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How Chaos Theory Changed the Universe · Stuff You Should Know

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Speaker 1: This episode of Stuff You Should Know is sponsored by Squarespace.
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Speaker 1: website apart. Welcome to Stuff you Should Know from House
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Speaker 1: Stuff Works dot com. Hey, and welcome to the podcast.
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Speaker 1: I'm Josh Clark with Charles W. Chuck Bryant, and there's
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Speaker 1: Jerry over there. So this is Stuff you Should Know,
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Speaker 1: the podcast about chaos theory. Like, uh, have you ever
0:39
Speaker 1: seen Event Horizon? I did not bad great movie? Are
0:44
Speaker 1: you crazy? I don't think it was great? Oh so
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Speaker 1: imagine it it. I thought it was okay. It was
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Speaker 1: like a love crafty and thing in our space. Loved
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Speaker 1: it all right, I love crafted it. I liked it. Um.
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Speaker 1: That's what I think of when I think of k US.
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Speaker 1: You know, there's that one part where they kind of
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Speaker 1: give you like a glimpse behind, like the dimension that
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Speaker 1: this action is taking place in, to see the chaos underneath,
1:10
Speaker 1: and you should check that out again. I think about
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Speaker 1: Jurassic Park and Jeff Goldblum as as the creep. Dr
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Speaker 1: Malcolm explaining chaos in the little auto driving suv or
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Speaker 1: whatever that was. Yeah, that's what it was called in
1:30
Speaker 1: the script, the auto driving suv scene. Yeah, and you
1:33
Speaker 1: know what, he actually rewatched that scene and it confirmed
1:36
Speaker 1: two things. One is that he uh, he actually did
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Speaker 1: a pretty decent job for a Hollywood movie with a
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Speaker 1: very rudimentary explanation of chaos. Um, and you watched it
1:46
Speaker 1: for this Yeah, yeah, just that scene. And then it
1:49
Speaker 1: also confirmed of what a creep that character was. Yeah,
1:53
Speaker 1: if you watched that scene, he's like, you know, he's
1:56
Speaker 1: all gross and flirty with her right in front of
1:58
Speaker 1: her ex but there's just you know, he's talking to her.
2:01
Speaker 1: I didn't even notice this at first. He like he
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Speaker 1: just like touches her hair out of nowhere for no reason.
2:07
Speaker 1: He's just talking to her and he just like grabs
2:09
Speaker 1: her hair and touches it. And I'm like, what a creep.
2:12
Speaker 1: I know, if you look closely, you can see the
2:14
Speaker 1: hormones emerging through his chest hair. Yeah, and I love
2:18
Speaker 1: Jeff Goldblum. It's not a reflection on him. He was
2:21
Speaker 1: basically doing Jeff Goldblum. Well that's what Yeah, sure he's
2:25
Speaker 1: Jeff Goldblum, but I don't think that's how in the
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Speaker 1: manner in which he speaks. But I don't think he's
2:29
Speaker 1: a creep, do you. Wow, I've got nothing against Jeff
2:34
Speaker 1: gold Okay, I think he's a I think he's doing
2:37
Speaker 1: Jeff Goldblum. It was also a sign of the times,
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Speaker 1: like if that movie were made today, doctor what was
2:44
Speaker 1: her name in the movie? Yeah, Dr Sutler would be like,
2:47
Speaker 1: it's very inappropriate to stroke my hair, like, don't touch me.
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Speaker 1: But this was the nineties. Was eight No, it was nineties.
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Speaker 1: It was the early mid ninety. Other thing, the book
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Speaker 1: came out and in the book, uh Ian Malcolm, who's
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Speaker 1: a Kayetician creep Kaetician? Right he um he he goes
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Speaker 1: into even more depth about chaos. But that was I mean,
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Speaker 1: that was the first time I ever heard of chaos
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Speaker 1: theory was from Jurassic Parks, and um it really it
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Speaker 1: was really misleading. I think the entire term chaos is
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Speaker 1: very misleading as far as the general public goes as
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Speaker 1: from what I researched in this this for this article. Well, yeah,
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Speaker 1: I mean you hear the word chaos as an English
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Speaker 1: speaker and you think frenetic and crazy, out of control. Yeah,
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Speaker 1: and that's not what it means in terms of science
3:46
Speaker 1: like this, right. What it means, I guess we can
3:48
Speaker 1: say up front is is basically the idea that complex
3:53
Speaker 1: systems do not behave in very neat ways that we
3:58
Speaker 1: can easily grasp, understand, ander measure, right, and not even
4:03
Speaker 1: even simple systems don't. Sometimes it doesn't always have to
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Speaker 1: be complex. But um, I want to give a shout
4:09
Speaker 1: out in addition to our own article to UH when
4:13
Speaker 1: you know, when it comes to stuff like this, the
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Speaker 1: brain breaking stuff. For me, this is a breaker. You
4:18
Speaker 1: know how I always go to like blank blank for
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Speaker 1: kids because it always helps. If there's a dinosaur mascot
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Speaker 1: on the page, it's a sure thing we can understand it.
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Speaker 1: But the best explanation for all this stuff that I
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Speaker 1: found on the internet was from a website called a
4:35
Speaker 1: bar Um A B A R I. M. Publications, which
4:39
Speaker 1: turns out to be a website about biblical patterns and
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Speaker 1: sandwiched in the middle, there is a really great, easy
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Speaker 1: to understand UH series of pages on chaos there. So
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Speaker 1: I was like, man, I get it now in a
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Speaker 1: rudimentary way, right, Well, yeah, um, I think even a
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Speaker 1: lot of people who deal with systems that display chaotic behavior,
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Speaker 1: which I guess is to say basically all systems eventually
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Speaker 1: under the right conditions, Um, don't necessarily understand chaos. Yeah,
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Speaker 1: And they defined a complex system is specifically. It doesn't
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Speaker 1: mean just like, oh it's complex, I mean it is,
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Speaker 1: but specifically, Um, they define it in a way that
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Speaker 1: helped me understand it's a system that has so much motion,
5:26
Speaker 1: so many elements that are in motion, moving parts. Yeah,
5:29
Speaker 1: that it takes like a computer to calculate all the
5:33
Speaker 1: possibilities of like what that could look like five minutes
5:36
Speaker 1: from now, ten years from now. So before computers came around,
5:41
Speaker 1: we before the quantum mechanical revolution, it was there's a
5:46
Speaker 1: lot more basic. It was like what comes up must
5:48
Speaker 1: come down, stuff like that. Let's talk about that, Chuckers,
5:51
Speaker 1: because when you're talking about chaos theory, it helps to
5:54
Speaker 1: understand how it revolutionized the universe by getting a clear
6:00
Speaker 1: picture of how we understood the universe leading up to
6:03
Speaker 1: the discovery of chaos. Right, So, prior to the um
6:10
Speaker 1: the scientific revolution, everybody was like, oh, well, it's it's God.
6:14
Speaker 1: The Earth is at the center of the universe and
6:16
Speaker 1: God is spinning everything around like a top right, it
6:19
Speaker 1: was all a theistic explanation. Then the scientific revolution happens
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Speaker 1: and people start applying things like math and making like
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Speaker 1: mathematical discoveries and and and figuring out that there are
6:32
Speaker 1: there's order. They're finding order in patterns and predictability to
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Speaker 1: the universe if you can apply mathematics to it, specifically,
6:42
Speaker 1: if you can apply mathematics to the starting point, right, right,
6:47
Speaker 1: So if you can, if you can um figure out
6:49
Speaker 1: how a system works mathematically speaking, right, you can go
6:53
Speaker 1: in and plug in whatever coordinates you want to and
6:57
Speaker 1: watch it go. You can predict what what the outcomes
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Speaker 1: can be and what this is the it's based on
7:03
Speaker 1: what at the time was a totally revolutionary idea um
7:07
Speaker 1: By Initially, I think the cart was the first one
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Speaker 1: to kind of say, causing effect is a pretty big
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Speaker 1: part of our universe, right. Yeah. It was sort of
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Speaker 1: like where this is sixteen hundreds, where early science met philosophy.
