Texas? And everyone was like, whoa,

647
00:32:21,040 --> 00:32:22,920
Speaker 2: WHOA mine's blown?

648
00:32:24,360 --> 00:32:26,960
Speaker 1: Should we take a break? All right, We'll be right back,

649
00:32:43,960 --> 00:32:44,400
Speaker 1: all right.

650
00:32:44,480 --> 00:32:51,120
Speaker 2: So the Lorens Attractor is that picture that he ended

651
00:32:51,200 --> 00:32:58,360
Speaker 2: up with, The Lorens Attractor. And this biblical pattern website

652
00:32:59,000 --> 00:33:03,240
Speaker 2: that I found described attractors and strange attractors in a

653
00:33:03,280 --> 00:33:06,640
Speaker 2: way that even dumb old me could understand what you got.

654
00:33:06,880 --> 00:33:10,480
Speaker 2: So if I may, he says, all right, here's the

655
00:33:10,520 --> 00:33:15,200
Speaker 2: cycle of chaos. He said, I actually I don't know

656
00:33:15,200 --> 00:33:19,840
Speaker 2: who wrote this. A woman could have been a small child, Noah.

657
00:33:19,520 --> 00:33:24,960
Speaker 1: Of undetermined gender. I have no idea the gender neutral narrator,

658
00:33:25,960 --> 00:33:26,480
Speaker 1: they said.

659
00:33:26,920 --> 00:33:29,840
Speaker 2: He said, right, think about a town that has like

660
00:33:29,960 --> 00:33:33,520
Speaker 2: ten thousand people living in it. To make that town work,

661
00:33:33,600 --> 00:33:35,680
Speaker 2: you got to have like a gas station, a grocery store,

662
00:33:36,760 --> 00:33:41,200
Speaker 2: a library, whatever you need to sustain that town. Okay,

663
00:33:41,360 --> 00:33:45,320
Speaker 2: so all these things are built, everyone's happy, you have equilibrium.

664
00:33:45,600 --> 00:33:49,320
Speaker 2: He said, So that's great. Then let's say you build

665
00:33:49,400 --> 00:33:52,520
Speaker 2: some Someone comes and builds a factory on the outskirts

666
00:33:52,560 --> 00:33:54,560
Speaker 2: of that town, and there's going to be ten thousand

667
00:33:54,560 --> 00:33:55,600
Speaker 2: more people living there.

668
00:33:55,560 --> 00:33:56,800
Speaker 4: Right, and they don't go to church?

669
00:33:57,560 --> 00:34:01,560
Speaker 1: Maybe so, uh, did I say church they needed a church?

670
00:34:01,640 --> 00:34:05,480
Speaker 4: No? No, okay, I was just assuming this is what's equilibrium.

671
00:34:05,520 --> 00:34:08,239
Speaker 2: No, no, no, but you just have more people. So there's

672
00:34:08,719 --> 00:34:11,359
Speaker 2: you need another gas station and another grocery store. Let's

673
00:34:11,360 --> 00:34:14,680
Speaker 2: say so they build all these things and then you

674
00:34:14,719 --> 00:34:18,399
Speaker 2: reach equilibrium. Again, it's maintained because you build all these

675
00:34:18,400 --> 00:34:19,000
Speaker 2: other systems.

676
00:34:19,080 --> 00:34:19,279
Speaker 3: Up.

677
00:34:19,440 --> 00:34:24,480
Speaker 2: I see that equilibrium. It's called an attractor. Okay, So

678
00:34:24,560 --> 00:34:28,239
Speaker 2: then he said it said they said.

679
00:34:30,360 --> 00:34:33,080
Speaker 5: He capital he the royal.

680
00:34:33,120 --> 00:34:36,320
Speaker 2: He said, all right, Now, let's say instead of that

681
00:34:36,719 --> 00:34:39,719
Speaker 2: factory being built and you have those original ten thousand,

682
00:34:39,800 --> 00:34:42,040
Speaker 2: let's say three thousand. Those people just up and leave

683
00:34:42,080 --> 00:34:45,719
Speaker 2: one day, and the grocery store guy says, well, there's

684
00:34:45,760 --> 00:34:47,960
Speaker 2: only seven thousand people here. We need eight thousand people

685
00:34:47,960 --> 00:34:51,040
Speaker 2: living here to make a profit, so I'm shutting down

686
00:34:51,080 --> 00:34:54,160
Speaker 2: this grocery store. Then all of a sudden, you have

687
00:34:54,280 --> 00:34:57,799
Speaker 2: demand for groceries. So things go on for a little while,

688
00:34:57,800 --> 00:34:59,279
Speaker 2: and someone comes in and say, hey, this town needs

689
00:34:59,320 --> 00:35:03,000
Speaker 2: a grocery store. They build a grocery store, they can't sustain,

690
00:35:03,160 --> 00:35:05,560
Speaker 2: they shut down. Someone else comes along because the demand,

691
00:35:06,239 --> 00:35:11,800
Speaker 2: and it is this search for equilibrium, this dynamic Well,

692
00:35:12,040 --> 00:35:16,240
Speaker 2: you reach equililibrium here and there as the store opens.

693
00:35:15,880 --> 00:35:17,759
Speaker 4: Periods of stability, periods.

694
00:35:17,400 --> 00:35:22,759
Speaker 2: Of stability, and that dynamic equilibrium is called a strange attractor.

695
00:35:23,360 --> 00:35:26,759
Speaker 2: So an attractor is the state which the system settles on.

696
00:35:27,120 --> 00:35:32,520
Speaker 2: Stranger attractor is the trajectory on which it never settles

697
00:35:32,560 --> 00:35:36,040
Speaker 2: down but tries to reach the equilibrium with periods of stability.

698
00:35:36,560 --> 00:35:37,120
Speaker 1: Does that make.

699
00:35:37,080 --> 00:35:42,440
Speaker 4: Sense that Bible based explanation was dynamite. I understand it

700
00:35:42,480 --> 00:35:45,600
Speaker 4: better than I did before, and I understood it okay before.

