July 19, 2016 • 55 mins
Since the age of Descartes, science has put all of its eggs in the basket of determinism, the idea that with accurate enough measurements any aspect of the universe could be predicted. But the universe, it turns out, is not so tidy.
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Speaker 1 (00:00):
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website apart. Welcome to Stuff you Should Know from House
Stuff Works dot com. Hey, and welcome to the podcast.
(00:27):
I'm Josh Clark with Charles W. Chuck Bryant, and there's
Jerry over there. So this is Stuff you Should Know,
the podcast about chaos theory. Like, uh, have you ever
seen Event Horizon? I did not bad great movie? Are
you crazy? I don't think it was great? Oh so
imagine it it. I thought it was okay. It was
(00:50):
like a love crafty and thing in our space. Loved
it all right, I love crafted it. I liked it. Um.
That's what I think of when I think of k US.
You know, there's that one part where they kind of
give you like a glimpse behind, like the dimension that
this action is taking place in, to see the chaos underneath,
and you should check that out again. I think about
(01:14):
Jurassic Park and Jeff Goldblum as as the creep. Dr
Malcolm explaining chaos in the little auto driving suv or
whatever that was. Yeah, that's what it was called in
the script, the auto driving suv scene. Yeah, and you
know what, he actually rewatched that scene and it confirmed
(01:36):
two things. One is that he uh, he actually did
a pretty decent job for a Hollywood movie with a
very rudimentary explanation of chaos. Um, and you watched it
for this Yeah, yeah, just that scene. And then it
also confirmed of what a creep that character was. Yeah,
if you watched that scene, he's like, you know, he's
all gross and flirty with her right in front of
(01:58):
her ex but there's just you know, he's talking to her.
I didn't even notice this at first. He like he
just like touches her hair out of nowhere for no reason.
He's just talking to her and he just like grabs
her hair and touches it. And I'm like, what a creep.
I know, if you look closely, you can see the
hormones emerging through his chest hair. Yeah, and I love
Jeff Goldblum. It's not a reflection on him. He was
(02:21):
basically doing Jeff Goldblum. Well that's what Yeah, sure he's
Jeff Goldblum, but I don't think that's how in the
manner in which he speaks. But I don't think he's
a creep, do you. Wow, I've got nothing against Jeff
gold Okay, I think he's a I think he's doing
Jeff Goldblum. It was also a sign of the times,
like if that movie were made today, doctor what was
(02:44):
her name in the movie? Yeah, Dr Sutler would be like,
it's very inappropriate to stroke my hair, like, don't touch me.
But this was the nineties. Was eight No, it was nineties.
It was the early mid ninety. Other thing, the book
came out and in the book, uh Ian Malcolm, who's
(03:07):
a Kayetician creep Kaetician? Right he um he he goes
into even more depth about chaos. But that was I mean,
that was the first time I ever heard of chaos
theory was from Jurassic Parks, and um it really it
was really misleading. I think the entire term chaos is
(03:28):
very misleading as far as the general public goes as
from what I researched in this this for this article. Well, yeah,
I mean you hear the word chaos as an English
speaker and you think frenetic and crazy, out of control. Yeah,
and that's not what it means in terms of science
like this, right. What it means, I guess we can
(03:48):
say up front is is basically the idea that complex
systems do not behave in very neat ways that we
can easily grasp, understand, ander measure, right, and not even
even simple systems don't. Sometimes it doesn't always have to
be complex. But um, I want to give a shout
(04:09):
out in addition to our own article to UH when
you know, when it comes to stuff like this, the
brain breaking stuff. For me, this is a breaker. You
know how I always go to like blank blank for
kids because it always helps. If there's a dinosaur mascot
on the page, it's a sure thing we can understand it.
But the best explanation for all this stuff that I
(04:31):
found on the internet was from a website called a
bar Um A B A R I. M. Publications, which
turns out to be a website about biblical patterns and
sandwiched in the middle, there is a really great, easy
to understand UH series of pages on chaos there. So
(04:51):
I was like, man, I get it now in a
rudimentary way, right, Well, yeah, um, I think even a
lot of people who deal with systems that display chaotic behavior,
which I guess is to say basically all systems eventually
under the right conditions, Um, don't necessarily understand chaos. Yeah,
(05:12):
And they defined a complex system is specifically. It doesn't
mean just like, oh it's complex, I mean it is,
but specifically, Um, they define it in a way that
helped me understand it's a system that has so much motion,
so many elements that are in motion, moving parts. Yeah,
that it takes like a computer to calculate all the
(05:33):
possibilities of like what that could look like five minutes
from now, ten years from now. So before computers came around,
we before the quantum mechanical revolution, it was there's a
lot more basic. It was like what comes up must
come down, stuff like that. Let's talk about that, Chuckers,
because when you're talking about chaos theory, it helps to
(05:54):
understand how it revolutionized the universe by getting a clear
picture of how we understood the universe leading up to
the discovery of chaos. Right, So, prior to the um
the scientific revolution, everybody was like, oh, well, it's it's God.
The Earth is at the center of the universe and
(06:16):
God is spinning everything around like a top right, it
was all a theistic explanation. Then the scientific revolution happens
and people start applying things like math and making like
mathematical discoveries and and and figuring out that there are
there's order. They're finding order in patterns and predictability to
(06:37):
the universe if you can apply mathematics to it, specifically,
if you can apply mathematics to the starting point, right, right,
So if you can, if you can um figure out
how a system works mathematically speaking, right, you can go
in and plug in whatever coordinates you want to and
(06:57):
watch it go. You can predict what what the outcomes
can be and what this is the it's based on
what at the time was a totally revolutionary idea um
By Initially, I think the cart was the first one
to kind of say, causing effect is a pretty big
part of our universe, right. Yeah. It was sort of
like where this is sixteen hundreds, where early science met philosophy.
(07:22):
They kind of complimented one another as far as something
that's we're talking about determinism, right, So that was the
kind of the seeds of determinism. Was the scientific revolution,
And like you said, where philosophy and science came together
in the form of Descartes, right, and then Newton came
along and we did a whole episode on him. Yeah,
January of this year. That was a good one. It
(07:43):
was really good. Like I think you said in that
episode that there's possibly no scienists that changed the world
more than Newton has. He's he's got legs. People shouted
out others and email, but I'll just say he's at
the near the top for sure with some other people.
The Cream. Yeah, so Newton came along and Newton said
that was his name, Isaac the Cream new anytime he
(08:06):
don't to be like cream. Yeah, you just got creamed.
I thought he was a boxer. He's a basketball player.
He was much more well known as a boxer, but
he definitely could dunk as a as a B baller. So,
um man, that threw me off a little bit. Yeah,
the Cream comes along and uh, he basically says, watch this, dude,
(08:29):
this causing effect thing you're talking about, I can express
it in quantifiable terms. And he comes up with all
of these great laws and and basically sets the stage
the foundation for science for the next three centuries or so. Yeah,
these these laws that were so rock solid and powerful
that scientists kind of got ahead of themselves a little
(08:52):
and said we're done. Like with Newton's laws, we can predict, uh,
we can predict everything if we have a good enough
beginning accurate value to plug into his equations, and they weren't.
I think there was a little hubrius and a little
just excitement about like, well we figured it all out
right that that you could take Newton's laws and if
(09:14):
you had accurate enough measurements, uh, you could predict what
the outcome would be of that system that you plug
those measurements into using these formula. And at the time,
a lot of this was like planetary, like, well, we
know that these planets are here and they're moving and
their orbiting, So if we know these things, we can
plug it into an equation and we can figure out
(09:36):
what it's going to be like in a hundred years exactly.
And they figured out the basis of determinism is what
we just said, that if you have accurate measurements, you
can take those measurements and use them to predict um
how a system is going to change over time using
differential equations. Right, yeah, so this is what this is
(09:56):
what Newton comes along and figures out that you can
describe the uni us in these mathematical terms using differential
equations and um like, you said there was a tremendous
amount of hubris, and well, I think you said there's
some hubris. I think there's a tremendous amount of hubris
where science basically said, we've mastered the universe, We've uncovered
(10:17):
the blueprint of the universe, and now we understand everything.
It's just a matter now of getting our scientific measurements
more and more and more exact. Because again, the hallmark
of determinism is that if you have exact measurements, you
can predict an outcome accurately, like the pool queue example
or the pool table example. Right right, So if you've
(10:39):
got a pool table, let's say you're playing some nine ball.
You have that beautiful little diamond set up, you got
your cue ball, you put that cue ball, and you
you crack it with the queue, and if you are
super accurate with your initial measurements, you should be able
to mathematically plot out via angles where the balls will
end up, right exactly, like you can say, this is
(11:01):
what the table will look like after the break. If
you know the force the angle, all those little variable temperature,
if there's wind in the room, like the felt on
the table, like everything. The more specific you are, the
more accurate your end result will be. Right. And then
one of the other hallmarks of determinism is that if
you take those exact same initial conditions and do them again,
(11:23):
the table, the pool table will look exactly the same
after the break, which is pretty much impossible for like
a human to do with their hands. Sure, but the
idea at the time of science is that if you
could build a perfect machine, sure that could recreate these conditions,
it will happen the same way every time. Right, And this,
I mean this led to they had hubris, but you
(11:45):
could understand it when like literally in two people predicted
Neptune would exist within months, that would exist, but does exist.
And this is not by looking up in the sky
like they did it with math and they were right.
So imagine in eighty when that happens, they're like, yeah,
(12:07):
we kinda we've got the math down, so we're pretty
much all knowing well. Plus also, for the most part,
these not just with Neptune, they were finding um that
this stuff really panned out. It held true for everything
from um you know, the investigation into electricity to new
chemical reactions and understanding those and it it laid the
(12:30):
scientific revolution, laid the basis for the industrial revolution, and
just the change that came out of the world like that.
It definitely there. It is understandable how science kind of
was like we got it all figured out well, and
like you said, they even Galileo was smart enough to
know there's uncertainty in these measurements, like the precision is key.
(12:56):
So they spent what does the article say, a lot
of the much of an enth and twenty century just
trying to build better instrumentation to get more and more
smaller and smaller and more precise measurements. Right, That was
like basically the goal of it, right, Yeah, which was
the right direction. That's like exactly what they should have
been doing. The problem is there, Like you said, Galileo
(13:18):
knew that there was some sort of there there gonna
be some flaws and measurement that we just didn't have
those great scientific instruments yet. It's called the uncertainty principle.