7:22
Speaker 1: They kind of complimented one another as far as something
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Speaker 1: that's we're talking about determinism, right, So that was the
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Speaker 1: kind of the seeds of determinism. Was the scientific revolution,
7:32
Speaker 1: And like you said, where philosophy and science came together
7:35
Speaker 1: in the form of Descartes, right, and then Newton came
7:38
Speaker 1: along and we did a whole episode on him. Yeah,
7:40
Speaker 1: January of this year. That was a good one. It
7:43
Speaker 1: was really good. Like I think you said in that
7:46
Speaker 1: episode that there's possibly no scienists that changed the world
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Speaker 1: more than Newton has. He's he's got legs. People shouted
7:53
Speaker 1: out others and email, but I'll just say he's at
7:57
Speaker 1: the near the top for sure with some other people.
7:59
Speaker 1: The Cream. Yeah, so Newton came along and Newton said
8:02
Speaker 1: that was his name, Isaac the Cream new anytime he
8:06
Speaker 1: don't to be like cream. Yeah, you just got creamed.
8:10
Speaker 1: I thought he was a boxer. He's a basketball player.
8:13
Speaker 1: He was much more well known as a boxer, but
8:15
Speaker 1: he definitely could dunk as a as a B baller. So,
8:20
Speaker 1: um man, that threw me off a little bit. Yeah,
8:24
Speaker 1: the Cream comes along and uh, he basically says, watch this, dude,
8:29
Speaker 1: this causing effect thing you're talking about, I can express
8:32
Speaker 1: it in quantifiable terms. And he comes up with all
8:36
Speaker 1: of these great laws and and basically sets the stage
8:39
Speaker 1: the foundation for science for the next three centuries or so. Yeah,
8:43
Speaker 1: these these laws that were so rock solid and powerful
8:48
Speaker 1: that scientists kind of got ahead of themselves a little
8:52
Speaker 1: and said we're done. Like with Newton's laws, we can predict, uh,
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Speaker 1: we can predict everything if we have a good enough
9:00
Speaker 1: beginning accurate value to plug into his equations, and they weren't.
9:05
Speaker 1: I think there was a little hubrius and a little
9:07
Speaker 1: just excitement about like, well we figured it all out
9:11
Speaker 1: right that that you could take Newton's laws and if
9:14
Speaker 1: you had accurate enough measurements, uh, you could predict what
9:19
Speaker 1: the outcome would be of that system that you plug
9:22
Speaker 1: those measurements into using these formula. And at the time,
9:25
Speaker 1: a lot of this was like planetary, like, well, we
9:28
Speaker 1: know that these planets are here and they're moving and
9:31
Speaker 1: their orbiting, So if we know these things, we can
9:34
Speaker 1: plug it into an equation and we can figure out
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Speaker 1: what it's going to be like in a hundred years exactly.
9:39
Speaker 1: And they figured out the basis of determinism is what
9:44
Speaker 1: we just said, that if you have accurate measurements, you
9:47
Speaker 1: can take those measurements and use them to predict um
9:50
Speaker 1: how a system is going to change over time using
9:53
Speaker 1: differential equations. Right, yeah, so this is what this is
9:56
Speaker 1: what Newton comes along and figures out that you can
9:59
Speaker 1: describe the uni us in these mathematical terms using differential
10:03
Speaker 1: equations and um like, you said there was a tremendous
10:06
Speaker 1: amount of hubris, and well, I think you said there's
10:10
Speaker 1: some hubris. I think there's a tremendous amount of hubris
10:12
Speaker 1: where science basically said, we've mastered the universe, We've uncovered
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Speaker 1: the blueprint of the universe, and now we understand everything.
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Speaker 1: It's just a matter now of getting our scientific measurements
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Speaker 1: more and more and more exact. Because again, the hallmark
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Speaker 1: of determinism is that if you have exact measurements, you
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Speaker 1: can predict an outcome accurately, like the pool queue example
10:35
Speaker 1: or the pool table example. Right right, So if you've
10:39
Speaker 1: got a pool table, let's say you're playing some nine ball.
10:42
Speaker 1: You have that beautiful little diamond set up, you got
10:45
Speaker 1: your cue ball, you put that cue ball, and you
10:48
Speaker 1: you crack it with the queue, and if you are
10:52
Speaker 1: super accurate with your initial measurements, you should be able
10:54
Speaker 1: to mathematically plot out via angles where the balls will
10:58
Speaker 1: end up, right exactly, like you can say, this is
11:01
Speaker 1: what the table will look like after the break. If
11:03
Speaker 1: you know the force the angle, all those little variable temperature,
11:08
Speaker 1: if there's wind in the room, like the felt on
11:10
Speaker 1: the table, like everything. The more specific you are, the
11:14
Speaker 1: more accurate your end result will be. Right. And then
11:16
Speaker 1: one of the other hallmarks of determinism is that if
11:18
Speaker 1: you take those exact same initial conditions and do them again,
11:23
Speaker 1: the table, the pool table will look exactly the same
11:26
Speaker 1: after the break, which is pretty much impossible for like
11:29
Speaker 1: a human to do with their hands. Sure, but the
11:32
Speaker 1: idea at the time of science is that if you
11:35
Speaker 1: could build a perfect machine, sure that could recreate these conditions,
11:39
Speaker 1: it will happen the same way every time. Right, And this,
11:42
Speaker 1: I mean this led to they had hubris, but you
11:45
Speaker 1: could understand it when like literally in two people predicted
11:53
Speaker 1: Neptune would exist within months, that would exist, but does exist.
11:57
Speaker 1: And this is not by looking up in the sky
11:59
Speaker 1: like they did it with math and they were right.
12:02
Speaker 1: So imagine in eighty when that happens, they're like, yeah,
12:07
Speaker 1: we kinda we've got the math down, so we're pretty
12:10
Speaker 1: much all knowing well. Plus also, for the most part,
12:13
Speaker 1: these not just with Neptune, they were finding um that
12:18
Speaker 1: this stuff really panned out. It held true for everything
12:21
Speaker 1: from um you know, the investigation into electricity to new
12:26
Speaker 1: chemical reactions and understanding those and it it laid the
12:30
Speaker 1: scientific revolution, laid the basis for the industrial revolution, and
12:34
Speaker 1: just the change that came out of the world like that.
12:37
Speaker 1: It definitely there. It is understandable how science kind of
12:41
Speaker 1: was like we got it all figured out well, and
12:43
Speaker 1: like you said, they even Galileo was smart enough to
12:48
Speaker 1: know there's uncertainty in these measurements, like the precision is key.
12:56
Speaker 1: So they spent what does the article say, a lot
12:58
Speaker 1: of the much of an enth and twenty century just
13:01
Speaker 1: trying to build better instrumentation to get more and more
13:05
Speaker 1: smaller and smaller and more precise measurements. Right, That was
13:09
Speaker 1: like basically the goal of it, right, Yeah, which was
13:11
Speaker 1: the right direction. That's like exactly what they should have
13:13
Speaker 1: been doing. The problem is there, Like you said, Galileo
13:18
Speaker 1: knew that there was some sort of there there gonna
13:21
Speaker 1: be some flaws and measurement that we just didn't have
13:25
Speaker 1: those great scientific instruments yet. It's called the uncertainty principle.
13:30
Speaker 1: It's accuracy, right, But the idea is that if you
13:36
Speaker 1: have a good enough instruments, you can overcome that, and
13:39
Speaker 1: that the the more you shrink the um error in
13:46
Speaker 1: measuring the initial conditions, the more you're gonna shrink the
13:50
Speaker 1: error in the outcome. It would be proportionate. Right. They
13:53
Speaker 1: were correct. The thing is they were also aware but
13:58
Speaker 1: ignoring in a lot a lot of ways some outstanding problems,
14:03
Speaker 1: specifically something called the end body problem. You know what,
14:08
Speaker 1: I'm so excited about this. I need to take a break.