701
00:35:46,800 --> 00:35:47,319
Speaker 4: That's great.

702
00:35:47,440 --> 00:35:52,440
Speaker 1: Surely can add yeah, yeah, no, you're gonna add to it. No,

703
00:35:52,640 --> 00:35:53,040
Speaker 1: that's it.

704
00:35:53,280 --> 00:35:56,879
Speaker 4: No, I mean like it. Yeah. An attractor is where

705
00:35:56,960 --> 00:36:00,719
Speaker 4: if you graph something and eventually it reaches equilibrium, it's

706
00:36:00,719 --> 00:36:03,960
Speaker 4: a regular attractor. If it never reaches equilibrium and is

707
00:36:04,040 --> 00:36:07,000
Speaker 4: constantly trying to and has periods of stability, strange attractor.

708
00:36:07,239 --> 00:36:07,680
Speaker 5: I can't.

709
00:36:07,800 --> 00:36:10,680
Speaker 1: I can't top that, all right, grocery store, small town.

710
00:36:10,520 --> 00:36:11,160
Speaker 5: That was great.

711
00:36:11,480 --> 00:36:16,439
Speaker 4: So Lorenz's strange attractor was named a Lorenz attractor named

712
00:36:16,480 --> 00:36:17,480
Speaker 4: after him. Big deal.

713
00:36:17,520 --> 00:36:19,480
Speaker 1: They weren't using the word chaos yet.

714
00:36:19,440 --> 00:36:23,839
Speaker 4: No, but he published that paper about butterfly wings, right, yeah,

715
00:36:23,880 --> 00:36:28,279
Speaker 4: the butterfly effect, and it coupled with his picture is

716
00:36:28,440 --> 00:36:31,399
Speaker 4: the picture of a strange attractor, which is almost the

717
00:36:32,000 --> 00:36:39,040
Speaker 4: aside from fractals, almost the the emblem or the logo

718
00:36:39,239 --> 00:36:43,520
Speaker 4: for chaos theory, the Lorens attractor is. It got attention

719
00:36:43,960 --> 00:36:46,640
Speaker 4: off the bat. It wasn't like puant Caret's findings where

720
00:36:46,640 --> 00:36:50,040
Speaker 4: it got neglected for seventy years. Almost immediately everybody was

721
00:36:50,040 --> 00:36:53,480
Speaker 4: talking about this because again, what Lorenzo had uncovered, which

722
00:36:53,520 --> 00:36:55,840
Speaker 4: is the same thing that puant care had uncovered, is

723
00:36:55,880 --> 00:37:01,680
Speaker 4: that determinism is possibly based on all that the universe

724
00:37:01,840 --> 00:37:04,759
Speaker 4: isn't stable, that the universe isn't predictable, and that what

725
00:37:04,800 --> 00:37:08,600
Speaker 4: we are seeing as stable and predictable are these little

726
00:37:08,640 --> 00:37:13,440
Speaker 4: periods windows of stability that are found in strange attractor graphs.

727
00:37:13,760 --> 00:37:16,120
Speaker 4: That that's what we think the order of the universe is,

728
00:37:16,200 --> 00:37:21,160
Speaker 4: but that that is actually the abnormal aspect of the universe,

729
00:37:21,320 --> 00:37:25,839
Speaker 4: and that instability unpredictability as far as we're concerned is

730
00:37:25,920 --> 00:37:29,719
Speaker 4: the actual state of affairs in nature. And I think

731
00:37:29,800 --> 00:37:32,959
Speaker 4: as far as we're concerned is a really important point too, Chuck,

732
00:37:33,280 --> 00:37:39,920
Speaker 4: because it doesn't mean that nature is unstable chaotic. It

733
00:37:40,080 --> 00:37:43,919
Speaker 4: means that our picture of what we understand as order

734
00:37:44,280 --> 00:37:48,920
Speaker 4: doesn't jibe with how the universe actually functions. It's just

735
00:37:49,000 --> 00:37:54,600
Speaker 4: our understanding of it, and we're just so anthropocentric that

736
00:37:55,680 --> 00:37:57,920
Speaker 4: we see it as chaos and disorder and something to

737
00:37:57,960 --> 00:38:00,879
Speaker 4: be feared, when really it's just come flexity that we

738
00:38:01,800 --> 00:38:06,160
Speaker 4: don't have the capability of predicting. Yeah, after a certain.

739
00:38:05,920 --> 00:38:08,279
Speaker 2: Degree, Yeah, I think that makes me feel a little better,

740
00:38:08,320 --> 00:38:10,600
Speaker 2: because when you read stuff like this, you start to

741
00:38:10,600 --> 00:38:13,560
Speaker 2: feel like, well, the Earth could just throw us all

742
00:38:13,640 --> 00:38:16,920
Speaker 2: off of its face at any moment because it starts

743
00:38:16,920 --> 00:38:19,480
Speaker 2: spinning so fast that gravity becomes undone.

744
00:38:19,560 --> 00:38:20,400
Speaker 1: And I know that's not right.

745
00:38:20,440 --> 00:38:22,800
Speaker 4: By the way, I've always loved that kind of science

746
00:38:22,840 --> 00:38:26,600
Speaker 4: that shows we don't know anything, like Robert Robert Hume,

747
00:38:27,040 --> 00:38:28,920
Speaker 4: who I know, I understand was a philosopher, but he

748
00:38:28,960 --> 00:38:30,120
Speaker 4: was a philosopher scientist.

749
00:38:30,280 --> 00:38:30,560
Speaker 1: Sure.

750
00:38:31,239 --> 00:38:33,319
Speaker 4: His whole jam was like cause and effect is an

751
00:38:33,320 --> 00:38:37,600
Speaker 4: illusion that, like we all, it's just an assumption, like

752
00:38:37,640 --> 00:38:40,319
Speaker 4: that if you drop a pencil, it will always fall down.

753
00:38:40,480 --> 00:38:44,680
Speaker 4: It's an illusion and this is pre gravity understanding gravity.