It's accuracy, right, But the idea is that if you
have a good enough instruments, you can overcome that, and
(13:39):
that the the more you shrink the um error in
measuring the initial conditions, the more you're gonna shrink the
error in the outcome. It would be proportionate. Right. They
were correct. The thing is they were also aware but
ignoring in a lot a lot of ways some outstanding problems,
(14:03):
specifically something called the end body problem. You know what,
I'm so excited about this. I need to take a break.
I think that's a good idea. I need to go
check out my end body in the bathroom. Okay, and
we'll be back. All right, check, we're back. So there's
(14:37):
some there's some issues right with determinism. There's some some
weird problems out there that are saying like, hey, pay
attention to me because I'm not sure determinism works. Uh.
And one of the one is the end body problem. Yeah.
How this came about was that was King Oscar number
(14:58):
two of Sweet and Norway. Yeah, I don't want to
leave out Norway both. Uh. He said, you know what,
let's offer a prize to anyone who can prove the
stability of the solar system something that has been stable
for a long time before that and a lot of
the most brilliant minds on planet Earth got together and
(15:19):
tried to do this, uh with mathematical proofs, and no
one could do it. Uh. And then a dude name Honoree.
You gotta help me there with that, Oh, say, the
whole thing very nice. He was French, believe it or not,
and he was a mathematician, and he said, you know what,
(15:41):
I'm not gonna look at this big picture of all
the planets in the sun and all their orbits. You'd
have to be a fool to try that. Sure, he said,
I'm gonna shrink this down, Like we talked about shrinking
that initial value, you know, and um, that initial condition.
He shrunk it down. He said, I'm gonna look at
just a couple of bodies orbiting one another, uh, with
(16:03):
a common center of gravity. And I'm gonna look at this.
And this was called the N body problem. Yeah, which
was smart to do, because the more variables you factor
into um a nonlinear equation like that, just the harder
it's gonna be. So he shrunk it down. So the
N body problem has to do with three or more
(16:24):
celestial bodies orbiting one another. So Plank said, I'll just
start with three. Smart and what he found from doing
his equations for this this King Oscar. The sequel prize
um was that shrinking the initial conditions um measurement or
rate of error right, did not really shrink the the
(16:49):
error in the outcome, which flies in the face of determinism.
What he found was that just very very minute different
is in the initial conditions fed into a system produced
wildly different outcomes after a fairly short time. Yeah, like,
let me just round off the mass of this planet
(17:11):
at like the eighth decimal point, and you know who cares?
Who cares at that point? Let me just round that
one to a two, and that would throw everything off
at a at a pretty high rate. And he said,
wait a minute, I think this contest is in polsib right,
He said, there is no way to prove prove, to
(17:34):
prove the stability of the Solar system, because he just
uncovered the idea that it's impossible for us to predict
the the the rate of change among celestial bodies. Yeah,
it's such a complex system. There are far too many
variables that, uh, it's impossible to start with something so
(17:58):
minute to get the equation whatever the sum that you want. Well,
not only that, but the result, not only that and
this is what really undermined determinism was that he figured
out that you would have to have an infinitely precise measurement,
which even if you build a perfect machine that could
(18:20):
take the infinitely or a perfect machine that could take
a measurement of like the the movement of a celestial
body around another, you, it's literally impossible to get infinite
and infinitely precise measurement, which means that we could never
predict out to a certain degree the movement of the
(18:41):
celestial bodies. Like he was saying, like, no, you you
can't get You can't build a machine that that gets
measurements enough that we can overcome this, like determinism is wrong,
Like you can't just say, uh, we have the understanding
to predict everything. There's a lot of stuff out there
(19:02):
that we're not able to predict. And he uncovered it
trying to figure out this end body problem. Yeah, and
King Oscar the sequel said you win, Yeah, bring me
another rack of lamb and uh, here's your prize. And
he won by proving that it was impossible, which is
pretty interesting. And they utterly and completely changed not just math,
but like our our our understanding of the universe, and
(19:24):
our understanding of our understanding of the universe, which is
even more kind of earth shaking. Yeah, he discovered dynamical
instability or chaos, and um, they didn't have supercomputers at
the time, so it would be a little while, about
seventy years at m I T until uh we could
actually kind of feed these things into machines capable of
(19:47):
plotting these things out in a way that we could see,
which was really incredible. So there is this dude um
seven years later, uh named um Edward Lawrence Lawrence. Yeah. Well,
first of all, we should set the stage the reason
this guy he was a meteorologist and scientists, right, not
(20:08):
that those are not the same thing, right, He's a
scientist who dabbled the meteorology. Here was a mathematician, Yeah,
but he was really into meteorology because it was there
was a weird juxtaposition at the time where we were
sending people into outer space but we couldn't predict the weather. Yeah,
and it was it was definitely a blot on the
(20:28):
field of meteorology. People were like, do you guys know
what you're doing? And and meteorologists are like, you have
no idea how hard this is? Like yeah, we can
predict it a couple of days out, but after that,
it's just it's totally unpredictable. It drives as mad and
it's not. It wasn't just there. Um, their reputations that
were at stake, like people were losing their lives because
(20:50):
of it, right, Yeah. N two there were two notorious storms,
one on the East coast and one on the west. Uh,
the ash Wednesday storm in the East and the big
blow on the West that of a lot of people,
cost hundreds of millions of dollars in damage. And people
were like, you know, we need to be able to
see these things coming a little more because it's a problem.
And meteorologists were like, why did you do it then?
(21:13):
So they thought the key was these big supercomputers. Remember
the supercomputers. When they came out the big rooms full
of hardware, it was amazing, and they were finally able
to do like these incredible calculations that we could never
do before. I know, they were able to like crunch
sixty four bites a second. Yeah, we had the advocates
and then the supercomputer. There was nothing in between. Um.
(21:36):
I looked up the computer that Lawrence was working with
the Whopper Royal McBee. What was the Whopper board games?
Was it? It called the Whopper w a PR I
can't believe they called it that. So the guy just
nicknamed it Joshua. No, Joshua was the the software Falcon
(21:57):
was the old man who designed all the stuff up
and his son was Joshua. And that was the password. Oh,
that was the password. Yeah, I guess I was too
young to understand what a password was. Okay, you didn't
even there weren't passwords at the time. Shouted it at
the computer and they're like, okay, access granted. Yeah. Still
that movie holds up. Does it really check it out? Yeah,
(22:20):
it's still very very fun. Young Ali Sheety boy had
a crush on her from that movie. She was great. Yeah.
What else was she in recently? Wasn't she in something? Well?
I mean she kind of went away for a while
and then had her big comeback with the indie movie
High Art, But that was a while ago. Has she
been in anything else recently? Sure? I think I saw
(22:42):
something and something recently and I didn't realize that was her.
She looks familiar and I was like, oh, that's Ali Sheety.
I don't know, all right, I could look it up,
but I won't. It doesn't matter anyway. I still crushed
on her. So the the Royal mcbeebe was not quite
the whopper. You could actually sit down at it. The
(23:03):
Royal McBee that's the name of that sounds like a
hamburger too. It was by the Royal Typewriter Company. And
they got into computers for a second. And this is
the kind of computer that Lawrence was working with, and
it was a huge deal, Like you were saying, Avacus supercomputer. Um.
But it was still pretty dumb as far as what
(23:25):
we have today is concerned. But it was enough that
Lawrence is like Lawrence and his ILK, where like, finally
we can start running models and actually predict the weather. Yeah,
he started doing just that. He did. So he started
off with UM, a computational model of twelve meteorological meteorological
I like how you calculations, which is very basic because
(23:48):
they're infinite meteorological calculations, probably depending to stay wrong again,
like it sounds like you're about to say it wrong
and then you pull it out at the last second.
Maybe it's really impressive, but uh so that's very basic.
But he wanted to start out you know with something
at Hannibal. So he narrowed it down to twelve conditions,
basically twelve calculations that had you know, temperature, wind, speed, pressure,
(24:12):
stuff like that started forecasting weather. Uh. And then he said,
you know, it'd be great if you could see this,
So I'm gonna spit it into my wonder machine, the
McWhopper Royal MCB, and I'm gonna get a print out
so you can visualize what this looks like. So things
were going well and you had this print out, and
(24:32):
everyone was amazed because these these calculations never seemed to
repeat themselves. He was making like, um, like like word art.
You remember that. That was the first thing anybody did
on a computer. Oh yeah, it was to make word
art like a butterfly, right you would print out. Yeah.
I never could do that. I couldn't either, Like you
(24:53):
have to be able to visualize things spatially that you
have to that right kind of brain for that, right
or you have to be following a guy book that
you have you ever seen? Me? You and everyone we know. Yeah,
I love that movie. That's a great movie. Those little
kids in there they were doing that. Oh yeah, yeah, forever,
back and forth, poop. Well, I haven't. I haven't seen
(25:15):
that since it came out. It's been a while. Oh
you gotta see it again? Yeah, great movie. Ali's not
in it. It's a Miranda July right, and she like
wrote and directed to right. She did a great job.
It's like it's one of those rare movies where like
there's just the right amount of whimsy, because whimsy so
easily overpowers everything else and becomes like, yeah, this is
(25:40):
like the most perfectly balanced amount of like whimsy you've
ever seen in a movie. Yeah, there's too much whimsy.
I just like terrible Garden State. I just want to
punch in the face terrible. Although I like Garden State,
but I haven't seen it since it came out. It
hasn't aged. Well, it's just when you look at it now,
it's just so cute and whimsical. Oh yeah, it's like
come on, yeah, boy, we're getting to a lot of
(26:03):
movies today. Oh yeah, well we're stalling. We haven't even
talked about butterfly Effect yet, which is coming and I'm
dreading it. That's why I'm stalling. All right, So where
were we? He was running his calculations, printing out his
values so people could see it, and then he got
a little lazy one day. In this output he noticed
(26:27):
was interesting, so he said, you know, I'm gonna repeat
this calculation see it again, but I'm gonna save time.
I'm just gonna kind of pick up in the middle,
and I'm not gonna input as many numbers, but I'm
still using the same values, just I'm not going out
to six decimal points. So the print out he had
went to three decimal points. So he was working from
(26:49):
the print out and didn't take into account that the
computer accepted six decimal points, so he was just getting
in three correct and expecting that the outcome would be
the same, right, yes, but the outcome was way different.