14:10
Speaker 1: I think that's a good idea. I need to go
14:12
Speaker 1: check out my end body in the bathroom. Okay, and
14:17
Speaker 1: we'll be back. All right, check, we're back. So there's
14:37
Speaker 1: some there's some issues right with determinism. There's some some
14:41
Speaker 1: weird problems out there that are saying like, hey, pay
14:46
Speaker 1: attention to me because I'm not sure determinism works. Uh.
14:51
Speaker 1: And one of the one is the end body problem. Yeah.
14:53
Speaker 1: How this came about was that was King Oscar number
14:58
Speaker 1: two of Sweet and Norway. Yeah, I don't want to
15:03
Speaker 1: leave out Norway both. Uh. He said, you know what,
15:06
Speaker 1: let's offer a prize to anyone who can prove the
15:08
Speaker 1: stability of the solar system something that has been stable
15:13
Speaker 1: for a long time before that and a lot of
15:16
Speaker 1: the most brilliant minds on planet Earth got together and
15:19
Speaker 1: tried to do this, uh with mathematical proofs, and no
15:23
Speaker 1: one could do it. Uh. And then a dude name Honoree.
15:29
Speaker 1: You gotta help me there with that, Oh, say, the
15:33
Speaker 1: whole thing very nice. He was French, believe it or not,
15:38
Speaker 1: and he was a mathematician, and he said, you know what,
15:41
Speaker 1: I'm not gonna look at this big picture of all
15:44
Speaker 1: the planets in the sun and all their orbits. You'd
15:46
Speaker 1: have to be a fool to try that. Sure, he said,
15:48
Speaker 1: I'm gonna shrink this down, Like we talked about shrinking
15:51
Speaker 1: that initial value, you know, and um, that initial condition.
15:56
Speaker 1: He shrunk it down. He said, I'm gonna look at
15:58
Speaker 1: just a couple of bodies orbiting one another, uh, with
16:03
Speaker 1: a common center of gravity. And I'm gonna look at this.
16:06
Speaker 1: And this was called the N body problem. Yeah, which
16:09
Speaker 1: was smart to do, because the more variables you factor
16:13
Speaker 1: into um a nonlinear equation like that, just the harder
16:18
Speaker 1: it's gonna be. So he shrunk it down. So the
16:20
Speaker 1: N body problem has to do with three or more
16:24
Speaker 1: celestial bodies orbiting one another. So Plank said, I'll just
16:28
Speaker 1: start with three. Smart and what he found from doing
16:32
Speaker 1: his equations for this this King Oscar. The sequel prize
16:36
Speaker 1: um was that shrinking the initial conditions um measurement or
16:43
Speaker 1: rate of error right, did not really shrink the the
16:49
Speaker 1: error in the outcome, which flies in the face of determinism.
16:54
Speaker 1: What he found was that just very very minute different
17:00
Speaker 1: is in the initial conditions fed into a system produced
17:05
Speaker 1: wildly different outcomes after a fairly short time. Yeah, like,
17:09
Speaker 1: let me just round off the mass of this planet
17:11
Speaker 1: at like the eighth decimal point, and you know who cares?
17:16
Speaker 1: Who cares at that point? Let me just round that
17:18
Speaker 1: one to a two, and that would throw everything off
17:21
Speaker 1: at a at a pretty high rate. And he said,
17:25
Speaker 1: wait a minute, I think this contest is in polsib right,
17:30
Speaker 1: He said, there is no way to prove prove, to
17:34
Speaker 1: prove the stability of the Solar system, because he just
17:39
Speaker 1: uncovered the idea that it's impossible for us to predict
17:44
Speaker 1: the the the rate of change among celestial bodies. Yeah,
17:50
Speaker 1: it's such a complex system. There are far too many
17:53
Speaker 1: variables that, uh, it's impossible to start with something so
17:58
Speaker 1: minute to get the equation whatever the sum that you want. Well,
18:04
Speaker 1: not only that, but the result, not only that and
18:07
Speaker 1: this is what really undermined determinism was that he figured
18:11
Speaker 1: out that you would have to have an infinitely precise measurement,
18:18
Speaker 1: which even if you build a perfect machine that could
18:20
Speaker 1: take the infinitely or a perfect machine that could take
18:24
Speaker 1: a measurement of like the the movement of a celestial
18:27
Speaker 1: body around another, you, it's literally impossible to get infinite
18:34
Speaker 1: and infinitely precise measurement, which means that we could never
18:38
Speaker 1: predict out to a certain degree the movement of the
18:41
Speaker 1: celestial bodies. Like he was saying, like, no, you you
18:46
Speaker 1: can't get You can't build a machine that that gets
18:49
Speaker 1: measurements enough that we can overcome this, like determinism is wrong,
18:53
Speaker 1: Like you can't just say, uh, we have the understanding
18:58
Speaker 1: to predict everything. There's a lot of stuff out there
19:02
Speaker 1: that we're not able to predict. And he uncovered it
19:04
Speaker 1: trying to figure out this end body problem. Yeah, and
19:07
Speaker 1: King Oscar the sequel said you win, Yeah, bring me
19:10
Speaker 1: another rack of lamb and uh, here's your prize. And
19:13
Speaker 1: he won by proving that it was impossible, which is
19:16
Speaker 1: pretty interesting. And they utterly and completely changed not just math,
19:21
Speaker 1: but like our our our understanding of the universe, and
19:24
Speaker 1: our understanding of our understanding of the universe, which is
19:27
Speaker 1: even more kind of earth shaking. Yeah, he discovered dynamical
19:30
Speaker 1: instability or chaos, and um, they didn't have supercomputers at
19:36
Speaker 1: the time, so it would be a little while, about
19:38
Speaker 1: seventy years at m I T until uh we could
19:43
Speaker 1: actually kind of feed these things into machines capable of
19:47
Speaker 1: plotting these things out in a way that we could see,
19:50
Speaker 1: which was really incredible. So there is this dude um
19:54
Speaker 1: seven years later, uh named um Edward Lawrence Lawrence. Yeah. Well,
20:01
Speaker 1: first of all, we should set the stage the reason
20:03
Speaker 1: this guy he was a meteorologist and scientists, right, not
20:08
Speaker 1: that those are not the same thing, right, He's a
20:11
Speaker 1: scientist who dabbled the meteorology. Here was a mathematician, Yeah,
20:15
Speaker 1: but he was really into meteorology because it was there
20:18
Speaker 1: was a weird juxtaposition at the time where we were
20:22
Speaker 1: sending people into outer space but we couldn't predict the weather. Yeah,
20:25
Speaker 1: and it was it was definitely a blot on the
20:28
Speaker 1: field of meteorology. People were like, do you guys know
20:31
Speaker 1: what you're doing? And and meteorologists are like, you have
20:35
Speaker 1: no idea how hard this is? Like yeah, we can
20:38
Speaker 1: predict it a couple of days out, but after that,
20:40
Speaker 1: it's just it's totally unpredictable. It drives as mad and
20:44
Speaker 1: it's not. It wasn't just there. Um, their reputations that
20:47
Speaker 1: were at stake, like people were losing their lives because
20:50
Speaker 1: of it, right, Yeah. N two there were two notorious storms,
20:53
Speaker 1: one on the East coast and one on the west. Uh,
20:56
Speaker 1: the ash Wednesday storm in the East and the big
20:58
Speaker 1: blow on the West that of a lot of people,
21:00
Speaker 1: cost hundreds of millions of dollars in damage. And people
21:04
Speaker 1: were like, you know, we need to be able to
21:05
Speaker 1: see these things coming a little more because it's a problem.
21:09
Speaker 1: And meteorologists were like, why did you do it then?