754
00:38:45,120 --> 00:38:46,320
Speaker 4: But he makes a good.

755
00:38:46,200 --> 00:38:48,080
Speaker 1: Price gravity when everyone's just floating around.

756
00:38:48,360 --> 00:38:52,040
Speaker 4: Yeah, going this pencils got me wacky. Yeah, but the

757
00:38:52,120 --> 00:38:55,799
Speaker 4: point was that you know, we are we base a

758
00:38:55,800 --> 00:38:59,360
Speaker 4: lot of our assumptions or a lot of stuff that

759
00:38:59,400 --> 00:39:02,840
Speaker 4: we take a law are actually based on assumptions that

760
00:39:02,880 --> 00:39:05,799
Speaker 4: are made from observations over time, and that we're just

761
00:39:05,840 --> 00:39:08,440
Speaker 4: making predictions that cause and effect as an illusion. I

762
00:39:08,480 --> 00:39:15,400
Speaker 4: love that guy, and this definitely supports that idea for sure. Sorry,

763
00:39:15,640 --> 00:39:17,320
Speaker 4: I'm excited about chaos theory.

764
00:39:17,360 --> 00:39:18,200
Speaker 1: Can believe it?

765
00:39:18,320 --> 00:39:21,600
Speaker 2: Well, I mean I like that I'm able to understand

766
00:39:21,600 --> 00:39:24,200
Speaker 2: it in enough of a rudimentary way that I can

767
00:39:24,480 --> 00:39:26,040
Speaker 2: talk about it at a dinner party.

768
00:39:26,120 --> 00:39:30,200
Speaker 4: Well, thank your Bible website. Well once you take the

769
00:39:30,280 --> 00:39:34,280
Speaker 4: formulas out, Yeah, for people like us, we're like, oh, okay,

770
00:39:34,280 --> 00:39:35,680
Speaker 4: we can understand chaos.

771
00:39:35,760 --> 00:39:35,960
Speaker 1: Yeah.

772
00:39:36,040 --> 00:39:38,759
Speaker 4: Then when somebody says, good, do a differential equation, You're

773
00:39:38,760 --> 00:39:39,040
Speaker 4: just like.

774
00:39:39,280 --> 00:39:43,200
Speaker 1: What what a different equation? All right?

775
00:39:43,239 --> 00:39:46,240
Speaker 2: So earlier I said that chaos had not been used

776
00:39:46,239 --> 00:39:49,680
Speaker 2: the word chaos to describe all this junk, right, and

777
00:39:49,719 --> 00:39:51,120
Speaker 2: that didn't happen until.

778
00:39:51,160 --> 00:39:54,880
Speaker 1: Later on, well actually about ten years, you know, but

779
00:39:54,960 --> 00:39:55,400
Speaker 1: it was kind of.

780
00:39:55,360 --> 00:39:56,920
Speaker 2: At the same time this other stuff was going on

781
00:39:57,000 --> 00:40:01,359
Speaker 2: with Lorenz. Yeah, late sixties, early seventies. There's a guy

782
00:40:01,440 --> 00:40:06,520
Speaker 2: named Stephen Smale, Fields metal recipient, so you know, he's

783
00:40:06,520 --> 00:40:12,439
Speaker 2: good at math, and he described something that we now

784
00:40:12,520 --> 00:40:16,319
Speaker 2: know as the Smale horseshoe. And it goes a little

785
00:40:16,360 --> 00:40:21,719
Speaker 2: something like this. So, all right, take a piece of

786
00:40:22,040 --> 00:40:25,799
Speaker 2: dough like bread dough, and you smash it out into

787
00:40:25,840 --> 00:40:27,080
Speaker 2: a big flat rectangle.

788
00:40:27,480 --> 00:40:29,920
Speaker 1: Do So you're looking at that thing and you're like, boy,

789
00:40:30,120 --> 00:40:31,680
Speaker 1: I hope this makes some good bread.

790
00:40:31,760 --> 00:40:32,959
Speaker 4: This is gonna be so good.

791
00:40:33,400 --> 00:40:35,359
Speaker 1: So then you go a little roseberry on it, yeah,

792
00:40:35,400 --> 00:40:37,600
Speaker 1: maybe so well sea salt.

793
00:40:37,520 --> 00:40:40,759
Speaker 4: Yeah, and then lick it before you bake it so

794
00:40:40,840 --> 00:40:42,880
Speaker 4: you know it's yours. No one else can have it.

795
00:40:44,239 --> 00:40:47,160
Speaker 2: So you have that flat rectangle of dough, you roll

796
00:40:47,200 --> 00:40:51,480
Speaker 2: it up into a tube and then you smash that

797
00:40:51,520 --> 00:40:54,040
Speaker 2: down kind of flat, and then you bend that down

798
00:40:54,120 --> 00:40:57,200
Speaker 2: to where it eventually looks like a horseshoe. Okay, so

799
00:40:57,280 --> 00:41:00,400
Speaker 2: now you take that horseshoe, you take another rectangle dough

800
00:41:00,760 --> 00:41:03,920
Speaker 2: and you throw that horseshoe onto that and then you

801
00:41:04,000 --> 00:41:08,160
Speaker 2: do the same thing. The smale horseshoe basically says you

802
00:41:08,200 --> 00:41:11,680
Speaker 2: cannot predict where the two points of that horseshoe will

803
00:41:11,760 --> 00:41:15,720
Speaker 2: end up. Yeah, you can roll it a million times

804
00:41:15,960 --> 00:41:19,400
Speaker 2: and it'll end up in a million different places.

805
00:41:19,600 --> 00:41:21,520
Speaker 4: Totally random, different places too.

806
00:41:21,480 --> 00:41:22,680
Speaker 1: Totally random. You never know.

807
00:41:23,040 --> 00:41:24,759
Speaker 2: It's like a box of chocolates. You never know what

808
00:41:24,800 --> 00:41:25,399
Speaker 2: you're gonna get.

