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