21:13
Speaker 1: So they thought the key was these big supercomputers. Remember
21:17
Speaker 1: the supercomputers. When they came out the big rooms full
21:20
Speaker 1: of hardware, it was amazing, and they were finally able
21:24
Speaker 1: to do like these incredible calculations that we could never
21:27
Speaker 1: do before. I know, they were able to like crunch
21:29
Speaker 1: sixty four bites a second. Yeah, we had the advocates
21:32
Speaker 1: and then the supercomputer. There was nothing in between. Um.
21:36
Speaker 1: I looked up the computer that Lawrence was working with
21:39
Speaker 1: the Whopper Royal McBee. What was the Whopper board games?
21:43
Speaker 1: Was it? It called the Whopper w a PR I
21:47
Speaker 1: can't believe they called it that. So the guy just
21:50
Speaker 1: nicknamed it Joshua. No, Joshua was the the software Falcon
21:57
Speaker 1: was the old man who designed all the stuff up
22:00
Speaker 1: and his son was Joshua. And that was the password. Oh,
22:03
Speaker 1: that was the password. Yeah, I guess I was too
22:06
Speaker 1: young to understand what a password was. Okay, you didn't
22:09
Speaker 1: even there weren't passwords at the time. Shouted it at
22:12
Speaker 1: the computer and they're like, okay, access granted. Yeah. Still
22:16
Speaker 1: that movie holds up. Does it really check it out? Yeah,
22:20
Speaker 1: it's still very very fun. Young Ali Sheety boy had
22:23
Speaker 1: a crush on her from that movie. She was great. Yeah.
22:26
Speaker 1: What else was she in recently? Wasn't she in something? Well?
22:30
Speaker 1: I mean she kind of went away for a while
22:32
Speaker 1: and then had her big comeback with the indie movie
22:35
Speaker 1: High Art, But that was a while ago. Has she
22:38
Speaker 1: been in anything else recently? Sure? I think I saw
22:42
Speaker 1: something and something recently and I didn't realize that was her.
22:46
Speaker 1: She looks familiar and I was like, oh, that's Ali Sheety.
22:50
Speaker 1: I don't know, all right, I could look it up,
22:53
Speaker 1: but I won't. It doesn't matter anyway. I still crushed
22:56
Speaker 1: on her. So the the Royal mcbeebe was not quite
23:00
Speaker 1: the whopper. You could actually sit down at it. The
23:03
Speaker 1: Royal McBee that's the name of that sounds like a
23:05
Speaker 1: hamburger too. It was by the Royal Typewriter Company. And
23:10
Speaker 1: they got into computers for a second. And this is
23:12
Speaker 1: the kind of computer that Lawrence was working with, and
23:16
Speaker 1: it was a huge deal, Like you were saying, Avacus supercomputer. Um.
23:21
Speaker 1: But it was still pretty dumb as far as what
23:25
Speaker 1: we have today is concerned. But it was enough that
23:27
Speaker 1: Lawrence is like Lawrence and his ILK, where like, finally
23:31
Speaker 1: we can start running models and actually predict the weather. Yeah,
23:35
Speaker 1: he started doing just that. He did. So he started
23:38
Speaker 1: off with UM, a computational model of twelve meteorological meteorological
23:44
Speaker 1: I like how you calculations, which is very basic because
23:48
Speaker 1: they're infinite meteorological calculations, probably depending to stay wrong again,
23:54
Speaker 1: like it sounds like you're about to say it wrong
23:56
Speaker 1: and then you pull it out at the last second.
23:58
Speaker 1: Maybe it's really impressive, but uh so that's very basic.
24:01
Speaker 1: But he wanted to start out you know with something
24:03
Speaker 1: at Hannibal. So he narrowed it down to twelve conditions,
24:06
Speaker 1: basically twelve calculations that had you know, temperature, wind, speed, pressure,
24:12
Speaker 1: stuff like that started forecasting weather. Uh. And then he said,
24:16
Speaker 1: you know, it'd be great if you could see this,
24:18
Speaker 1: So I'm gonna spit it into my wonder machine, the
24:22
Speaker 1: McWhopper Royal MCB, and I'm gonna get a print out
24:27
Speaker 1: so you can visualize what this looks like. So things
24:31
Speaker 1: were going well and you had this print out, and
24:32
Speaker 1: everyone was amazed because these these calculations never seemed to
24:36
Speaker 1: repeat themselves. He was making like, um, like like word art.
24:43
Speaker 1: You remember that. That was the first thing anybody did
24:45
Speaker 1: on a computer. Oh yeah, it was to make word
24:47
Speaker 1: art like a butterfly, right you would print out. Yeah.
24:50
Speaker 1: I never could do that. I couldn't either, Like you
24:53
Speaker 1: have to be able to visualize things spatially that you
24:56
Speaker 1: have to that right kind of brain for that, right
24:59
Speaker 1: or you have to be following a guy book that
25:01
Speaker 1: you have you ever seen? Me? You and everyone we know. Yeah,
25:06
Speaker 1: I love that movie. That's a great movie. Those little
25:08
Speaker 1: kids in there they were doing that. Oh yeah, yeah, forever,
25:13
Speaker 1: back and forth, poop. Well, I haven't. I haven't seen
25:15
Speaker 1: that since it came out. It's been a while. Oh
25:17
Speaker 1: you gotta see it again? Yeah, great movie. Ali's not
25:21
Speaker 1: in it. It's a Miranda July right, and she like
25:24
Speaker 1: wrote and directed to right. She did a great job.
25:26
Speaker 1: It's like it's one of those rare movies where like
25:31
Speaker 1: there's just the right amount of whimsy, because whimsy so
25:35
Speaker 1: easily overpowers everything else and becomes like, yeah, this is
25:40
Speaker 1: like the most perfectly balanced amount of like whimsy you've
25:43
Speaker 1: ever seen in a movie. Yeah, there's too much whimsy.
25:45
Speaker 1: I just like terrible Garden State. I just want to
25:47
Speaker 1: punch in the face terrible. Although I like Garden State,
25:50
Speaker 1: but I haven't seen it since it came out. It
25:52
Speaker 1: hasn't aged. Well, it's just when you look at it now,
25:55
Speaker 1: it's just so cute and whimsical. Oh yeah, it's like
25:58
Speaker 1: come on, yeah, boy, we're getting to a lot of
26:03
Speaker 1: movies today. Oh yeah, well we're stalling. We haven't even
26:06
Speaker 1: talked about butterfly Effect yet, which is coming and I'm
26:10
Speaker 1: dreading it. That's why I'm stalling. All right, So where
26:13
Speaker 1: were we? He was running his calculations, printing out his
26:17
Speaker 1: values so people could see it, and then he got
26:21
Speaker 1: a little lazy one day. In this output he noticed
26:27
Speaker 1: was interesting, so he said, you know, I'm gonna repeat
26:29
Speaker 1: this calculation see it again, but I'm gonna save time.
26:33
Speaker 1: I'm just gonna kind of pick up in the middle,
26:36
Speaker 1: and I'm not gonna input as many numbers, but I'm
26:40
Speaker 1: still using the same values, just I'm not going out
26:43
Speaker 1: to six decimal points. So the print out he had
26:46
Speaker 1: went to three decimal points. So he was working from
26:49
Speaker 1: the print out and didn't take into account that the
26:51
Speaker 1: computer accepted six decimal points, so he was just getting
26:54
Speaker 1: in three correct and expecting that the outcome would be
26:57
Speaker 1: the same, right, yes, but the outcome was way different.