809
00:41:26,040 --> 00:41:28,319
Speaker 1: You have to say it, and that became known.

810
00:41:28,480 --> 00:41:29,239
Speaker 4: You have to say it.

811
00:41:29,360 --> 00:41:32,080
Speaker 1: Oh what imitate Forrest Gump? Sure, I can't do that.

812
00:41:32,080 --> 00:41:32,520
Speaker 4: That's fine.

813
00:41:32,640 --> 00:41:34,200
Speaker 1: He's not one. He's not in my repertoire.

814
00:41:34,760 --> 00:41:35,280
Speaker 4: That's fine.

815
00:41:35,800 --> 00:41:37,920
Speaker 1: Although I did see that again part of it recently.

816
00:41:38,880 --> 00:41:40,120
Speaker 4: Does it hold up well?

817
00:41:40,160 --> 00:41:42,200
Speaker 2: I mean, take out forty minutes of it and it

818
00:41:42,200 --> 00:41:45,719
Speaker 2: would have been a better movie, like all of that.

819
00:41:45,880 --> 00:41:47,120
Speaker 1: Coincidence stuff that.

820
00:41:47,840 --> 00:41:48,800
Speaker 5: Oh I love that.

821
00:41:49,239 --> 00:41:52,000
Speaker 2: And he also did the smile T shirt like it

822
00:41:52,080 --> 00:41:55,600
Speaker 2: was just too much, Like he really hammered it too much.

823
00:41:55,760 --> 00:41:56,480
Speaker 3: I liked it.

824
00:41:57,280 --> 00:41:58,800
Speaker 4: That was the basis of the movie.

825
00:41:58,960 --> 00:41:59,160
Speaker 1: I know.

826
00:41:59,200 --> 00:42:01,319
Speaker 2: But see it again and I guarantee you, like an

827
00:42:01,360 --> 00:42:02,920
Speaker 2: hour and a half into it, you'll be like, I

828
00:42:03,000 --> 00:42:05,120
Speaker 2: get it.

829
00:42:06,000 --> 00:42:07,759
Speaker 4: You know. It was a good Tom Hanks movie. That

830
00:42:07,880 --> 00:42:10,400
Speaker 4: was overlooked A Road to Perdition.

831
00:42:11,360 --> 00:42:14,080
Speaker 1: Yeah, this is a good one. Great Sam Mendez.

832
00:42:14,600 --> 00:42:16,120
Speaker 4: Oh man, that guy's awesome.

833
00:42:16,320 --> 00:42:16,800
Speaker 1: Yeah.

834
00:42:16,880 --> 00:42:19,520
Speaker 4: Oh what is he going to do? He might do something.

835
00:42:19,719 --> 00:42:21,960
Speaker 2: He did the James Bar he did Skyfall. Yeah, yeah, no,

836
00:42:22,040 --> 00:42:24,239
Speaker 2: he's gonna also that last one that wasn't so great.

837
00:42:24,440 --> 00:42:27,480
Speaker 4: He's got a potential project coming up and he would

838
00:42:27,480 --> 00:42:29,759
Speaker 4: be amazing for it. I don't remember what it was.

839
00:42:29,840 --> 00:42:31,279
Speaker 1: Did you see Revolutionary Road?

840
00:42:31,640 --> 00:42:31,879
Speaker 5: Yes?

841
00:42:32,239 --> 00:42:34,600
Speaker 4: God how it was just like.

842
00:42:35,040 --> 00:42:36,479
Speaker 1: Yeah, you want to jump off a bridge?

843
00:42:36,520 --> 00:42:40,280
Speaker 4: Yeah? Movie like every five minutes during that movie.

844
00:42:40,320 --> 00:42:41,000
Speaker 1: That was hardcore.

845
00:42:41,160 --> 00:42:41,560
Speaker 4: It is.

846
00:42:42,320 --> 00:42:43,680
Speaker 5: Uh he did that one too, huh.

847
00:42:43,800 --> 00:42:45,719
Speaker 2: Yeah, and don't see that if you're like engaged to

848
00:42:45,719 --> 00:42:47,279
Speaker 2: be married or thinking about.

849
00:42:47,040 --> 00:42:50,439
Speaker 4: It, yeah, or if you're blue already. Yeah I'm yeah.

850
00:42:50,640 --> 00:42:52,759
Speaker 4: Just take a really good good mood and be like

851
00:42:52,800 --> 00:42:54,960
Speaker 4: I'm sick of being in a good mood. Sit down

852
00:42:55,000 --> 00:42:56,279
Speaker 4: and watch Revolutionary Road.

853
00:42:56,360 --> 00:42:58,520
Speaker 1: Yeah. Watch Joe versus the Volcano instead.

854
00:42:58,640 --> 00:43:01,040
Speaker 4: Great movie. Uh.

855
00:43:01,120 --> 00:43:03,680
Speaker 1: Where was I smale? Horseshoe is what that's called?

856
00:43:04,360 --> 00:43:08,680
Speaker 2: And that was he was the first person to actually

857
00:43:08,800 --> 00:43:09,840
Speaker 2: use the word chaos.

858
00:43:10,000 --> 00:43:11,520
Speaker 5: Oh he was, I think so.

859
00:43:11,719 --> 00:43:14,359
Speaker 4: No, No, No, York was Tom York's dad.

860
00:43:14,640 --> 00:43:16,760
Speaker 1: Yeah, you're right, he wasn't the first person York correct.

861
00:43:16,880 --> 00:43:19,680
Speaker 4: But SMaL's horseshoe illustrates a really good point.

862
00:43:19,760 --> 00:43:21,160
Speaker 1: Chuck, is it Tom York's dad?

863
00:43:21,480 --> 00:43:26,280
Speaker 4: No, Okay, no, but they're both British, Sure, Yorky's actually

864
00:43:26,280 --> 00:43:33,200
Speaker 4: one's Australian. Nope, they're British. So those two points which

865
00:43:33,200 --> 00:43:35,440
Speaker 4: should which started out right by each other, and then

866
00:43:35,560 --> 00:43:37,240
Speaker 4: end up in two totally different places.