He went, whoa, whoa what? Yeah, he's like, what's going
on here? It was a big deal. I mean, someone
would have come up with this eventually, probably, yeah, but
(27:10):
I sort of accidentally came upon it. It's neat that
this guy did this because it changed his career. I
think he went from emphasis on meteorology to an emphasis
on chaos math to stud scientists basically. So look, I mean,
the guy's got an attractor named after him, you know
what I mean. Yeah, Well, let's get to that. So
Lorenz starts looking at this and he's like, wait a minute,
(27:32):
this is this is weird, this is worth investigating, and
like uh, like uh, what was his name? Plankara? He said,
I need fewer variables, So I'm not going to try
to predict weather with these twelve differential equations that you
have to take into Account'm just gonna take one aspect
of weather called the rolling convection current, and I'm going
(27:56):
to see how I can write it down in formula form.
So a rolling convection current, chuck, is where you know,
how the wind is created where air at the surface
is heated and then starts to rise and suddenly cool
air from higher above comes in to fill that that
vacuum that's left, and that creates a rolling um or
(28:20):
vertically based convection current. Okay you could. I would describe
it as oven oven boiling water, a cup of coffee.
Wherever there's a temperature differential based on a vertical alignment,
you're going to have a rolling convection current. Okay, yeah,
it sounds complex, but he just picked out one thing, basically,
(28:42):
one condition, and this is the one he picked out.
But had you seen my hands moving listeners, you would
be like, oh, yeah, I know. So um He's like, okay,
I can figure this out. So he comes up with
three three formula that kind of describe a rolling invection current,
and he starts trying to figure out how to describe
(29:05):
this rolling convection current. Right, and so, like I said,
he got this these three formula, which we're basically three
variables that he calculated over time, and he plugged him
in and he found three variables that changed over time.
And he found that after a certain point, when you
graph these things out, and since there're three, you graph
them out on a three dimensional graph. So x, Y
(29:28):
and Z. Again, he wanted to just be able to
visualize this because it's easier for people to understand. He
was a very visual guy. All of a sudden, it
made this crazy graph that where the the line as
it progressed forward through time, went all over the place.
It went from this access to another access to the
other axis, and it would spend some time over here,
(29:48):
and then it would suddenly loop over to the other one,
and it followed no rhyme or reason. It never retraced
its path. And it was describing how a convey action
current changes over time, right, and Lorenz is looking at this,
he was expecting these three things to equalize and eventually
(30:10):
form a line, because that's what determinism says, things are
going to fall into a certain amount of equilibrium and
just even out over time. That is not what he
found now, And what he discovered was what pan quar
A discovered, which was that some systems, even relatively simple systems,
exhibit very complex, unpredictable behavior, which you could call chaos. Yeah.
(30:35):
And when you say things were going all over like
if you look at the graph, it it's not just
lines going in straight lines bouncing all over the place randomly,
like there was an order to it, but the lines
were not on top of one another. Like let's say
you draw a figure eight with your pencil, and then
you continue drawing that figure eight, it's gonna slip outside
(30:55):
those curves every time unless you're a robot. Um. And
that's what it ended up looking like. Yeah, yeah, it
never retraced the same path twice ever. Um. It had
a lot of really surprising properties, and at the time
it just fell completely outside the understanding of science, right. Yeah.
Luckily this happened to Lawrence, who was curious enough to
(31:18):
be like, what is going on here? And again he
sat down and started to do the math and thinking
about this and especially how it applied to the weather right,
and he came up with something very famous. Yes, the
butterfly effect. Yes, uh a, this thing kind of looked
like butterfly wings a little bit, uh and be When
(31:40):
he went to present his findings, he basically had the
notion He's like, I'm gonna I'm gonna wile these people
in the crowd in No, it's a conference that I'm
going to and I'm gonna I'm gonna say something like,
you know, the seagull flaps his wings and it starts
a small turbulence that can one that can affect whether
on the other side the world, the small little thing
(32:02):
will just grow and grow in snowball and effective things.
And he had a colleague goes like, seagull wings, that's nice,
and he said, how about this, and this is the title.
They ended up with, predictability Colin does the flap of
a butterfly's wings in Brazil set off a tornado and
Texas and everyone was like, WHOA mind's blown? Should we
(32:27):
take a break? All right, We'll be right back, all right.
So the lawns attractor. Uh, is that picture that he
(32:53):
ended up with? The Laurens attractor? And this biblical pattern
website that I found described attractors and strange attractors in
a way that even dumb old me could understand. So
if I may, he says, all right, here's the cycle
of chaos. He said, Actually, I don't know who wrote this.
(33:19):
A woman could have been a small child, could have
been no of undetermined gender. I have no idea. So
the gender neutral narrator, they said, he's sorry. I think
about a town that has like ten thousand people living
in it. To make that town work, you gotta have
like a gas station, a grocery store, a library, um,
(33:41):
whatever you need to sustain that town. So all these
things are built, everyone's happy. You have equilibrium, he said.
So that's great. Then let's say you build some Someone
comes and build a factory on the outskirts of that town,
and there's gonna be ten thousand more people living there
and they don't go to church. Maybe so, uh, did
(34:02):
I say church? They needed a church? Okay? I was
just assuming this is what's called no. But you just
have more people. So there's you need another gas station
and another grocery store. Let's say, so they build all
these things, and then you reach equilibrium. Again, it's maintained
because you build all these other systems up. That equilibrium
(34:24):
is called an attractor. Okay, so then he said it's said,
they said he capital he the royal. He said, all right, now,
let's say instead of that that factory being built, and
you have those original tin bowls, and let's say three
thousand those people just up and leave one day, and
(34:46):
the grocery store guy says, well, there's only seven thousand
people here. We need eight thousand people living here to
to make a profit. So I'm shutting down this grocery store.
Then all of a sudden, you have demand for groceries.
So things go on a little while, and someone comes
in and say, hey, this town needs a grocery store.
They build a grocery store, they can't sustain, they shut down.
(35:06):
Someone else comes along because the demand and it is
this search for equilibrium, this dyna Well, you reach equal
delibrium here and there as the store opens, periods of stability,
periods of stability, and that dynamic equilibrium is called a
strange attractor. So, an attractor is the state which a
(35:28):
system settles on. Stranger attractor is the trajectory on which
it never settles down but tries to reach the equilibrium
with periods of stability. Does that make sense that Bible
based explanation was dynamite. I understand it better than I
did before, and I understood it okay before. That's great.
(35:50):
Surely can add yeah, yeah, now you're gonna add to it. No,
that's it, No, I mean like it. Yeah. An attractor
is where if you raft something and eventually it reaches equilibrium,
it's a regular attractor. If it never reaches equilibrium, is
constantly trying to and has periods of stability. Strange attractor.
I can't. I can't top that, alright, grocery store, small town.
(36:13):
That was great. So um Lorenz, a strange attractor was
named a Lorenz attractor named after him. Big deal. They
weren't using the word chaos yet. No. But he published
that paper about butterfly wings, right, the butterfly effect, and
it coupled with his pictures the picture of a strange attractor,
which is almost the aside from fractals, almost the the
(36:38):
the um emblem or the logo for chaos theory. The
Laurens attractor is um. It got attention off the bat.
It wasn't like plan cares findings where he got neglected
for seventy years. Almost immediately everybody was talking about this
because again, what Lorenz had uncovered, which is the same
thing that plan Care had uncovered, is that determinism is
(37:00):
possibly based on an illusion that the universe isn't stable,
that the universe isn't predictable, and that what we are
seeing as stable and predictable are these little periods windows
of stability that are found in strange attractor graphs, that
that's what we think the order of the universe is,
but that that is actually the abnormal aspect of the universe,
(37:23):
and that instability unpredictability, as far as we're concerned, is
the actual state of affairs in in nature. And I
think as far as we're concerned, is a really important
point to Chuck, because it doesn't mean that nature is
unstable chaotic. It means that our picture of what we
(37:45):
understand as order doesn't jibe with how the universe actually functions.
It's just our understanding of it, and we're's just so
um anthropocentric that you know, we we see it as
chaos and disorder and something to be feared, when really
it's just complexity that we don't have the capability of
(38:05):
predicting after a certain degree. Yeah, I think that makes
me feel a little better, because when you read stuff
like this, you start to feel like, well, the Earth
could just throw us all off of its face at
any moment because it starts spinning so fast that gravity
becomes undone. And I know that's not right. By the way,
I've always loved that kind of science that shows we
(38:26):
don't know anything. Like Robert Hume, who I know, I
understand was a philosopher, but he was a philosopher scientist. Um.
His whole jam was like causing effect as an illusion
that like we all we it's it's just an assumption,
like that if you drop a pencil, it will always
fall down, and it's an illusion. And this is pretty
um gravity understanding gravity. But he makes a good pet
(38:49):
gravity when everyone's just floating around. Yeah, going this pencils
got me wacky. But but the point was that you know,
we we are. We base a lot of our assumptions
um or a lot of stuff that we take as
law are actually based on assumptions that are made from
observations over time, and that we're just making predictions that
(39:09):
causing effect as an illusion. I love that guy, and
this this definitely supports that idea for sure. Yeah. Sorry,
I'm I'm excited about chaos theory. Believe it. Well, I
mean I like that I'm able to understand it and
enough of a rudimentary way that I can talk about
it at a dinner party. Well, thank your Bible website. Well,
(39:31):
once you take the formulas out for people like us,
we're like, okay, we can understand chaos. Then when somebody says, good,
do a differential equation, just like, what a different equation? Right?
All right? So earlier I said that chaos had not
been used the word chaos to describe all this junk, uh,
(39:52):
And that didn't happen until later on and well actually
about ten years, you know, but it was kind of
at the same time this other stuff was going on
with rinds. Yeah, late sixties, early seventies. There was a
guy named Steven Smile Uh fields metal recipients. So you know,
he's good at math, and um, he describes something that
(40:14):
we now know as the small horseshoe, and it goes
a little something like this. Uh So, all right, take
a piece of dough with like bread dough, and you
smash it out into a big flat rectangle. So you're
looking at that thing and you're like, boy, I hope
this makes some good bread. This is gonna be so good.
(40:36):
So then you do a little rosemary on it. Yeah
maybe so yeah, and then um lick it before you
bake it, so you know it's yours. No one else
can happen. Uh So, you you have that flat rectangle
of dough, you roll it up into a tube, and
then you smash that down kind of flat, and then
you bend that down to where it eventually looks like
(40:57):
a horse shoe. So now how you take that horseshoe.