27:00
Speaker 1: He went, whoa, whoa what? Yeah, he's like, what's going
27:04
Speaker 1: on here? It was a big deal. I mean, someone
27:08
Speaker 1: would have come up with this eventually, probably, yeah, but
27:10
Speaker 1: I sort of accidentally came upon it. It's neat that
27:12
Speaker 1: this guy did this because it changed his career. I
27:15
Speaker 1: think he went from emphasis on meteorology to an emphasis
27:19
Speaker 1: on chaos math to stud scientists basically. So look, I mean,
27:24
Speaker 1: the guy's got an attractor named after him, you know
27:26
Speaker 1: what I mean. Yeah, Well, let's get to that. So
27:29
Speaker 1: Lorenz starts looking at this and he's like, wait a minute,
27:32
Speaker 1: this is this is weird, this is worth investigating, and
27:36
Speaker 1: like uh, like uh, what was his name? Plankara? He said,
27:41
Speaker 1: I need fewer variables, So I'm not going to try
27:44
Speaker 1: to predict weather with these twelve differential equations that you
27:48
Speaker 1: have to take into Account'm just gonna take one aspect
27:52
Speaker 1: of weather called the rolling convection current, and I'm going
27:56
Speaker 1: to see how I can write it down in formula form.
28:00
Speaker 1: So a rolling convection current, chuck, is where you know,
28:03
Speaker 1: how the wind is created where air at the surface
28:08
Speaker 1: is heated and then starts to rise and suddenly cool
28:12
Speaker 1: air from higher above comes in to fill that that
28:15
Speaker 1: vacuum that's left, and that creates a rolling um or
28:20
Speaker 1: vertically based convection current. Okay you could. I would describe
28:25
Speaker 1: it as oven oven boiling water, a cup of coffee.
28:30
Speaker 1: Wherever there's a temperature differential based on a vertical alignment,
28:35
Speaker 1: you're going to have a rolling convection current. Okay, yeah,
28:39
Speaker 1: it sounds complex, but he just picked out one thing, basically,
28:42
Speaker 1: one condition, and this is the one he picked out.
28:44
Speaker 1: But had you seen my hands moving listeners, you would
28:48
Speaker 1: be like, oh, yeah, I know. So um He's like, okay,
28:53
Speaker 1: I can figure this out. So he comes up with
28:55
Speaker 1: three three formula that kind of describe a rolling invection current,
29:01
Speaker 1: and he starts trying to figure out how to describe
29:05
Speaker 1: this rolling convection current. Right, and so, like I said,
29:09
Speaker 1: he got this these three formula, which we're basically three
29:12
Speaker 1: variables that he calculated over time, and he plugged him
29:15
Speaker 1: in and he found three variables that changed over time.
29:19
Speaker 1: And he found that after a certain point, when you
29:22
Speaker 1: graph these things out, and since there're three, you graph
29:25
Speaker 1: them out on a three dimensional graph. So x, Y
29:28
Speaker 1: and Z. Again, he wanted to just be able to
29:30
Speaker 1: visualize this because it's easier for people to understand. He
29:33
Speaker 1: was a very visual guy. All of a sudden, it
29:35
Speaker 1: made this crazy graph that where the the line as
29:40
Speaker 1: it progressed forward through time, went all over the place.
29:43
Speaker 1: It went from this access to another access to the
29:45
Speaker 1: other axis, and it would spend some time over here,
29:48
Speaker 1: and then it would suddenly loop over to the other one,
29:51
Speaker 1: and it followed no rhyme or reason. It never retraced
29:55
Speaker 1: its path. And it was describing how a convey action
30:00
Speaker 1: current changes over time, right, and Lorenz is looking at this,
30:06
Speaker 1: he was expecting these three things to equalize and eventually
30:10
Speaker 1: form a line, because that's what determinism says, things are
30:13
Speaker 1: going to fall into a certain amount of equilibrium and
30:17
Speaker 1: just even out over time. That is not what he
30:19
Speaker 1: found now, And what he discovered was what pan quar
30:23
Speaker 1: A discovered, which was that some systems, even relatively simple systems,
30:29
Speaker 1: exhibit very complex, unpredictable behavior, which you could call chaos. Yeah.
30:35
Speaker 1: And when you say things were going all over like
30:37
Speaker 1: if you look at the graph, it it's not just
30:40
Speaker 1: lines going in straight lines bouncing all over the place randomly,
30:43
Speaker 1: like there was an order to it, but the lines
30:46
Speaker 1: were not on top of one another. Like let's say
30:49
Speaker 1: you draw a figure eight with your pencil, and then
30:52
Speaker 1: you continue drawing that figure eight, it's gonna slip outside
30:55
Speaker 1: those curves every time unless you're a robot. Um. And
31:00
Speaker 1: that's what it ended up looking like. Yeah, yeah, it
31:02
Speaker 1: never retraced the same path twice ever. Um. It had
31:07
Speaker 1: a lot of really surprising properties, and at the time
31:11
Speaker 1: it just fell completely outside the understanding of science, right. Yeah.
31:15
Speaker 1: Luckily this happened to Lawrence, who was curious enough to
31:18
Speaker 1: be like, what is going on here? And again he
31:21
Speaker 1: sat down and started to do the math and thinking
31:23
Speaker 1: about this and especially how it applied to the weather right,
31:27
Speaker 1: and he came up with something very famous. Yes, the
31:31
Speaker 1: butterfly effect. Yes, uh a, this thing kind of looked
31:36
Speaker 1: like butterfly wings a little bit, uh and be When
31:40
Speaker 1: he went to present his findings, he basically had the
31:44
Speaker 1: notion He's like, I'm gonna I'm gonna wile these people
31:47
Speaker 1: in the crowd in No, it's a conference that I'm
31:50
Speaker 1: going to and I'm gonna I'm gonna say something like,
31:53
Speaker 1: you know, the seagull flaps his wings and it starts
31:56
Speaker 1: a small turbulence that can one that can affect whether
31:59
Speaker 1: on the other side the world, the small little thing
32:02
Speaker 1: will just grow and grow in snowball and effective things.
32:05
Speaker 1: And he had a colleague goes like, seagull wings, that's nice,
32:10
Speaker 1: and he said, how about this, and this is the title.
32:12
Speaker 1: They ended up with, predictability Colin does the flap of
32:16
Speaker 1: a butterfly's wings in Brazil set off a tornado and
32:20
Speaker 1: Texas and everyone was like, WHOA mind's blown? Should we
32:27
Speaker 1: take a break? All right, We'll be right back, all right.
32:47
Speaker 1: So the lawns attractor. Uh, is that picture that he
32:53
Speaker 1: ended up with? The Laurens attractor? And this biblical pattern
33:00
Speaker 1: website that I found described attractors and strange attractors in
33:05
Speaker 1: a way that even dumb old me could understand. So
33:09
Speaker 1: if I may, he says, all right, here's the cycle
33:13
Speaker 1: of chaos. He said, Actually, I don't know who wrote this.
33:19
Speaker 1: A woman could have been a small child, could have
33:21
Speaker 1: been no of undetermined gender. I have no idea. So
33:25
Speaker 1: the gender neutral narrator, they said, he's sorry. I think
33:30
Speaker 1: about a town that has like ten thousand people living
33:33
Speaker 1: in it. To make that town work, you gotta have
33:36
Speaker 1: like a gas station, a grocery store, a library, um,
33:41
Speaker 1: whatever you need to sustain that town. So all these
33:44
Speaker 1: things are built, everyone's happy. You have equilibrium, he said.
33:48
Speaker 1: So that's great. Then let's say you build some Someone
33:52
Speaker 1: comes and build a factory on the outskirts of that town,
33:56
Speaker 1: and there's gonna be ten thousand more people living there
33:58
Speaker 1: and they don't go to church. Maybe so, uh, did
34:02
Speaker 1: I say church? They needed a church? Okay? I was
34:05
Speaker 1: just assuming this is what's called no. But you just
34:09
Speaker 1: have more people. So there's you need another gas station
34:12
Speaker 1: and another grocery store. Let's say, so they build all
34:15
Speaker 1: these things, and then you reach equilibrium. Again, it's maintained
34:20
Speaker 1: because you build all these other systems up. That equilibrium
34:24
Speaker 1: is called an attractor. Okay, so then he said it's said,
34:30
Speaker 1: they said he capital he the royal. He said, all right, now,
34:37
Speaker 1: let's say instead of that that factory being built, and
34:41
Speaker 1: you have those original tin bowls, and let's say three
34:42
Speaker 1: thousand those people just up and leave one day, and
34:46
Speaker 1: the grocery store guy says, well, there's only seven thousand
34:49
Speaker 1: people here. We need eight thousand people living here to
34:51
Speaker 1: to make a profit. So I'm shutting down this grocery store.