867
00:43:37,320 --> 00:43:37,720
Speaker 1: Yeah.

868
00:43:37,800 --> 00:43:40,759
Speaker 4: That applies not just to bread dough, but also too

869
00:43:40,880 --> 00:43:44,080
Speaker 4: things like water molecules that are right next to each

870
00:43:44,120 --> 00:43:47,759
Speaker 4: other at some point and then a month later they're

871
00:43:47,800 --> 00:43:50,799
Speaker 4: in two different oceans, even though you would assume that

872
00:43:50,840 --> 00:43:53,120
Speaker 4: they would go through all the same motions and everything.

873
00:43:53,239 --> 00:43:54,120
Speaker 5: Oh, sure, but they're not.

874
00:43:54,160 --> 00:43:57,600
Speaker 4: There's so many different variables with things like ocean currents

875
00:43:57,640 --> 00:44:00,479
Speaker 4: that two water molecules that were one side by side

876
00:44:00,560 --> 00:44:04,560
Speaker 4: end up in totally random different places. Yeah, and that's

877
00:44:04,600 --> 00:44:09,839
Speaker 4: part of chaos. It's basically chaos personified yea, or chaos

878
00:44:09,880 --> 00:44:11,000
Speaker 4: molecule fied.

879
00:44:12,360 --> 00:44:13,360
Speaker 1: So we mentioned York.

880
00:44:13,960 --> 00:44:16,600
Speaker 2: Where I was going with that was there was an

881
00:44:16,600 --> 00:44:21,560
Speaker 2: Australian named Robert May, and he was a population biologist,

882
00:44:22,040 --> 00:44:25,640
Speaker 2: so he was using math to model how animal populations

883
00:44:25,680 --> 00:44:30,640
Speaker 2: would change over time giving certain starting conditions. So he

884
00:44:30,719 --> 00:44:36,000
Speaker 2: started using these equations as differential equations, and he came

885
00:44:36,080 --> 00:44:38,640
Speaker 2: up with a formula known as the logistic difference equation

886
00:44:39,480 --> 00:44:43,600
Speaker 2: that basically enabled him to predict these animal populations pretty well.

887
00:44:44,560 --> 00:44:47,320
Speaker 4: Yeah, and it was working pretty well for a while,

888
00:44:47,360 --> 00:44:51,000
Speaker 4: but he noticed something really really weird, right, Yeah, he

889
00:44:51,080 --> 00:44:55,400
Speaker 4: had this formula, the logistic difference in equation is the

890
00:44:55,480 --> 00:44:58,120
Speaker 4: name of it. Sure, Okay, So we had that formula,

891
00:44:58,200 --> 00:45:02,600
Speaker 4: and he figured out that if you took R, which

892
00:45:02,640 --> 00:45:07,000
Speaker 4: in this case was the reproductive rate of an animal population, yeah,

893
00:45:07,320 --> 00:45:09,400
Speaker 4: and you pushed it past three.

894
00:45:09,400 --> 00:45:10,240
Speaker 1: The number three.

895
00:45:10,440 --> 00:45:14,680
Speaker 4: So that meant that the average animal in this population

896
00:45:14,760 --> 00:45:19,880
Speaker 4: of animals had three offspring in its lifetime or in

897
00:45:19,920 --> 00:45:22,799
Speaker 4: a season whatever. If you pushed it past three, all

898
00:45:22,840 --> 00:45:28,640
Speaker 4: of a sudden, the number of the population would diverge.

899
00:45:28,760 --> 00:45:31,520
Speaker 1: Yeah, if you pushed it equal to three actually, or more.

900
00:45:31,640 --> 00:45:34,840
Speaker 4: Right, it would diverge. Yeah, which is weird because a

901
00:45:34,920 --> 00:45:38,799
Speaker 4: population of animals can't be two different numbers, you know,

902
00:45:39,280 --> 00:45:42,600
Speaker 4: like that herd of antelope is not there's not thirty,

903
00:45:42,719 --> 00:45:45,600
Speaker 4: but there's also forty five of them at the same time. Yeah,

904
00:45:45,719 --> 00:45:48,360
Speaker 4: that's called a superposition, and that has to do with

905
00:45:48,440 --> 00:45:52,120
Speaker 4: quantum states, not herds of antelopes.

906
00:45:52,200 --> 00:45:54,120
Speaker 5: Sure, that was kind of weird.

907
00:45:54,480 --> 00:45:56,320
Speaker 4: And then he found if you pushed it a little further,

908
00:45:56,360 --> 00:46:00,280
Speaker 4: if you made the reproductive rate like three point oh five,

909
00:46:00,000 --> 00:46:03,000
Speaker 4: five seven or something like that. I think it was

910
00:46:03,040 --> 00:46:05,759
Speaker 4: a different number, but you just tweaked it a little bit, not.

911
00:46:05,760 --> 00:46:06,400
Speaker 5: Even to four.

912
00:46:06,440 --> 00:46:09,120
Speaker 1: We're talking like, oh yeah, millions.

913
00:46:08,640 --> 00:46:13,279
Speaker 4: Of a of a degree. All of a sudden it

914
00:46:13,280 --> 00:46:16,200
Speaker 4: would turn into four. So there'd be four different numbers

915
00:46:16,239 --> 00:46:18,480
Speaker 4: for that was the animal population, and then we would

916
00:46:18,480 --> 00:46:20,520
Speaker 4: turn into sixteen. And then all of a sudden, after

917
00:46:20,520 --> 00:46:23,160
Speaker 4: a certain point, it would turn into chaos. The number

918
00:46:23,160 --> 00:46:25,400
Speaker 4: would be everything at once, all over the place, just

919
00:46:25,440 --> 00:46:28,960
Speaker 4: totally random numbers that it oscillated between.

920
00:46:29,160 --> 00:46:32,320
Speaker 2: Yeah, but in all that chaos, there would be periods

921
00:46:32,360 --> 00:46:33,000
Speaker 2: of stability.