You take another rectangle of dough and you throw that
horseshoe onto that, and then you do the same thing.
The smell horseshoe basically says you cannot predict where the
two points of that horseshoe will end up. You can
roll it a million times and they'll end up in
(41:19):
a million different places, totally random, different places to totally random.
You never know. It's like a box of chocolates. You
never know what you're gonna get. You have to say it,
and that became known. You have to say it. Oh
what imitate Forrest Gumps? Now I can't do that. That's fine.
He's not one. He's not in my repertoire. That's fine.
Although I did see that again part of it recently.
(41:41):
Does it hold up well? I mean, take out forty
minutes of it and it would have been a better movie,
like all of that coincidence stuff that that and he
also did the smile t shirt like it was just
too much, Like he really hammered it too much was
the basis of the movie. I know. But see it
(42:02):
again and I guarantee you, like an hour and a
half into it, you'll be like, I get it. You know.
It was a good Tom Hanks movie that was overlooked.
Road to Perdition, Yeah, that was a good one. Great
Sam Indees. Oh man, that guy is awesome. Yeah, Oh
what is he gonna do? He might do something he
(42:22):
did the James bo he did Skyfall. Yeah, yeah, I
know he's gonna also that last one that wasn't so great.
He's got a potential project coming up and he would
be amazing for and I don't remember what it was.
Did you see Revolutionary Road? Yes? God have it was
just like, yeah, you want to jump off a bridge
like every five minutes during that movie. That was hardcore.
(42:45):
H he did that one too. Huh yeah, And don't
see that if you're like engaged to be married or
thinking about it, yeah, or if you're blue already. I'm yeah,
just take a really good good mood and be like
I'm sick of being in a good mood, sit down
and watch Revolutionary Road. Watch Joe Versus of Volcano instead. Uh?
Where was I smell? Horseshoe? Is what that's called? And? Um?
(43:09):
That was he was the first person to actually use
the word chaos. Oh he was? I think so? No? No, No,
York was Tom York's dad. Yeah, you're right, he wasn't
the first person York correct. But it's male's horseshoe. Illustrates
a really good point, Chuck, is it Tom York's dad? No?
But they're both British, sure, York. Actually one's Australian. No,
(43:31):
they're British. Um. So those two points, which should which
started out right by each other and then end up
in two totally different places. That applies not just a
bread dough, but also too, things like water molecules that
are right next to each other at some point and
then months later they're in two different oceans, even though
(43:52):
you would assume that they would go through all the
same motions and everything, but they're not. There's so many
different variables with things like ocean currents that two water
molecules that were once side by side end up in
totally random different places. And that's part of chaos. It's
basically chaos personified or chaos molecule fied. So we mentioned York.
(44:16):
Where I was going with that was, Um, there was
an Australian named Robert May and he was a population biologist.
So he was using math to model how animal populations
would change over time, giving certain starting conditions. Uh. So
he started using uh these equations is differential equations, and
(44:38):
he came up with a formula known as the logistic
difference equation that basically enabled him to predict these animal
populations pretty well. Yeah, and it was working pretty well
for a while, but he noticed something really really weird, right, Yeah,
he had this formula. Um, the logistic difference equation is
the name of it. Sure, Okay, So we had of
(45:00):
that formula, and he figured out that if you took
our which in this case was the reproductive rate of
a animal population, and you pushed it past three, the
number three, so that meant that the average animal in
this population of animals had three offspring in its lifetime
(45:22):
or in a season, whatever. If you pushed the past three,
all of a sudden, the number of the population would diverge.
If you pushed it equal to three, actually or more,
it would diverge, which is weird because a population of
animals can't be two different numbers, you know, like that
herd of antelope is not there's not thirty, but there's
(45:45):
also forty five of them at the same time. That's
called the superposition, and that has to do with quantum states,
not herds of antelopes. That was kind of weird. And
then he found if you pushed it a little further,
if you made the productive rate like three point oh
five seven or something like that. I think it was
a different number, but you just tweaked it a little bit,
(46:08):
not even to four. We're talking like millions of a
of a of a degree um, all of a sudden
it would turn into four so there'd be four different
numbers for that was the animal population, and then would
turn into sixteen, and then all of a sudden, after
a certain point, it would turn into chaos. The number
would be everything at once, all over the place, just
totally random numbers that it oscillated between. But in all
(46:33):
that chaos, there would be periods of stability. Right, you
push it a little further, and all of a sudden
it would just go to two again. But beyond that
it didn't go back to the original two numbers. It
went to another two. So if you looked at it
on a graph, it went line divided into two, divided
into four eight sixteen chaos, two four sixteen sixteen chaos,
(46:55):
all before you even got to the number four of
the reproductive right. And he was working with Mr York
because he was a little confounded, so he was a
mathematician buddy of his, James Yorke from the University of Maryland,
so they worked together on this. In the nine they
co authored a paper called Period three implies Chaos and man,
(47:17):
finally somebody said the word. I kept thinking it was
all these other people. Yeah, and the this this paper
where they first debut the name chaos. Um. They they
based it. UM. Tom York's Dead based it on Edward
Lawrences paper. He was like, you know what, I have
a feeling that has something to do with the Lawrens attractor.
(47:40):
So that, um, that that provided chaos to the world.
And it it was the basically the third the third
time a scientist had said we don't understand the universe
like we think we do, and determinism is based on
an illusion of order, a really chaotic universe. And this, uh,
(48:04):
this established chaos. It took off like a rocket. And
the eighties and the nineties, you know, as you know
from Jurassic Park, chaos was everything. Everybody's like, chaos, this
is totally awesome. It's the new frontier science. And then
it just went It just went away, And a lot
of people said, well, it was a little overhyped, but
I think more than anything, and I think this is
(48:24):
kind of the current understanding of chaos because it didn't
actually go away. It became a deeper and deeper field.
As you'll see, Um, people mistook what chaos meant. It
wasn't the a new the new type of science. It
was a new understanding of the universe. It was saying like, yes,
you can still use new Tony in physics, like don't
(48:45):
throw everything out the window. You can still try and
predict weather and still try and build more accurate instruments
and get you know, decent results. But you can't with
absolute perfection. Complex systems like determinism. The the ultimate goal
of determinism is false. It can never be it can
never be done because we can't have an infinitely precise
(49:07):
measurement for every variable or any variable. Therefore, we can't
predict these outcomes. Right, So you would expect science to
be like, what's the point, what's the point of anything? Well,
some some chaos people have said, no, this is this
is great, this is good. We'll take this. Will take
the universe as it is, rather than trying to force
(49:29):
it into our pretty little equations and saying like if
the ocean temperature is this at this time of year, uh,
and the fish population is this at that time, then
this is how many offspring this fish stole. This fish
population is going to have. Um, say, okay, here is
the fish population, here is the ocean temperature, here all
(49:51):
these other variables. Let's feed it into a model and
see what happens. Not this is going to happen. What
happens instead? And this is kind of the understanding of
chaos theory. Now. It's taking raw data, as much data
as you can possibly get your hands on, as precise
data as you could possibly get your hands on, and
just feeding it into a model and seeing what patterns emerge.
(50:14):
Rather than making assumptions, it's saying, what's the outcome? What
comes out of this model? Yeah, And that's why, like
when you see some things, like you know, fifty years
ago they predicted this animal be its extinct and it's not. Well,
it's because the variations were too complex they tried to predict. Uh.
And that's why if you look at a ten day forecast, you, sir,
(50:39):
are a fool. All right, Well, ten days from now
says it's going to rain in the afternoon. Come on.
But if you take if you took enough variables for
weather for like a city, and fed it into a
model of the weather for that city, you could find, uh,
you could find a time when it was similar to
(51:01):
what it is now, and you could conceivably make some
assumptions based on that. You can say, well, actually we
can we can predict a little further out than we think.
But um, it's it's based on this, this theory, this
understanding of chaos, of unpredictability, of not just not forcing
nature into our formulas, but putting data into a model
(51:24):
and seeing what comes out of it. Yeah, and then
at the end of that you learn like when that
animal is not extinct like you thought it would be,
you go back and look at the original thing and
you have a more accurate picture of how the you know,
data could have been off slightly this one value, and
then you have more buffalo than you think. Yeah, sure
(51:45):
you got buffaloed by chaos. And we're not even getting
into fractals. It's a whole other thing. And we did
a whole other podcast in June about fractals and Mandel Bena,
mandel Brett, mendel Brett, mandel Brett. Yeah, and go listen
to that one and hear me clinging to the edge
of a clift. Man. We we should end this, but first, um,
(52:09):
I want to say, there is a really interesting article
it's pretty understandable on Quanta magazine about a guy named
George and he is a chaos theory dude who's got
a whole lab and is applying it to real life.
So it's a really good picture of chaos theory and action.
(52:34):
Go check it out. Okay, Uh, if you want to
know more about chaos theory, I hope your brain is
not broken. Yeah, go take some LSD and look attical that. Um,
you can type those words into how stuff works in
the search bar any of those fractals LSD chaos. It'll
bring up some good stuff. And since I said good stuff,
(52:56):
it's time for a listener. Now, I'm gonna call this
rare shout out get requests all the time. I bet
I know which one is really dude his girlfriend? Yeah no,
so far, so good. Hey, guys, just want to say
I think you're doing a wonderful job with the show.
To this date. My first time listening was during my
(53:17):
first deployment. Uh yeah, when I listened to your list
on famous and influential films and I was hooked after that.
Since I came back State Side has spent many hours
driving to and fro uh see my girlfriend, to my barracks,
and I can happily say that they've been made all
the more enjoyable by listening to you guys. Even my
(53:39):
girlfriend Rachel has warmed up to you dudes, which was
not a pleasant I'm sorry, which was a pleasant shock
to me that she has told me repeatedly that she
cannot listen to audiobooks because quote hearing people talk on
the radio gives me a headache end quote. Anyway, I
hope you guys continue to make awesome podcasts as I'm
headed out on my next deployment. And if you could
(53:59):
give a show it out to Rachel, I'm sure it
would make her feel a little better that I got
the pleasant people on the podcast to reaffirm how much
I love her. That is John Rachel hanging there, John,
be safe and thanks for listening. Yeah, man, thank you.
That is a greed email. I love that one. Glad
we don't give you a headache. Rachel. Yeah. For she
(54:19):
listened to this son, and she's like, okay, oh yeah,
everybody's gonna get a headache from this one. Like I
came to hate the sound of my own voice from
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(54:42):
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Stuff Works dot com. Hey, and welcome to the podcast.