34:55
Speaker 1: Then all of a sudden, you have demand for groceries.
34:59
Speaker 1: So things go on a little while, and someone comes
35:00
Speaker 1: in and say, hey, this town needs a grocery store.
35:02
Speaker 1: They build a grocery store, they can't sustain, they shut down.
35:06
Speaker 1: Someone else comes along because the demand and it is
35:09
Speaker 1: this search for equilibrium, this dyna Well, you reach equal
35:15
Speaker 1: delibrium here and there as the store opens, periods of stability,
35:19
Speaker 1: periods of stability, and that dynamic equilibrium is called a
35:24
Speaker 1: strange attractor. So, an attractor is the state which a
35:28
Speaker 1: system settles on. Stranger attractor is the trajectory on which
35:33
Speaker 1: it never settles down but tries to reach the equilibrium
35:37
Speaker 1: with periods of stability. Does that make sense that Bible
35:41
Speaker 1: based explanation was dynamite. I understand it better than I
35:45
Speaker 1: did before, and I understood it okay before. That's great.
35:50
Speaker 1: Surely can add yeah, yeah, now you're gonna add to it. No,
35:55
Speaker 1: that's it, No, I mean like it. Yeah. An attractor
35:59
Speaker 1: is where if you raft something and eventually it reaches equilibrium,
36:03
Speaker 1: it's a regular attractor. If it never reaches equilibrium, is
36:06
Speaker 1: constantly trying to and has periods of stability. Strange attractor.
36:09
Speaker 1: I can't. I can't top that, alright, grocery store, small town.
36:13
Speaker 1: That was great. So um Lorenz, a strange attractor was
36:16
Speaker 1: named a Lorenz attractor named after him. Big deal. They
36:20
Speaker 1: weren't using the word chaos yet. No. But he published
36:24
Speaker 1: that paper about butterfly wings, right, the butterfly effect, and
36:28
Speaker 1: it coupled with his pictures the picture of a strange attractor,
36:32
Speaker 1: which is almost the aside from fractals, almost the the
36:38
Speaker 1: the um emblem or the logo for chaos theory. The
36:42
Speaker 1: Laurens attractor is um. It got attention off the bat.
36:47
Speaker 1: It wasn't like plan cares findings where he got neglected
36:50
Speaker 1: for seventy years. Almost immediately everybody was talking about this
36:53
Speaker 1: because again, what Lorenz had uncovered, which is the same
36:56
Speaker 1: thing that plan Care had uncovered, is that determinism is
37:00
Speaker 1: possibly based on an illusion that the universe isn't stable,
37:05
Speaker 1: that the universe isn't predictable, and that what we are
37:07
Speaker 1: seeing as stable and predictable are these little periods windows
37:12
Speaker 1: of stability that are found in strange attractor graphs, that
37:16
Speaker 1: that's what we think the order of the universe is,
37:18
Speaker 1: but that that is actually the abnormal aspect of the universe,
37:23
Speaker 1: and that instability unpredictability, as far as we're concerned, is
37:28
Speaker 1: the actual state of affairs in in nature. And I
37:32
Speaker 1: think as far as we're concerned, is a really important
37:34
Speaker 1: point to Chuck, because it doesn't mean that nature is
37:39
Speaker 1: unstable chaotic. It means that our picture of what we
37:45
Speaker 1: understand as order doesn't jibe with how the universe actually functions.
37:51
Speaker 1: It's just our understanding of it, and we're's just so
37:54
Speaker 1: um anthropocentric that you know, we we see it as
37:59
Speaker 1: chaos and disorder and something to be feared, when really
38:01
Speaker 1: it's just complexity that we don't have the capability of
38:05
Speaker 1: predicting after a certain degree. Yeah, I think that makes
38:10
Speaker 1: me feel a little better, because when you read stuff
38:12
Speaker 1: like this, you start to feel like, well, the Earth
38:14
Speaker 1: could just throw us all off of its face at
38:17
Speaker 1: any moment because it starts spinning so fast that gravity
38:21
Speaker 1: becomes undone. And I know that's not right. By the way,
38:23
Speaker 1: I've always loved that kind of science that shows we
38:26
Speaker 1: don't know anything. Like Robert Hume, who I know, I
38:30
Speaker 1: understand was a philosopher, but he was a philosopher scientist. Um.
38:33
Speaker 1: His whole jam was like causing effect as an illusion
38:37
Speaker 1: that like we all we it's it's just an assumption,
38:40
Speaker 1: like that if you drop a pencil, it will always
38:42
Speaker 1: fall down, and it's an illusion. And this is pretty
38:45
Speaker 1: um gravity understanding gravity. But he makes a good pet
38:49
Speaker 1: gravity when everyone's just floating around. Yeah, going this pencils
38:52
Speaker 1: got me wacky. But but the point was that you know,
38:55
Speaker 1: we we are. We base a lot of our assumptions
39:00
Speaker 1: um or a lot of stuff that we take as
39:02
Speaker 1: law are actually based on assumptions that are made from
39:06
Speaker 1: observations over time, and that we're just making predictions that
39:09
Speaker 1: causing effect as an illusion. I love that guy, and
39:12
Speaker 1: this this definitely supports that idea for sure. Yeah. Sorry,
39:18
Speaker 1: I'm I'm excited about chaos theory. Believe it. Well, I
39:21
Speaker 1: mean I like that I'm able to understand it and
39:24
Speaker 1: enough of a rudimentary way that I can talk about
39:27
Speaker 1: it at a dinner party. Well, thank your Bible website. Well,
39:31
Speaker 1: once you take the formulas out for people like us,
39:36
Speaker 1: we're like, okay, we can understand chaos. Then when somebody says, good,
39:39
Speaker 1: do a differential equation, just like, what a different equation? Right?
39:45
Speaker 1: All right? So earlier I said that chaos had not
39:47
Speaker 1: been used the word chaos to describe all this junk, uh,
39:52
Speaker 1: And that didn't happen until later on and well actually
39:56
Speaker 1: about ten years, you know, but it was kind of
39:57
Speaker 1: at the same time this other stuff was going on
39:59
Speaker 1: with rinds. Yeah, late sixties, early seventies. There was a
40:03
Speaker 1: guy named Steven Smile Uh fields metal recipients. So you know,
40:08
Speaker 1: he's good at math, and um, he describes something that
40:14
Speaker 1: we now know as the small horseshoe, and it goes
40:18
Speaker 1: a little something like this. Uh So, all right, take
40:23
Speaker 1: a piece of dough with like bread dough, and you
40:27
Speaker 1: smash it out into a big flat rectangle. So you're
40:30
Speaker 1: looking at that thing and you're like, boy, I hope
40:33
Speaker 1: this makes some good bread. This is gonna be so good.
40:36
Speaker 1: So then you do a little rosemary on it. Yeah
40:38
Speaker 1: maybe so yeah, and then um lick it before you
40:42
Speaker 1: bake it, so you know it's yours. No one else
40:45
Speaker 1: can happen. Uh So, you you have that flat rectangle
40:48
Speaker 1: of dough, you roll it up into a tube, and
40:53
Speaker 1: then you smash that down kind of flat, and then
40:55
Speaker 1: you bend that down to where it eventually looks like
40:57
Speaker 1: a horse shoe. So now how you take that horseshoe.
41:01
Speaker 1: You take another rectangle of dough and you throw that
41:04
Speaker 1: horseshoe onto that, and then you do the same thing.
41:07
Speaker 1: The smell horseshoe basically says you cannot predict where the
41:12
Speaker 1: two points of that horseshoe will end up. You can
41:16
Speaker 1: roll it a million times and they'll end up in
41:19
Speaker 1: a million different places, totally random, different places to totally random.