922
00:46:33,160 --> 00:46:34,800
Speaker 4: Right, you push it a little further, and all of

923
00:46:34,800 --> 00:46:36,960
Speaker 4: a sudden it would just go to two again. Yeah,

924
00:46:37,200 --> 00:46:39,719
Speaker 4: but beyond that, it didn't go back to the original

925
00:46:39,760 --> 00:46:41,960
Speaker 4: two numbers. It went to another two. So if you

926
00:46:41,960 --> 00:46:45,000
Speaker 4: looked at it on a graph, it went line divided

927
00:46:45,040 --> 00:46:49,439
Speaker 4: into two, divided into four eight sixteen chaos two four

928
00:46:49,560 --> 00:46:53,799
Speaker 4: sixteen two four eight sixteen chaos all before you even

929
00:46:53,840 --> 00:46:56,799
Speaker 4: got to the number four of the reproductive rate.

930
00:46:57,280 --> 00:47:00,320
Speaker 2: Yeah, And he was working with mister York because he

931
00:47:00,360 --> 00:47:02,760
Speaker 2: was a little confounded. So he was a mathematician buddy

932
00:47:02,760 --> 00:47:06,000
Speaker 2: of his, James York from the University of Maryland. So

933
00:47:06,080 --> 00:47:09,080
Speaker 2: they worked together on this, and in nineteen seventy five

934
00:47:09,120 --> 00:47:14,120
Speaker 2: they co authored a paper called Period three Implies Chaos

935
00:47:14,200 --> 00:47:18,839
Speaker 2: and man, finally somebody said the word I kept thinking

936
00:47:18,880 --> 00:47:19,920
Speaker 2: it was all these other people.

937
00:47:20,120 --> 00:47:24,480
Speaker 4: Yeah, And this paper where they first debuted the name chaos.

938
00:47:25,960 --> 00:47:30,880
Speaker 4: They they based it Tom York's dead based it on

939
00:47:31,560 --> 00:47:32,720
Speaker 4: Edward Lawrence's paper.

940
00:47:33,200 --> 00:47:33,399
Speaker 3: Yeah.

941
00:47:33,440 --> 00:47:35,080
Speaker 4: He was like, you know what, I have a feeling

942
00:47:35,160 --> 00:47:37,200
Speaker 4: that has something to do with the Lawrens attractor.

943
00:47:37,600 --> 00:47:38,440
Speaker 3: So that.

944
00:47:40,280 --> 00:47:43,760
Speaker 4: Provided chaos to the world. And it was the basically

945
00:47:43,800 --> 00:47:49,040
Speaker 4: the third the third time a scientist had said we

946
00:47:49,480 --> 00:47:52,719
Speaker 4: don't understand the universe like we think we do, and

947
00:47:52,760 --> 00:47:56,040
Speaker 4: determinism is based on an illusion like don't you get

948
00:47:56,040 --> 00:48:01,520
Speaker 4: it of order in a really chaotic universe? And this

949
00:48:01,760 --> 00:48:04,000
Speaker 4: established chaos. It took off like a rocket in the

950
00:48:04,120 --> 00:48:06,319
Speaker 4: eighties and the nineties. You know, as you know from

951
00:48:06,400 --> 00:48:10,120
Speaker 4: Jurassic Park, chaos was everything everybody's like chaos, this is

952
00:48:10,360 --> 00:48:13,200
Speaker 4: totally awesome, it's the new frontier of science. And then

953
00:48:13,239 --> 00:48:15,440
Speaker 4: it just went It just went away, And a lot

954
00:48:15,440 --> 00:48:19,160
Speaker 4: of people said, well, it was a little overhyped, but

955
00:48:19,239 --> 00:48:21,840
Speaker 4: I think more than anything, and I think this is

956
00:48:21,920 --> 00:48:24,000
Speaker 4: kind of the current understanding of chaos because it didn't

957
00:48:24,000 --> 00:48:26,320
Speaker 4: actually go away. It became a deeper and deeper field.

958
00:48:26,520 --> 00:48:32,720
Speaker 4: As you'll see, people mistook what chaos meant. It wasn't

959
00:48:32,840 --> 00:48:37,960
Speaker 4: the new type of science. It was a new understanding

960
00:48:38,040 --> 00:48:40,160
Speaker 4: of the universe. It was saying like, yes, you can

961
00:48:40,200 --> 00:48:41,920
Speaker 4: still use Newtonian physics.

962
00:48:42,120 --> 00:48:44,520
Speaker 2: Yeah, like don't throw everything out the windows. No, you

963
00:48:44,560 --> 00:48:46,400
Speaker 2: can still try and predict weather and still try and

964
00:48:46,400 --> 00:48:50,240
Speaker 2: build more accurate instruments and get you know, decent results.

965
00:48:50,640 --> 00:48:57,320
Speaker 2: But you can't with absolute perfection predict complex systems like determinism.

966
00:48:57,440 --> 00:49:01,439
Speaker 4: The ultimate goal of determinism is false. It can never

967
00:49:01,480 --> 00:49:04,080
Speaker 4: be it can never be done because we can't have

968
00:49:04,120 --> 00:49:08,280
Speaker 4: an infinitely precise measurement for every variable or any variable. Therefore,

969
00:49:08,280 --> 00:49:11,160
Speaker 4: we can't predict these outcomes. Right, So you would expect

970
00:49:11,200 --> 00:49:14,120
Speaker 4: science to be like, what's the point, what's.

971
00:49:13,960 --> 00:49:14,800
Speaker 5: The point of anything?

972
00:49:14,880 --> 00:49:15,720
Speaker 1: No, not science.