(00:27):
I'm Josh Clark with Charles W. Chuck Bryant, and there's
Jerry over there. So this is Stuff you Should Know,
the podcast about chaos theory. Like, uh, have you ever
seen Event Horizon? I did not bad great movie? Are
you crazy? I don't think it was great? Oh so
imagine it it. I thought it was okay. It was
(00:50):
like a love crafty and thing in our space. Loved
it all right, I love crafted it. I liked it. Um.
That's what I think of when I think of k US.
You know, there's that one part where they kind of
give you like a glimpse behind, like the dimension that
this action is taking place in, to see the chaos underneath,
and you should check that out again. I think about
(01:14):
Jurassic Park and Jeff Goldblum as as the creep. Dr
Malcolm explaining chaos in the little auto driving suv or
whatever that was. Yeah, that's what it was called in
the script, the auto driving suv scene. Yeah, and you
know what, he actually rewatched that scene and it confirmed
(01:36):
two things. One is that he uh, he actually did
a pretty decent job for a Hollywood movie with a
very rudimentary explanation of chaos. Um, and you watched it
for this Yeah, yeah, just that scene. And then it
also confirmed of what a creep that character was. Yeah,
if you watched that scene, he's like, you know, he's
all gross and flirty with her right in front of
(01:58):
her ex but there's just you know, he's talking to her.
I didn't even notice this at first. He like he
just like touches her hair out of nowhere for no reason.
He's just talking to her and he just like grabs
her hair and touches it. And I'm like, what a creep.
I know, if you look closely, you can see the
hormones emerging through his chest hair. Yeah, and I love
Jeff Goldblum. It's not a reflection on him. He was
(02:21):
basically doing Jeff Goldblum. Well that's what Yeah, sure he's
Jeff Goldblum, but I don't think that's how in the
manner in which he speaks. But I don't think he's
a creep, do you. Wow, I've got nothing against Jeff
gold Okay, I think he's a I think he's doing
Jeff Goldblum. It was also a sign of the times,
like if that movie were made today, doctor what was
(02:44):
her name in the movie? Yeah, Dr Sutler would be like,
it's very inappropriate to stroke my hair, like, don't touch me.
But this was the nineties. Was eight No, it was nineties.
It was the early mid ninety. Other thing, the book
came out and in the book, uh Ian Malcolm, who's
(03:07):
a Kayetician creep Kaetician? Right he um he he goes
into even more depth about chaos. But that was I mean,
that was the first time I ever heard of chaos
theory was from Jurassic Parks, and um it really it
was really misleading. I think the entire term chaos is
(03:28):
very misleading as far as the general public goes as
from what I researched in this this for this article. Well, yeah,
I mean you hear the word chaos as an English
speaker and you think frenetic and crazy, out of control. Yeah,
and that's not what it means in terms of science
like this, right. What it means, I guess we can
(03:48):
say up front is is basically the idea that complex
systems do not behave in very neat ways that we
can easily grasp, understand, ander measure, right, and not even
even simple systems don't. Sometimes it doesn't always have to
be complex. But um, I want to give a shout
(04:09):
out in addition to our own article to UH when
you know, when it comes to stuff like this, the
brain breaking stuff. For me, this is a breaker. You
know how I always go to like blank blank for
kids because it always helps. If there's a dinosaur mascot
on the page, it's a sure thing we can understand it.
But the best explanation for all this stuff that I
(04:31):
found on the internet was from a website called a
bar Um A B A R I. M. Publications, which
turns out to be a website about biblical patterns and
sandwiched in the middle, there is a really great, easy
to understand UH series of pages on chaos there. So
(04:51):
I was like, man, I get it now in a
rudimentary way, right, Well, yeah, um, I think even a
lot of people who deal with systems that display chaotic behavior,
which I guess is to say basically all systems eventually
under the right conditions, Um, don't necessarily understand chaos. Yeah,
(05:12):
And they defined a complex system is specifically. It doesn't
mean just like, oh it's complex, I mean it is,
but specifically, Um, they define it in a way that
helped me understand it's a system that has so much motion,
so many elements that are in motion, moving parts. Yeah,
that it takes like a computer to calculate all the
(05:33):
possibilities of like what that could look like five minutes
from now, ten years from now. So before computers came around,
we before the quantum mechanical revolution, it was there's a
lot more basic. It was like what comes up must
come down, stuff like that. Let's talk about that, Chuckers,
because when you're talking about chaos theory, it helps to
(05:54):
understand how it revolutionized the universe by getting a clear
picture of how we understood the universe leading up to
the discovery of chaos. Right, So, prior to the um
the scientific revolution, everybody was like, oh, well, it's it's God.
The Earth is at the center of the universe and
(06:16):
God is spinning everything around like a top right, it
was all a theistic explanation. Then the scientific revolution happens
and people start applying things like math and making like
mathematical discoveries and and and figuring out that there are
there's order. They're finding order in patterns and predictability to
(06:37):
the universe if you can apply mathematics to it, specifically,
if you can apply mathematics to the starting point, right, right,
So if you can, if you can um figure out
how a system works mathematically speaking, right, you can go
in and plug in whatever coordinates you want to and
(06:57):
watch it go. You can predict what what the outcomes
can be and what this is the it's based on
what at the time was a totally revolutionary idea um
By Initially, I think the cart was the first one
to kind of say, causing effect is a pretty big
part of our universe, right. Yeah. It was sort of
like where this is sixteen hundreds, where early science met philosophy.
(07:22):
They kind of complimented one another as far as something
that's we're talking about determinism, right, So that was the
kind of the seeds of determinism. Was the scientific revolution,
And like you said, where philosophy and science came together
in the form of Descartes, right, and then Newton came
along and we did a whole episode on him. Yeah,
January of this year. That was a good one. It
(07:43):
was really good. Like I think you said in that
episode that there's possibly no scienists that changed the world
more than Newton has. He's he's got legs. People shouted
out others and email, but I'll just say he's at
the near the top for sure with some other people.
The Cream. Yeah, so Newton came along and Newton said
that was his name, Isaac the Cream new anytime he
(08:06):
don't to be like cream. Yeah, you just got creamed.
I thought he was a boxer. He's a basketball player.
He was much more well known as a boxer, but
he definitely could dunk as a as a B baller. So,
um man, that threw me off a little bit. Yeah,
the Cream comes along and uh, he basically says, watch this, dude,
(08:29):
this causing effect thing you're talking about, I can express
it in quantifiable terms. And he comes up with all
of these great laws and and basically sets the stage
the foundation for science for the next three centuries or so. Yeah,
these these laws that were so rock solid and powerful
that scientists kind of got ahead of themselves a little
(08:52):
and said we're done. Like with Newton's laws, we can predict, uh,
we can predict everything if we have a good enough
beginning accurate value to plug into his equations, and they weren't.
I think there was a little hubrius and a little
just excitement about like, well we figured it all out
right that that you could take Newton's laws and if
(09:14):
you had accurate enough measurements, uh, you could predict what
the outcome would be of that system that you plug
those measurements into using these formula. And at the time,
a lot of this was like planetary, like, well, we
know that these planets are here and they're moving and
their orbiting, So if we know these things, we can
plug it into an equation and we can figure out
(09:36):
what it's going to be like in a hundred years exactly.
And they figured out the basis of determinism is what
we just said, that if you have accurate measurements, you
can take those measurements and use them to predict um
how a system is going to change over time using
differential equations. Right, yeah, so this is what this is
(09:56):
what Newton comes along and figures out that you can
describe the uni us in these mathematical terms using differential
equations and um like, you said there was a tremendous
amount of hubris, and well, I think you said there's
some hubris. I think there's a tremendous amount of hubris
where science basically said, we've mastered the universe, We've uncovered
(10:17):
the blueprint of the universe, and now we understand everything.
It's just a matter now of getting our scientific measurements
more and more and more exact. Because again, the hallmark
of determinism is that if you have exact measurements, you
can predict an outcome accurately, like the pool queue example
or the pool table example. Right right, So if you've
(10:39):
got a pool table, let's say you're playing some nine ball.
You have that beautiful little diamond set up, you got
your cue ball, you put that cue ball, and you
you crack it with the queue, and if you are
super accurate with your initial measurements, you should be able
to mathematically plot out via angles where the balls will
end up, right exactly, like you can say, this is
(11:01):
what the table will look like after the break. If
you know the force the angle, all those little variable temperature,
if there's wind in the room, like the felt on
the table, like everything. The more specific you are, the
more accurate your end result will be. Right. And then
one of the other hallmarks of determinism is that if
you take those exact same initial conditions and do them again,
(11:23):
the table, the pool table will look exactly the same
after the break, which is pretty much impossible for like
a human to do with their hands. Sure, but the
idea at the time of science is that if you
could build a perfect machine, sure that could recreate these conditions,
it will happen the same way every time. Right, And this,
I mean this led to they had hubris, but you
(11:45):
could understand it when like literally in two people predicted
Neptune would exist within months, that would exist, but does exist.
And this is not by looking up in the sky
like they did it with math and they were right.
So imagine in eighty when that happens, they're like, yeah,
(12:07):
we kinda we've got the math down, so we're pretty
much all knowing well. Plus also, for the most part,
these not just with Neptune, they were finding um that
this stuff really panned out. It held true for everything
from um you know, the investigation into electricity to new
chemical reactions and understanding those and it it laid the
(12:30):
scientific revolution, laid the basis for the industrial revolution, and
just the change that came out of the world like that.
It definitely there. It is understandable how science kind of
was like we got it all figured out well, and
like you said, they even Galileo was smart enough to
know there's uncertainty in these measurements, like the precision is key.
(12:56):
So they spent what does the article say, a lot
of the much of an enth and twenty century just
trying to build better instrumentation to get more and more
smaller and smaller and more precise measurements. Right, That was
like basically the goal of it, right, Yeah, which was
the right direction. That's like exactly what they should have
been doing. The problem is there, Like you said, Galileo
(13:18):
knew that there was some sort of there there gonna
be some flaws and measurement that we just didn't have
those great scientific instruments yet. It's called the uncertainty principle.
It's accuracy, right, But the idea is that if you
have a good enough instruments, you can overcome that, and
(13:39):
that the the more you shrink the um error in
measuring the initial conditions, the more you're gonna shrink the
error in the outcome. It would be proportionate. Right. They
were correct. The thing is they were also aware but
ignoring in a lot a lot of ways some outstanding problems,
(14:03):
specifically something called the end body problem. You know what,
I'm so excited about this. I need to take a break.
