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SYSK Selects: How Chaos Theory Changed the Universe · Stuff You Should Know

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