973
00:49:15,880 --> 00:49:19,759
Speaker 4: Well, some some chaos people have said no, this is

974
00:49:19,880 --> 00:49:23,719
Speaker 4: this is great, this is good. We'll take this. We'll

975
00:49:23,760 --> 00:49:26,799
Speaker 4: take the universe as it is, rather than trying to

976
00:49:26,840 --> 00:49:30,120
Speaker 4: force it into our pretty little equations and saying like,

977
00:49:30,520 --> 00:49:33,239
Speaker 4: if the ocean temperature is this at this time of

978
00:49:33,320 --> 00:49:36,440
Speaker 4: year and the fish population is this at that time,

979
00:49:36,680 --> 00:49:40,000
Speaker 4: then this is how many offspring this fish stop, this

980
00:49:40,040 --> 00:49:45,160
Speaker 4: fish population is going to have. Say okay, here is

981
00:49:45,239 --> 00:49:48,640
Speaker 4: the fish population, Here is the ocean temperature, here are

982
00:49:48,640 --> 00:49:51,120
Speaker 4: all these other variables. Let's feed it into a model

983
00:49:51,320 --> 00:49:54,840
Speaker 4: and see what happens. Not this is going to happen.

984
00:49:55,360 --> 00:49:59,080
Speaker 4: What happens instead, And this is kind of the understanding

985
00:49:59,120 --> 00:50:03,279
Speaker 4: of chaos there. Now, it's taking raw data, as much

986
00:50:03,400 --> 00:50:05,719
Speaker 4: data as you can possibly get your hands on, as

987
00:50:05,800 --> 00:50:07,840
Speaker 4: precise data as you could possibly get your hands on,

988
00:50:08,080 --> 00:50:10,600
Speaker 4: and just feeding it into a model and seeing what

989
00:50:10,719 --> 00:50:15,640
Speaker 4: patterns emerge. Rather than making assumptions, it's saying, what's the outcome?

990
00:50:15,640 --> 00:50:17,000
Speaker 4: What comes out of this model?

991
00:50:17,200 --> 00:50:20,200
Speaker 2: Yeah, And that's why, like when you see things like

992
00:50:20,360 --> 00:50:22,560
Speaker 2: you know, fifty years ago they predicted this animal be

993
00:50:22,600 --> 00:50:27,400
Speaker 2: its extinct and it's not. Well, it's because the variations

994
00:50:27,440 --> 00:50:31,160
Speaker 2: were too complex, right, They tried to predict And that's

995
00:50:31,160 --> 00:50:36,359
Speaker 2: why if you look at a ten day forecast, you, sir,

996
00:50:36,440 --> 00:50:40,600
Speaker 2: are a fool, Right, it's true, Well, ten days from

997
00:50:40,600 --> 00:50:42,520
Speaker 2: now it says it's going to rain in the afternoon.

998
00:50:42,960 --> 00:50:43,279
Speaker 1: Come on.

999
00:50:43,840 --> 00:50:46,640
Speaker 4: But if you take if you took enough variables for

1000
00:50:46,840 --> 00:50:50,160
Speaker 4: weather for like a city, and fed it into a

1001
00:50:50,280 --> 00:50:53,760
Speaker 4: model of the weather for that city, you could find

1002
00:50:55,160 --> 00:50:58,920
Speaker 4: you could find a time when it was similar to

1003
00:50:59,080 --> 00:51:01,640
Speaker 4: what it is now. Yeah, and you could conceivably make

1004
00:51:01,719 --> 00:51:04,960
Speaker 4: some assumptions based on that. You can say, well, actually

1005
00:51:05,239 --> 00:51:07,480
Speaker 4: we can, we can predict a little further out than

1006
00:51:07,520 --> 00:51:12,080
Speaker 4: we think. But it's it's based on this theory, this

1007
00:51:12,200 --> 00:51:17,760
Speaker 4: understanding of chaos, of unpredictability, of not just not forcing

1008
00:51:17,880 --> 00:51:21,880
Speaker 4: nature into our formulas, but putting data into a model

1009
00:51:21,920 --> 00:51:23,360
Speaker 4: and seeing what comes out of it.

1010
00:51:23,520 --> 00:51:23,719
Speaker 1: Yeah.

1011
00:51:23,760 --> 00:51:25,520
Speaker 2: And then at the end of that you learn, like

1012
00:51:25,600 --> 00:51:28,680
Speaker 2: when that animal is not extinct like you thought it

1013
00:51:28,719 --> 00:51:30,600
Speaker 2: would be, you go back and look at the original thing,

1014
00:51:30,640 --> 00:51:33,640
Speaker 2: and you have a more accurate picture of how the

1015
00:51:34,280 --> 00:51:38,400
Speaker 2: data could have been off slightly this one value, and

1016
00:51:38,440 --> 00:51:40,719
Speaker 2: then you have more buffalo than you think.

1017
00:51:41,600 --> 00:51:45,000
Speaker 4: Yeah, sure you got buffaloed by chaos.

1018
00:51:45,160 --> 00:51:47,640
Speaker 1: And we're not even getting into fractals. That's a whole

1019
00:51:47,680 --> 00:51:48,120
Speaker 1: other thing.

1020
00:51:48,160 --> 00:51:51,080
Speaker 2: And we did a whole other podcast in June twenty

1021
00:51:51,080 --> 00:51:58,080
Speaker 2: twelve about fractals and the mandal while mandel Brett mendel Brett, Mandelbrot, Yeah,

1022
00:51:58,280 --> 00:52:01,520
Speaker 2: and go listen to that one and hear me clinging

1023
00:52:01,520 --> 00:52:03,560
Speaker 2: to the edge of a clift.

1024
00:52:02,640 --> 00:52:07,520
Speaker 4: Yet, man, we should end this. But first I want

1025
00:52:07,560 --> 00:52:10,440
Speaker 4: to say, there is a really interesting article it's pretty

1026
00:52:10,520 --> 00:52:17,319
Speaker 4: understandable on Quanta magazine about a guy named George Sugihara

1027
00:52:18,280 --> 00:52:22,920
Speaker 4: and he is a chaos theory dude who's got a

1028
00:52:22,960 --> 00:52:25,880
Speaker 4: whole lab and is applying it to real life. So

1029
00:52:26,719 --> 00:52:30,800
Speaker 4: it's a really good picture of chaos theory and action.