I think that's a good idea. I need to go
check out my end body in the bathroom. Okay, and
we'll be back. All right, check, we're back. So there's
(14:37):
some there's some issues right with determinism. There's some some
weird problems out there that are saying like, hey, pay
attention to me because I'm not sure determinism works. Uh.
And one of the one is the end body problem. Yeah.
How this came about was that was King Oscar number
(14:58):
two of Sweet and Norway. Yeah, I don't want to
leave out Norway both. Uh. He said, you know what,
let's offer a prize to anyone who can prove the
stability of the solar system something that has been stable
for a long time before that and a lot of
the most brilliant minds on planet Earth got together and
(15:19):
tried to do this, uh with mathematical proofs, and no
one could do it. Uh. And then a dude name Honoree.
You gotta help me there with that, Oh, say, the
whole thing very nice. He was French, believe it or not,
and he was a mathematician, and he said, you know what,
(15:41):
I'm not gonna look at this big picture of all
the planets in the sun and all their orbits. You'd
have to be a fool to try that. Sure, he said,
I'm gonna shrink this down, Like we talked about shrinking
that initial value, you know, and um, that initial condition.
He shrunk it down. He said, I'm gonna look at
just a couple of bodies orbiting one another, uh, with
(16:03):
a common center of gravity. And I'm gonna look at this.
And this was called the N body problem. Yeah, which
was smart to do, because the more variables you factor
into um a nonlinear equation like that, just the harder
it's gonna be. So he shrunk it down. So the
N body problem has to do with three or more
(16:24):
celestial bodies orbiting one another. So Plank said, I'll just
start with three. Smart and what he found from doing
his equations for this this King Oscar. The sequel prize
um was that shrinking the initial conditions um measurement or
rate of error right, did not really shrink the the
(16:49):
error in the outcome, which flies in the face of determinism.
What he found was that just very very minute different
is in the initial conditions fed into a system produced
wildly different outcomes after a fairly short time. Yeah, like,
let me just round off the mass of this planet
(17:11):
at like the eighth decimal point, and you know who cares?
Who cares at that point? Let me just round that
one to a two, and that would throw everything off
at a at a pretty high rate. And he said,
wait a minute, I think this contest is in polsib right,
He said, there is no way to prove prove, to
(17:34):
prove the stability of the Solar system, because he just
uncovered the idea that it's impossible for us to predict
the the the rate of change among celestial bodies. Yeah,
it's such a complex system. There are far too many
variables that, uh, it's impossible to start with something so
(17:58):
minute to get the equation whatever the sum that you want. Well,
not only that, but the result, not only that and
this is what really undermined determinism was that he figured
out that you would have to have an infinitely precise measurement,
which even if you build a perfect machine that could
(18:20):
take the infinitely or a perfect machine that could take
a measurement of like the the movement of a celestial
body around another, you, it's literally impossible to get infinite
and infinitely precise measurement, which means that we could never
predict out to a certain degree the movement of the
(18:41):
celestial bodies. Like he was saying, like, no, you you
can't get You can't build a machine that that gets
measurements enough that we can overcome this, like determinism is wrong,
Like you can't just say, uh, we have the understanding
to predict everything. There's a lot of stuff out there
(19:02):
that we're not able to predict. And he uncovered it
trying to figure out this end body problem. Yeah, and
King Oscar the sequel said you win, Yeah, bring me
another rack of lamb and uh, here's your prize. And
he won by proving that it was impossible, which is
pretty interesting. And they utterly and completely changed not just math,
but like our our our understanding of the universe, and
(19:24):
our understanding of our understanding of the universe, which is
even more kind of earth shaking. Yeah, he discovered dynamical
instability or chaos, and um, they didn't have supercomputers at
the time, so it would be a little while, about
seventy years at m I T until uh we could
actually kind of feed these things into machines capable of
(19:47):
plotting these things out in a way that we could see,
which was really incredible. So there is this dude um
seven years later, uh named um Edward Lawrence Lawrence. Yeah. Well,
first of all, we should set the stage the reason
this guy he was a meteorologist and scientists, right, not
(20:08):
that those are not the same thing, right, He's a
scientist who dabbled the meteorology. Here was a mathematician, Yeah,
but he was really into meteorology because it was there
was a weird juxtaposition at the time where we were
sending people into outer space but we couldn't predict the weather. Yeah,
and it was it was definitely a blot on the
(20:28):
field of meteorology. People were like, do you guys know
what you're doing? And and meteorologists are like, you have
no idea how hard this is? Like yeah, we can
predict it a couple of days out, but after that,
it's just it's totally unpredictable. It drives as mad and
it's not. It wasn't just there. Um, their reputations that
were at stake, like people were losing their lives because
(20:50):
of it, right, Yeah. N two there were two notorious storms,
one on the East coast and one on the west. Uh,
the ash Wednesday storm in the East and the big
blow on the West that of a lot of people,
cost hundreds of millions of dollars in damage. And people
were like, you know, we need to be able to
see these things coming a little more because it's a problem.
And meteorologists were like, why did you do it then?
(21:13):
So they thought the key was these big supercomputers. Remember
the supercomputers. When they came out the big rooms full
of hardware, it was amazing, and they were finally able
to do like these incredible calculations that we could never
do before. I know, they were able to like crunch
sixty four bites a second. Yeah, we had the advocates
and then the supercomputer. There was nothing in between. Um.
(21:36):
I looked up the computer that Lawrence was working with
the Whopper Royal McBee. What was the Whopper board games?
Was it? It called the Whopper w a PR I
can't believe they called it that. So the guy just
nicknamed it Joshua. No, Joshua was the the software Falcon
(21:57):
was the old man who designed all the stuff up
and his son was Joshua. And that was the password. Oh,
that was the password. Yeah, I guess I was too
young to understand what a password was. Okay, you didn't
even there weren't passwords at the time. Shouted it at
the computer and they're like, okay, access granted. Yeah. Still
that movie holds up. Does it really check it out? Yeah,
(22:20):
it's still very very fun. Young Ali Sheety boy had
a crush on her from that movie. She was great. Yeah.
What else was she in recently? Wasn't she in something? Well?
I mean she kind of went away for a while
and then had her big comeback with the indie movie
High Art, But that was a while ago. Has she
been in anything else recently? Sure? I think I saw
(22:42):
something and something recently and I didn't realize that was her.
She looks familiar and I was like, oh, that's Ali Sheety.
I don't know, all right, I could look it up,
but I won't. It doesn't matter anyway. I still crushed
on her. So the the Royal mcbeebe was not quite
the whopper. You could actually sit down at it. The
(23:03):
Royal McBee that's the name of that sounds like a
hamburger too. It was by the Royal Typewriter Company. And
they got into computers for a second. And this is
the kind of computer that Lawrence was working with, and
it was a huge deal, Like you were saying, Avacus supercomputer. Um.
But it was still pretty dumb as far as what
(23:25):
we have today is concerned. But it was enough that
Lawrence is like Lawrence and his ILK, where like, finally
we can start running models and actually predict the weather. Yeah,
he started doing just that. He did. So he started
off with UM, a computational model of twelve meteorological meteorological
I like how you calculations, which is very basic because
(23:48):
they're infinite meteorological calculations, probably depending to stay wrong again,
like it sounds like you're about to say it wrong
and then you pull it out at the last second.
Maybe it's really impressive, but uh so that's very basic.
But he wanted to start out you know with something
at Hannibal. So he narrowed it down to twelve conditions,
basically twelve calculations that had you know, temperature, wind, speed, pressure,
(24:12):
stuff like that started forecasting weather. Uh. And then he said,
you know, it'd be great if you could see this,
So I'm gonna spit it into my wonder machine, the
McWhopper Royal MCB, and I'm gonna get a print out
so you can visualize what this looks like. So things
were going well and you had this print out, and
(24:32):
everyone was amazed because these these calculations never seemed to
repeat themselves. He was making like, um, like like word art.
You remember that. That was the first thing anybody did
on a computer. Oh yeah, it was to make word
art like a butterfly, right you would print out. Yeah.
I never could do that. I couldn't either, Like you
(24:53):
have to be able to visualize things spatially that you
have to that right kind of brain for that, right
or you have to be following a guy book that
you have you ever seen? Me? You and everyone we know. Yeah,
I love that movie. That's a great movie. Those little
kids in there they were doing that. Oh yeah, yeah, forever,
back and forth, poop. Well, I haven't. I haven't seen
(25:15):
that since it came out. It's been a while. Oh
you gotta see it again? Yeah, great movie. Ali's not
in it. It's a Miranda July right, and she like
wrote and directed to right. She did a great job.
It's like it's one of those rare movies where like
there's just the right amount of whimsy, because whimsy so
easily overpowers everything else and becomes like, yeah, this is
(25:40):
like the most perfectly balanced amount of like whimsy you've
ever seen in a movie. Yeah, there's too much whimsy.
I just like terrible Garden State. I just want to
punch in the face terrible. Although I like Garden State,
but I haven't seen it since it came out. It
hasn't aged. Well, it's just when you look at it now,
it's just so cute and whimsical. Oh yeah, it's like
come on, yeah, boy, we're getting to a lot of
(26:03):
movies today. Oh yeah, well we're stalling. We haven't even
talked about butterfly Effect yet, which is coming and I'm
dreading it. That's why I'm stalling. All right, So where
were we? He was running his calculations, printing out his
values so people could see it, and then he got
a little lazy one day. In this output he noticed
(26:27):
was interesting, so he said, you know, I'm gonna repeat
this calculation see it again, but I'm gonna save time.
I'm just gonna kind of pick up in the middle,
and I'm not gonna input as many numbers, but I'm
still using the same values, just I'm not going out
to six decimal points. So the print out he had
went to three decimal points. So he was working from
(26:49):
the print out and didn't take into account that the
computer accepted six decimal points, so he was just getting
in three correct and expecting that the outcome would be
the same, right, yes, but the outcome was way different.