41:24
Speaker 1: You never know. It's like a box of chocolates. You
41:27
Speaker 1: never know what you're gonna get. You have to say it,
41:29
Speaker 1: and that became known. You have to say it. Oh
41:32
Speaker 1: what imitate Forrest Gumps? Now I can't do that. That's fine.
41:35
Speaker 1: He's not one. He's not in my repertoire. That's fine.
41:38
Speaker 1: Although I did see that again part of it recently.
41:41
Speaker 1: Does it hold up well? I mean, take out forty
41:43
Speaker 1: minutes of it and it would have been a better movie,
41:47
Speaker 1: like all of that coincidence stuff that that and he
41:52
Speaker 1: also did the smile t shirt like it was just
41:54
Speaker 1: too much, Like he really hammered it too much was
42:00
Speaker 1: the basis of the movie. I know. But see it
42:02
Speaker 1: again and I guarantee you, like an hour and a
42:04
Speaker 1: half into it, you'll be like, I get it. You know.
42:08
Speaker 1: It was a good Tom Hanks movie that was overlooked.
42:12
Speaker 1: Road to Perdition, Yeah, that was a good one. Great
42:16
Speaker 1: Sam Indees. Oh man, that guy is awesome. Yeah, Oh
42:19
Speaker 1: what is he gonna do? He might do something he
42:22
Speaker 1: did the James bo he did Skyfall. Yeah, yeah, I
42:24
Speaker 1: know he's gonna also that last one that wasn't so great.
42:27
Speaker 1: He's got a potential project coming up and he would
42:30
Speaker 1: be amazing for and I don't remember what it was.
42:32
Speaker 1: Did you see Revolutionary Road? Yes? God have it was
42:36
Speaker 1: just like, yeah, you want to jump off a bridge
42:40
Speaker 1: like every five minutes during that movie. That was hardcore.
42:45
Speaker 1: H he did that one too. Huh yeah, And don't
42:46
Speaker 1: see that if you're like engaged to be married or
42:49
Speaker 1: thinking about it, yeah, or if you're blue already. I'm yeah,
42:53
Speaker 1: just take a really good good mood and be like
42:55
Speaker 1: I'm sick of being in a good mood, sit down
42:57
Speaker 1: and watch Revolutionary Road. Watch Joe Versus of Volcano instead. Uh?
43:03
Speaker 1: Where was I smell? Horseshoe? Is what that's called? And? Um?
43:09
Speaker 1: That was he was the first person to actually use
43:11
Speaker 1: the word chaos. Oh he was? I think so? No? No, No,
43:15
Speaker 1: York was Tom York's dad. Yeah, you're right, he wasn't
43:18
Speaker 1: the first person York correct. But it's male's horseshoe. Illustrates
43:21
Speaker 1: a really good point, Chuck, is it Tom York's dad? No?
43:25
Speaker 1: But they're both British, sure, York. Actually one's Australian. No,
43:31
Speaker 1: they're British. Um. So those two points, which should which
43:36
Speaker 1: started out right by each other and then end up
43:38
Speaker 1: in two totally different places. That applies not just a
43:41
Speaker 1: bread dough, but also too, things like water molecules that
43:45
Speaker 1: are right next to each other at some point and
43:48
Speaker 1: then months later they're in two different oceans, even though
43:52
Speaker 1: you would assume that they would go through all the
43:54
Speaker 1: same motions and everything, but they're not. There's so many
43:57
Speaker 1: different variables with things like ocean currents that two water
44:01
Speaker 1: molecules that were once side by side end up in
44:04
Speaker 1: totally random different places. And that's part of chaos. It's
44:08
Speaker 1: basically chaos personified or chaos molecule fied. So we mentioned York.
44:16
Speaker 1: Where I was going with that was, Um, there was
44:19
Speaker 1: an Australian named Robert May and he was a population biologist.
44:24
Speaker 1: So he was using math to model how animal populations
44:28
Speaker 1: would change over time, giving certain starting conditions. Uh. So
44:33
Speaker 1: he started using uh these equations is differential equations, and
44:38
Speaker 1: he came up with a formula known as the logistic
44:40
Speaker 1: difference equation that basically enabled him to predict these animal
44:44
Speaker 1: populations pretty well. Yeah, and it was working pretty well
44:49
Speaker 1: for a while, but he noticed something really really weird, right, Yeah,
44:53
Speaker 1: he had this formula. Um, the logistic difference equation is
44:57
Speaker 1: the name of it. Sure, Okay, So we had of
45:00
Speaker 1: that formula, and he figured out that if you took
45:04
Speaker 1: our which in this case was the reproductive rate of
45:07
Speaker 1: a animal population, and you pushed it past three, the
45:12
Speaker 1: number three, so that meant that the average animal in
45:16
Speaker 1: this population of animals had three offspring in its lifetime
45:22
Speaker 1: or in a season, whatever. If you pushed the past three,
45:25
Speaker 1: all of a sudden, the number of the population would diverge.
45:31
Speaker 1: If you pushed it equal to three, actually or more,
45:34
Speaker 1: it would diverge, which is weird because a population of
45:38
Speaker 1: animals can't be two different numbers, you know, like that
45:42
Speaker 1: herd of antelope is not there's not thirty, but there's
45:45
Speaker 1: also forty five of them at the same time. That's
45:48
Speaker 1: called the superposition, and that has to do with quantum states,
45:51
Speaker 1: not herds of antelopes. That was kind of weird. And
45:57
Speaker 1: then he found if you pushed it a little further,
45:58
Speaker 1: if you made the productive rate like three point oh
46:02
Speaker 1: five seven or something like that. I think it was
46:05
Speaker 1: a different number, but you just tweaked it a little bit,
46:08
Speaker 1: not even to four. We're talking like millions of a
46:11
Speaker 1: of a of a degree um, all of a sudden
46:15
Speaker 1: it would turn into four so there'd be four different
46:18
Speaker 1: numbers for that was the animal population, and then would
46:21
Speaker 1: turn into sixteen, and then all of a sudden, after
46:23
Speaker 1: a certain point, it would turn into chaos. The number
46:25
Speaker 1: would be everything at once, all over the place, just
46:28
Speaker 1: totally random numbers that it oscillated between. But in all
46:33
Speaker 1: that chaos, there would be periods of stability. Right, you
46:36
Speaker 1: push it a little further, and all of a sudden
46:37
Speaker 1: it would just go to two again. But beyond that
46:40
Speaker 1: it didn't go back to the original two numbers. It
46:43
Speaker 1: went to another two. So if you looked at it
46:45
Speaker 1: on a graph, it went line divided into two, divided
46:48
Speaker 1: into four eight sixteen chaos, two four sixteen sixteen chaos,
46:55
Speaker 1: all before you even got to the number four of
46:58
Speaker 1: the reproductive right. And he was working with Mr York
47:02
Speaker 1: because he was a little confounded, so he was a
47:04
Speaker 1: mathematician buddy of his, James Yorke from the University of Maryland,
47:08
Speaker 1: so they worked together on this. In the nine they
47:11
Speaker 1: co authored a paper called Period three implies Chaos and man,
47:17
Speaker 1: finally somebody said the word. I kept thinking it was
47:21
Speaker 1: all these other people. Yeah, and the this this paper
47:24
Speaker 1: where they first debut the name chaos. Um. They they
47:29
Speaker 1: based it. UM. Tom York's Dead based it on Edward
47:34
Speaker 1: Lawrences paper. He was like, you know what, I have
47:37
Speaker 1: a feeling that has something to do with the Lawrens attractor.
47:40
Speaker 1: So that, um, that that provided chaos to the world.