1030
00:52:31,760 --> 00:52:35,000
Speaker 4: Go check it out. Okay, if you want to know

1031
00:52:35,040 --> 00:52:39,160
Speaker 4: more about chaos theory, I hope your brain's not broken.

1032
00:52:39,239 --> 00:52:42,719
Speaker 1: Yeah, go take some LSD and look at fractals. Don't

1033
00:52:42,719 --> 00:52:42,960
Speaker 1: do that.

1034
00:52:43,920 --> 00:52:47,360
Speaker 4: You can type those words into how stuff works in

1035
00:52:47,400 --> 00:52:50,960
Speaker 4: the search bar any of those fractals, LSD chaos. It'll

1036
00:52:51,000 --> 00:52:53,920
Speaker 4: bring up some good stuff. And since I said good stuff,

1037
00:52:53,960 --> 00:52:55,319
Speaker 4: it's time for a listener mail.

1038
00:52:57,480 --> 00:53:01,040
Speaker 1: I'm gonna call this rare shout out get requests all

1039
00:53:01,080 --> 00:53:01,600
Speaker 1: the time.

1040
00:53:01,480 --> 00:53:05,200
Speaker 4: And bet I know each one really Yeah dude and

1041
00:53:05,200 --> 00:53:08,520
Speaker 4: his girlfriend. Yeah, no, so far so good.

1042
00:53:09,440 --> 00:53:10,920
Speaker 2: Hey, guys, just wanted to say I think you're doing

1043
00:53:10,960 --> 00:53:13,799
Speaker 2: a wonderful job with the show to this date. My

1044
00:53:13,920 --> 00:53:16,000
Speaker 2: first time listening was during my first deployment.

1045
00:53:17,040 --> 00:53:18,880
Speaker 5: Yeah yeah, yeah, when.

1046
00:53:18,760 --> 00:53:22,120
Speaker 2: I listened to your list on famous and influential films,

1047
00:53:22,440 --> 00:53:25,080
Speaker 2: I was hooked after that. Since I came back stateside,

1048
00:53:25,120 --> 00:53:27,960
Speaker 2: have spent many hours driving to and fro to see

1049
00:53:28,000 --> 00:53:31,480
Speaker 2: my girlfriend to my barracks, and I can happily say

1050
00:53:31,520 --> 00:53:34,720
Speaker 2: that they've been made all the more enjoyable by listening

1051
00:53:34,760 --> 00:53:35,320
Speaker 2: to you guys.

1052
00:53:35,640 --> 00:53:36,080
Speaker 4: That's good.

1053
00:53:36,360 --> 00:53:38,480
Speaker 2: Even my girlfriend Rachel has warmed up to you dudes,

1054
00:53:39,000 --> 00:53:41,120
Speaker 2: which was not a pleasant I'm sorry, which was a

1055
00:53:41,160 --> 00:53:44,200
Speaker 2: pleasant shock to me. She has told me repeatedly that

1056
00:53:44,239 --> 00:53:48,520
Speaker 2: she cannot listen to audiobooks because quote, hearing people talk

1057
00:53:48,640 --> 00:53:49,560
Speaker 2: on the radio.

1058
00:53:49,400 --> 00:53:51,200
Speaker 1: Gives me a headache end quote.

1059
00:53:51,520 --> 00:53:53,920
Speaker 2: Anyway, I hope you guys continue to make awesome podcasts

1060
00:53:54,160 --> 00:53:56,680
Speaker 2: as I'm headed out on my next deployment. And if

1061
00:53:56,719 --> 00:53:58,319
Speaker 2: you could give a shout out to Rachel, I'm sure

1062
00:53:58,320 --> 00:54:00,360
Speaker 2: it would make her feel a little better that I

1063
00:54:00,400 --> 00:54:03,480
Speaker 2: got the pleasant people on the podcast to reaffirm how

1064
00:54:03,560 --> 00:54:08,799
Speaker 2: much I love her. That is, John, Rachel, hang in there, John,

1065
00:54:08,960 --> 00:54:12,120
Speaker 2: be safe and uh, thanks for listening.

1066
00:54:12,200 --> 00:54:14,080
Speaker 4: Yeah, man, thank you, that is a great email. I

1067
00:54:14,120 --> 00:54:14,600
Speaker 4: love that one.

1068
00:54:14,640 --> 00:54:16,719
Speaker 1: Glad we don't give you a headache. Rachel, Yeah for real,

1069
00:54:16,800 --> 00:54:18,040
Speaker 1: she listens to this one, She's.

1070
00:54:17,920 --> 00:54:20,480
Speaker 4: Like, Okay, oh yeah, everybody's gonna get a headache from

1071
00:54:20,520 --> 00:54:22,840
Speaker 4: this one. Like I came to hate the sound of

1072
00:54:22,880 --> 00:54:24,240
Speaker 4: my own voice from this one.

1073
00:54:24,480 --> 00:54:25,239
Speaker 1: Ah, you'll be right.

1074
00:54:27,280 --> 00:54:28,719
Speaker 4: If you want to get in touch with us, you

1075
00:54:28,760 --> 00:54:31,120
Speaker 4: can hang out with us on Twitter at s y

1076
00:54:31,280 --> 00:54:33,880
Speaker 4: s K podcast. Same goes for Instagram. You can hang

1077
00:54:33,920 --> 00:54:35,800
Speaker 4: out with us on Facebook dot com slash stuff you

1078
00:54:35,800 --> 00:54:38,200
Speaker 4: Should Know. You can send us an email to stuff

1079
00:54:38,239 --> 00:54:41,120
Speaker 4: podcast at HowStuffWorks dot com and has always joined us

1080
00:54:41,120 --> 00:54:43,080
Speaker 4: at our home on the web, Stuff you Should Know

1081
00:54:43,280 --> 00:54:49,799
Speaker 4: dot com.

1082
00:54:49,840 --> 00:54:52,400
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1083
00:54:52,400 --> 00:54:59,880
Speaker 5: HowStuffWorks dot com.