He went, whoa, whoa what? Yeah, he's like, what's going
on here? It was a big deal. I mean, someone
would have come up with this eventually, probably, yeah, but
(27:10):
I sort of accidentally came upon it. It's neat that
this guy did this because it changed his career. I
think he went from emphasis on meteorology to an emphasis
on chaos math to stud scientists basically. So look, I mean,
the guy's got an attractor named after him, you know
what I mean. Yeah, Well, let's get to that. So
Lorenz starts looking at this and he's like, wait a minute,
(27:32):
this is this is weird, this is worth investigating, and
like uh, like uh, what was his name? Plankara? He said,
I need fewer variables, So I'm not going to try
to predict weather with these twelve differential equations that you
have to take into Account'm just gonna take one aspect
of weather called the rolling convection current, and I'm going
(27:56):
to see how I can write it down in formula form.
So a rolling convection current, chuck, is where you know,
how the wind is created where air at the surface
is heated and then starts to rise and suddenly cool
air from higher above comes in to fill that that
vacuum that's left, and that creates a rolling um or
(28:20):
vertically based convection current. Okay you could. I would describe
it as oven oven boiling water, a cup of coffee.
Wherever there's a temperature differential based on a vertical alignment,
you're going to have a rolling convection current. Okay, yeah,
it sounds complex, but he just picked out one thing, basically,
(28:42):
one condition, and this is the one he picked out.
But had you seen my hands moving listeners, you would
be like, oh, yeah, I know. So um He's like, okay,
I can figure this out. So he comes up with
three three formula that kind of describe a rolling invection current,
and he starts trying to figure out how to describe
(29:05):
this rolling convection current. Right, and so, like I said,
he got this these three formula, which we're basically three
variables that he calculated over time, and he plugged him
in and he found three variables that changed over time.
And he found that after a certain point, when you
graph these things out, and since there're three, you graph
them out on a three dimensional graph. So x, Y
(29:28):
and Z. Again, he wanted to just be able to
visualize this because it's easier for people to understand. He
was a very visual guy. All of a sudden, it
made this crazy graph that where the the line as
it progressed forward through time, went all over the place.
It went from this access to another access to the
other axis, and it would spend some time over here,
(29:48):
and then it would suddenly loop over to the other one,
and it followed no rhyme or reason. It never retraced
its path. And it was describing how a convey action
current changes over time, right, and Lorenz is looking at this,
he was expecting these three things to equalize and eventually
(30:10):
form a line, because that's what determinism says, things are
going to fall into a certain amount of equilibrium and
just even out over time. That is not what he
found now, And what he discovered was what pan quar
A discovered, which was that some systems, even relatively simple systems,
exhibit very complex, unpredictable behavior, which you could call chaos. Yeah.
(30:35):
And when you say things were going all over like
if you look at the graph, it it's not just
lines going in straight lines bouncing all over the place randomly,
like there was an order to it, but the lines
were not on top of one another. Like let's say
you draw a figure eight with your pencil, and then
you continue drawing that figure eight, it's gonna slip outside
(30:55):
those curves every time unless you're a robot. Um. And
that's what it ended up looking like. Yeah, yeah, it
never retraced the same path twice ever. Um. It had
a lot of really surprising properties, and at the time
it just fell completely outside the understanding of science, right. Yeah.
Luckily this happened to Lawrence, who was curious enough to
(31:18):
be like, what is going on here? And again he
sat down and started to do the math and thinking
about this and especially how it applied to the weather right,
and he came up with something very famous. Yes, the
butterfly effect. Yes, uh a, this thing kind of looked
like butterfly wings a little bit, uh and be When
(31:40):
he went to present his findings, he basically had the
notion He's like, I'm gonna I'm gonna wile these people
in the crowd in No, it's a conference that I'm
going to and I'm gonna I'm gonna say something like,
you know, the seagull flaps his wings and it starts
a small turbulence that can one that can affect whether
on the other side the world, the small little thing
(32:02):
will just grow and grow in snowball and effective things.
And he had a colleague goes like, seagull wings, that's nice,
and he said, how about this, and this is the title.
They ended up with, predictability Colin does the flap of
a butterfly's wings in Brazil set off a tornado and
Texas and everyone was like, WHOA mind's blown? Should we
(32:27):
take a break? All right, We'll be right back, all right.
So the lawns attractor. Uh, is that picture that he
(32:53):
ended up with? The Laurens attractor? And this biblical pattern
website that I found described attractors and strange attractors in
a way that even dumb old me could understand. So
if I may, he says, all right, here's the cycle
of chaos. He said, Actually, I don't know who wrote this.
(33:19):
A woman could have been a small child, could have
been no of undetermined gender. I have no idea. So
the gender neutral narrator, they said, he's sorry. I think
about a town that has like ten thousand people living
in it. To make that town work, you gotta have
like a gas station, a grocery store, a library, um,
(33:41):
whatever you need to sustain that town. So all these
things are built, everyone's happy. You have equilibrium, he said.
So that's great. Then let's say you build some Someone
comes and build a factory on the outskirts of that town,
and there's gonna be ten thousand more people living there
and they don't go to church. Maybe so, uh, did
(34:02):
I say church? They needed a church? Okay? I was
just assuming this is what's called no. But you just
have more people. So there's you need another gas station
and another grocery store. Let's say, so they build all
these things, and then you reach equilibrium. Again, it's maintained
because you build all these other systems up. That equilibrium
(34:24):
is called an attractor. Okay, so then he said it's said,
they said he capital he the royal. He said, all right, now,
let's say instead of that that factory being built, and
you have those original tin bowls, and let's say three
thousand those people just up and leave one day, and
(34:46):
the grocery store guy says, well, there's only seven thousand
people here. We need eight thousand people living here to
to make a profit. So I'm shutting down this grocery store.
Then all of a sudden, you have demand for groceries.
So things go on a little while, and someone comes
in and say, hey, this town needs a grocery store.
They build a grocery store, they can't sustain, they shut down.
(35:06):
Someone else comes along because the demand and it is
this search for equilibrium, this dyna Well, you reach equal
delibrium here and there as the store opens, periods of stability,
periods of stability, and that dynamic equilibrium is called a
strange attractor. So, an attractor is the state which a
(35:28):
system settles on. Stranger attractor is the trajectory on which
it never settles down but tries to reach the equilibrium
with periods of stability. Does that make sense that Bible
based explanation was dynamite. I understand it better than I
did before, and I understood it okay before. That's great.
(35:50):
Surely can add yeah, yeah, now you're gonna add to it. No,
that's it, No, I mean like it. Yeah. An attractor
is where if you raft something and eventually it reaches equilibrium,
it's a regular attractor. If it never reaches equilibrium, is
constantly trying to and has periods of stability. Strange attractor.
I can't. I can't top that, alright, grocery store, small town.
(36:13):
That was great. So um Lorenz, a strange attractor was
named a Lorenz attractor named after him. Big deal. They
weren't using the word chaos yet. No. But he published
that paper about butterfly wings, right, the butterfly effect, and
it coupled with his pictures the picture of a strange attractor,
which is almost the aside from fractals, almost the the
(36:38):
the um emblem or the logo for chaos theory. The
Laurens attractor is um. It got attention off the bat.
It wasn't like plan cares findings where he got neglected
for seventy years. Almost immediately everybody was talking about this
because again, what Lorenz had uncovered, which is the same
thing that plan Care had uncovered, is that determinism is
(37:00):
possibly based on an illusion that the universe isn't stable,
that the universe isn't predictable, and that what we are
seeing as stable and predictable are these little periods windows
of stability that are found in strange attractor graphs, that
that's what we think the order of the universe is,
but that that is actually the abnormal aspect of the universe,
(37:23):
and that instability unpredictability, as far as we're concerned, is
the actual state of affairs in in nature. And I
think as far as we're concerned, is a really important
point to Chuck, because it doesn't mean that nature is
unstable chaotic. It means that our picture of what we
(37:45):
understand as order doesn't jibe with how the universe actually functions.
It's just our understanding of it, and we're's just so
um anthropocentric that you know, we we see it as
chaos and disorder and something to be feared, when really
it's just complexity that we don't have the capability of
(38:05):
predicting after a certain degree. Yeah, I think that makes
me feel a little better, because when you read stuff
like this, you start to feel like, well, the Earth
could just throw us all off of its face at
any moment because it starts spinning so fast that gravity
becomes undone. And I know that's not right. By the way,
I've always loved that kind of science that shows we
(38:26):
don't know anything. Like Robert Hume, who I know, I
understand was a philosopher, but he was a philosopher scientist. Um.
His whole jam was like causing effect as an illusion
that like we all we it's it's just an assumption,
like that if you drop a pencil, it will always
fall down, and it's an illusion. And this is pretty
um gravity understanding gravity. But he makes a good pet
(38:49):
gravity when everyone's just floating around. Yeah, going this pencils
got me wacky. But but the point was that you know,
we we are. We base a lot of our assumptions
um or a lot of stuff that we take as
law are actually based on assumptions that are made from
observations over time, and that we're just making predictions that
(39:09):
causing effect as an illusion. I love that guy, and
this this definitely supports that idea for sure. Yeah. Sorry,
I'm I'm excited about chaos theory. Believe it. Well, I
mean I like that I'm able to understand it and
enough of a rudimentary way that I can talk about
it at a dinner party. Well, thank your Bible website. Well,
(39:31):
once you take the formulas out for people like us,
we're like, okay, we can understand chaos. Then when somebody says, good,
do a differential equation, just like, what a different equation? Right?
All right? So earlier I said that chaos had not
been used the word chaos to describe all this junk, uh,
(39:52):
And that didn't happen until later on and well actually
about ten years, you know, but it was kind of
at the same time this other stuff was going on
with rinds. Yeah, late sixties, early seventies. There was a
guy named Steven Smile Uh fields metal recipients. So you know,
he's good at math, and um, he describes something that
(40:14):
we now know as the small horseshoe, and it goes
a little something like this. Uh So, all right, take
a piece of dough with like bread dough, and you
smash it out into a big flat rectangle. So you're
looking at that thing and you're like, boy, I hope
this makes some good bread. This is gonna be so good.
(40:36):
So then you do a little rosemary on it. Yeah
maybe so yeah, and then um lick it before you
bake it, so you know it's yours. No one else
can happen. Uh So, you you have that flat rectangle
of dough, you roll it up into a tube, and
then you smash that down kind of flat, and then
you bend that down to where it eventually looks like
(40:57):
a horse shoe. So now how you take that horseshoe.
You take another rectangle of dough and you throw that
horseshoe onto that, and then you do the same thing.
The smell horseshoe basically says you cannot predict where the
two points of that horseshoe will end up. You can
roll it a million times and they'll end up in
(41:19):
a million different places, totally random, different places to totally random.