47:44
Speaker 1: And it it was the basically the third the third
47:48
Speaker 1: time a scientist had said we don't understand the universe
47:53
Speaker 1: like we think we do, and determinism is based on
47:57
Speaker 1: an illusion of order, a really chaotic universe. And this, uh,
48:04
Speaker 1: this established chaos. It took off like a rocket. And
48:06
Speaker 1: the eighties and the nineties, you know, as you know
48:08
Speaker 1: from Jurassic Park, chaos was everything. Everybody's like, chaos, this
48:12
Speaker 1: is totally awesome. It's the new frontier science. And then
48:15
Speaker 1: it just went It just went away, And a lot
48:18
Speaker 1: of people said, well, it was a little overhyped, but
48:21
Speaker 1: I think more than anything, and I think this is
48:24
Speaker 1: kind of the current understanding of chaos because it didn't
48:26
Speaker 1: actually go away. It became a deeper and deeper field.
48:29
Speaker 1: As you'll see, Um, people mistook what chaos meant. It
48:35
Speaker 1: wasn't the a new the new type of science. It
48:39
Speaker 1: was a new understanding of the universe. It was saying like, yes,
48:42
Speaker 1: you can still use new Tony in physics, like don't
48:45
Speaker 1: throw everything out the window. You can still try and
48:47
Speaker 1: predict weather and still try and build more accurate instruments
48:50
Speaker 1: and get you know, decent results. But you can't with
48:54
Speaker 1: absolute perfection. Complex systems like determinism. The the ultimate goal
49:01
Speaker 1: of determinism is false. It can never be it can
49:05
Speaker 1: never be done because we can't have an infinitely precise
49:07
Speaker 1: measurement for every variable or any variable. Therefore, we can't
49:11
Speaker 1: predict these outcomes. Right, So you would expect science to
49:14
Speaker 1: be like, what's the point, what's the point of anything? Well,
49:18
Speaker 1: some some chaos people have said, no, this is this
49:22
Speaker 1: is great, this is good. We'll take this. Will take
49:26
Speaker 1: the universe as it is, rather than trying to force
49:29
Speaker 1: it into our pretty little equations and saying like if
49:33
Speaker 1: the ocean temperature is this at this time of year, uh,
49:36
Speaker 1: and the fish population is this at that time, then
49:39
Speaker 1: this is how many offspring this fish stole. This fish
49:42
Speaker 1: population is going to have. Um, say, okay, here is
49:47
Speaker 1: the fish population, here is the ocean temperature, here all
49:51
Speaker 1: these other variables. Let's feed it into a model and
49:54
Speaker 1: see what happens. Not this is going to happen. What
49:58
Speaker 1: happens instead? And this is kind of the understanding of
50:01
Speaker 1: chaos theory. Now. It's taking raw data, as much data
50:06
Speaker 1: as you can possibly get your hands on, as precise
50:08
Speaker 1: data as you could possibly get your hands on, and
50:10
Speaker 1: just feeding it into a model and seeing what patterns emerge.
50:14
Speaker 1: Rather than making assumptions, it's saying, what's the outcome? What
50:18
Speaker 1: comes out of this model? Yeah, And that's why, like
50:21
Speaker 1: when you see some things, like you know, fifty years
50:23
Speaker 1: ago they predicted this animal be its extinct and it's not. Well,
50:28
Speaker 1: it's because the variations were too complex they tried to predict. Uh.
50:33
Speaker 1: And that's why if you look at a ten day forecast, you, sir,
50:39
Speaker 1: are a fool. All right, Well, ten days from now
50:43
Speaker 1: says it's going to rain in the afternoon. Come on.
50:46
Speaker 1: But if you take if you took enough variables for
50:49
Speaker 1: weather for like a city, and fed it into a
50:52
Speaker 1: model of the weather for that city, you could find, uh,
50:57
Speaker 1: you could find a time when it was similar to
51:01
Speaker 1: what it is now, and you could conceivably make some
51:04
Speaker 1: assumptions based on that. You can say, well, actually we
51:08
Speaker 1: can we can predict a little further out than we think.
51:11
Speaker 1: But um, it's it's based on this, this theory, this
51:14
Speaker 1: understanding of chaos, of unpredictability, of not just not forcing
51:20
Speaker 1: nature into our formulas, but putting data into a model
51:24
Speaker 1: and seeing what comes out of it. Yeah, and then
51:26
Speaker 1: at the end of that you learn like when that
51:28
Speaker 1: animal is not extinct like you thought it would be,
51:31
Speaker 1: you go back and look at the original thing and
51:33
Speaker 1: you have a more accurate picture of how the you know,
51:36
Speaker 1: data could have been off slightly this one value, and
51:41
Speaker 1: then you have more buffalo than you think. Yeah, sure
51:45
Speaker 1: you got buffaloed by chaos. And we're not even getting
51:48
Speaker 1: into fractals. It's a whole other thing. And we did
51:51
Speaker 1: a whole other podcast in June about fractals and Mandel Bena,
51:57
Speaker 1: mandel Brett, mendel Brett, mandel Brett. Yeah, and go listen
52:01
Speaker 1: to that one and hear me clinging to the edge
52:04
Speaker 1: of a clift. Man. We we should end this, but first, um,
52:09
Speaker 1: I want to say, there is a really interesting article
52:12
Speaker 1: it's pretty understandable on Quanta magazine about a guy named
52:17
Speaker 1: George and he is a chaos theory dude who's got
52:25
Speaker 1: a whole lab and is applying it to real life.
52:28
Speaker 1: So it's a really good picture of chaos theory and action.
52:34
Speaker 1: Go check it out. Okay, Uh, if you want to
52:37
Speaker 1: know more about chaos theory, I hope your brain is
52:41
Speaker 1: not broken. Yeah, go take some LSD and look attical that. Um,
52:46
Speaker 1: you can type those words into how stuff works in
52:49
Speaker 1: the search bar any of those fractals LSD chaos. It'll
52:53
Speaker 1: bring up some good stuff. And since I said good stuff,
52:56
Speaker 1: it's time for a listener. Now, I'm gonna call this
53:00
Speaker 1: rare shout out get requests all the time. I bet
53:04
Speaker 1: I know which one is really dude his girlfriend? Yeah no,
53:10
Speaker 1: so far, so good. Hey, guys, just want to say
53:13
Speaker 1: I think you're doing a wonderful job with the show.
53:15
Speaker 1: To this date. My first time listening was during my
53:17
Speaker 1: first deployment. Uh yeah, when I listened to your list
53:22
Speaker 1: on famous and influential films and I was hooked after that.
53:26
Speaker 1: Since I came back State Side has spent many hours
53:28
Speaker 1: driving to and fro uh see my girlfriend, to my barracks,
53:33
Speaker 1: and I can happily say that they've been made all
53:35
Speaker 1: the more enjoyable by listening to you guys. Even my
53:39
Speaker 1: girlfriend Rachel has warmed up to you dudes, which was
53:42
Speaker 1: not a pleasant I'm sorry, which was a pleasant shock
53:44
Speaker 1: to me that she has told me repeatedly that she
53:47
Speaker 1: cannot listen to audiobooks because quote hearing people talk on
53:51
Speaker 1: the radio gives me a headache end quote. Anyway, I
53:54
Speaker 1: hope you guys continue to make awesome podcasts as I'm
53:57
Speaker 1: headed out on my next deployment. And if you could
53:59
Speaker 1: give a show it out to Rachel, I'm sure it
54:00
Speaker 1: would make her feel a little better that I got
54:03
Speaker 1: the pleasant people on the podcast to reaffirm how much
54:06
Speaker 1: I love her. That is John Rachel hanging there, John,
54:11
Speaker 1: be safe and thanks for listening. Yeah, man, thank you.
54:15
Speaker 1: That is a greed email. I love that one. Glad
54:17
Speaker 1: we don't give you a headache. Rachel. Yeah. For she
54:19
Speaker 1: listened to this son, and she's like, okay, oh yeah,
54:21
Speaker 1: everybody's gonna get a headache from this one. Like I
54:24
Speaker 1: came to hate the sound of my own voice from
54:26
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54:30
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54:42
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54:55
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