You never know. It's like a box of chocolates. You
never know what you're gonna get. You have to say it,
and that became known. You have to say it. Oh
what imitate Forrest Gumps? Now I can't do that. That's fine.
He's not one. He's not in my repertoire. That's fine.
Although I did see that again part of it recently.
(41:41):
Does it hold up well? I mean, take out forty
minutes of it and it would have been a better movie,
like all of that coincidence stuff that that and he
also did the smile t shirt like it was just
too much, Like he really hammered it too much was
the basis of the movie. I know. But see it
(42:02):
again and I guarantee you, like an hour and a
half into it, you'll be like, I get it. You know.
It was a good Tom Hanks movie that was overlooked.
Road to Perdition, Yeah, that was a good one. Great
Sam Indees. Oh man, that guy is awesome. Yeah, Oh
what is he gonna do? He might do something he
(42:22):
did the James bo he did Skyfall. Yeah, yeah, I
know he's gonna also that last one that wasn't so great.
He's got a potential project coming up and he would
be amazing for and I don't remember what it was.
Did you see Revolutionary Road? Yes? God have it was
just like, yeah, you want to jump off a bridge
like every five minutes during that movie. That was hardcore.
(42:45):
H he did that one too. Huh yeah, And don't
see that if you're like engaged to be married or
thinking about it, yeah, or if you're blue already. I'm yeah,
just take a really good good mood and be like
I'm sick of being in a good mood, sit down
and watch Revolutionary Road. Watch Joe Versus of Volcano instead. Uh?
Where was I smell? Horseshoe? Is what that's called? And? Um?
(43:09):
That was he was the first person to actually use
the word chaos. Oh he was? I think so? No? No, No,
York was Tom York's dad. Yeah, you're right, he wasn't
the first person York correct. But it's male's horseshoe. Illustrates
a really good point, Chuck, is it Tom York's dad? No?
But they're both British, sure, York. Actually one's Australian. No,
(43:31):
they're British. Um. So those two points, which should which
started out right by each other and then end up
in two totally different places. That applies not just a
bread dough, but also too, things like water molecules that
are right next to each other at some point and
then months later they're in two different oceans, even though
(43:52):
you would assume that they would go through all the
same motions and everything, but they're not. There's so many
different variables with things like ocean currents that two water
molecules that were once side by side end up in
totally random different places. And that's part of chaos. It's
basically chaos personified or chaos molecule fied. So we mentioned York.
(44:16):
Where I was going with that was, Um, there was
an Australian named Robert May and he was a population biologist.
So he was using math to model how animal populations
would change over time, giving certain starting conditions. Uh. So
he started using uh these equations is differential equations, and
(44:38):
he came up with a formula known as the logistic
difference equation that basically enabled him to predict these animal
populations pretty well. Yeah, and it was working pretty well
for a while, but he noticed something really really weird, right, Yeah,
he had this formula. Um, the logistic difference equation is
the name of it. Sure, Okay, So we had of
(45:00):
that formula, and he figured out that if you took
our which in this case was the reproductive rate of
a animal population, and you pushed it past three, the
number three, so that meant that the average animal in
this population of animals had three offspring in its lifetime
(45:22):
or in a season, whatever. If you pushed the past three,
all of a sudden, the number of the population would diverge.
If you pushed it equal to three, actually or more,
it would diverge, which is weird because a population of
animals can't be two different numbers, you know, like that
herd of antelope is not there's not thirty, but there's
(45:45):
also forty five of them at the same time. That's
called the superposition, and that has to do with quantum states,
not herds of antelopes. That was kind of weird. And
then he found if you pushed it a little further,
if you made the productive rate like three point oh
five seven or something like that. I think it was
a different number, but you just tweaked it a little bit,
(46:08):
not even to four. We're talking like millions of a
of a of a degree um, all of a sudden
it would turn into four so there'd be four different
numbers for that was the animal population, and then would
turn into sixteen, and then all of a sudden, after
a certain point, it would turn into chaos. The number
would be everything at once, all over the place, just
totally random numbers that it oscillated between. But in all
(46:33):
that chaos, there would be periods of stability. Right, you
push it a little further, and all of a sudden
it would just go to two again. But beyond that
it didn't go back to the original two numbers. It
went to another two. So if you looked at it
on a graph, it went line divided into two, divided
into four eight sixteen chaos, two four sixteen sixteen chaos,
(46:55):
all before you even got to the number four of
the reproductive right. And he was working with Mr York
because he was a little confounded, so he was a
mathematician buddy of his, James Yorke from the University of Maryland,
so they worked together on this. In the nine they
co authored a paper called Period three implies Chaos and man,
(47:17):
finally somebody said the word. I kept thinking it was
all these other people. Yeah, and the this this paper
where they first debut the name chaos. Um. They they
based it. UM. Tom York's Dead based it on Edward
Lawrences paper. He was like, you know what, I have
a feeling that has something to do with the Lawrens attractor.
(47:40):
So that, um, that that provided chaos to the world.
And it it was the basically the third the third
time a scientist had said we don't understand the universe
like we think we do, and determinism is based on
an illusion of order, a really chaotic universe. And this, uh,
(48:04):
this established chaos. It took off like a rocket. And
the eighties and the nineties, you know, as you know
from Jurassic Park, chaos was everything. Everybody's like, chaos, this
is totally awesome. It's the new frontier science. And then
it just went It just went away, And a lot
of people said, well, it was a little overhyped, but
I think more than anything, and I think this is
(48:24):
kind of the current understanding of chaos because it didn't
actually go away. It became a deeper and deeper field.
As you'll see, Um, people mistook what chaos meant. It
wasn't the a new the new type of science. It
was a new understanding of the universe. It was saying like, yes,
you can still use new Tony in physics, like don't
(48:45):
throw everything out the window. You can still try and
predict weather and still try and build more accurate instruments
and get you know, decent results. But you can't with
absolute perfection. Complex systems like determinism. The the ultimate goal
of determinism is false. It can never be it can
never be done because we can't have an infinitely precise
(49:07):
measurement for every variable or any variable. Therefore, we can't
predict these outcomes. Right, So you would expect science to
be like, what's the point, what's the point of anything? Well,
some some chaos people have said, no, this is this
is great, this is good. We'll take this. Will take
the universe as it is, rather than trying to force
(49:29):
it into our pretty little equations and saying like if
the ocean temperature is this at this time of year, uh,
and the fish population is this at that time, then
this is how many offspring this fish stole. This fish
population is going to have. Um, say, okay, here is
the fish population, here is the ocean temperature, here all
(49:51):
these other variables. Let's feed it into a model and
see what happens. Not this is going to happen. What
happens instead? And this is kind of the understanding of
chaos theory. Now. It's taking raw data, as much data
as you can possibly get your hands on, as precise
data as you could possibly get your hands on, and
just feeding it into a model and seeing what patterns emerge.
(50:14):
Rather than making assumptions, it's saying, what's the outcome? What
comes out of this model? Yeah, And that's why, like
when you see some things, like you know, fifty years
ago they predicted this animal be its extinct and it's not. Well,
it's because the variations were too complex they tried to predict. Uh.
And that's why if you look at a ten day forecast, you, sir,
(50:39):
are a fool. All right, Well, ten days from now
says it's going to rain in the afternoon. Come on.
But if you take if you took enough variables for
weather for like a city, and fed it into a
model of the weather for that city, you could find, uh,
you could find a time when it was similar to
(51:01):
what it is now, and you could conceivably make some
assumptions based on that. You can say, well, actually we
can we can predict a little further out than we think.
But um, it's it's based on this, this theory, this
understanding of chaos, of unpredictability, of not just not forcing
nature into our formulas, but putting data into a model
(51:24):
and seeing what comes out of it. Yeah, and then
at the end of that you learn like when that
animal is not extinct like you thought it would be,
you go back and look at the original thing and
you have a more accurate picture of how the you know,
data could have been off slightly this one value, and
then you have more buffalo than you think. Yeah, sure
(51:45):
you got buffaloed by chaos. And we're not even getting
into fractals. It's a whole other thing. And we did
a whole other podcast in June about fractals and Mandel Bena,
mandel Brett, mendel Brett, mandel Brett. Yeah, and go listen
to that one and hear me clinging to the edge
of a clift. Man. We we should end this, but first, um,
(52:09):
I want to say, there is a really interesting article
it's pretty understandable on Quanta magazine about a guy named
George and he is a chaos theory dude who's got
a whole lab and is applying it to real life.
So it's a really good picture of chaos theory and action.
(52:34):
Go check it out. Okay, Uh, if you want to
know more about chaos theory, I hope your brain is
not broken. Yeah, go take some LSD and look attical that. Um,
you can type those words into how stuff works in
the search bar any of those fractals LSD chaos. It'll
bring up some good stuff. And since I said good stuff,
(52:56):
it's time for a listener. Now, I'm gonna call this
rare shout out get requests all the time. I bet
I know which one is really dude his girlfriend? Yeah no,
so far, so good. Hey, guys, just want to say
I think you're doing a wonderful job with the show.
To this date. My first time listening was during my
(53:17):
first deployment. Uh yeah, when I listened to your list
on famous and influential films and I was hooked after that.
Since I came back State Side has spent many hours
driving to and fro uh see my girlfriend, to my barracks,
and I can happily say that they've been made all
the more enjoyable by listening to you guys. Even my
(53:39):
girlfriend Rachel has warmed up to you dudes, which was
not a pleasant I'm sorry, which was a pleasant shock
to me that she has told me repeatedly that she
cannot listen to audiobooks because quote hearing people talk on
the radio gives me a headache end quote. Anyway, I
hope you guys continue to make awesome podcasts as I'm
headed out on my next deployment. And if you could
(53:59):
give a show it out to Rachel, I'm sure it
would make her feel a little better that I got
the pleasant people on the podcast to reaffirm how much
I love her. That is John Rachel hanging there, John,
be safe and thanks for listening. Yeah, man, thank you.
That is a greed email. I love that one. Glad
we don't give you a headache. Rachel. Yeah. For she
(54:19):
listened to this son, and she's like, okay, oh yeah,
everybody's gonna get a headache from this one. Like I
came to hate the sound of my own voice from
this one. You'll be right. If you want to get
in touch with us, you can hang out with us
on Twitter at s y s K podcast thing goes
for Instagram. You can hang out with us on Facebook
dot com slash stuff. You should know. You can send
us an email to stuff podcast to how stuff Works
(54:42):
dot com and has always joined us at home on
the web. Stuff you Should Know dot com. For more
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