Program Notes
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Guest speakers: Ralph Abraham and Terence McKenna
Today’s program features a 1985 conversation between Terence McKenna and Ralph Abraham that took place at the Esalen Institute near Big Sur, California.
Their conversation centers on the science of chaotic attractors and models of their actions. While mathematicians will more fully understand some of these concepts, Ralph has a way of explaining them so we can all grok what he explains.
The concept of using mathematical models for things like tracking hurricanes is common to most of us, ideas about modeling society may be new to many of us, and exciting as well. If ever there was a time to project the track of our culture’s future, this is it.
Ralph Abraham’s recent book, Schims, is an excellent companion to this conversation.
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Transcript
00:00:00 ►
Three-dimensional, transforming, musical, linguistic objects.
00:00:09 ►
Alpha Shades.
00:00:17 ►
Greetings from cyberdelic space.
00:00:20 ►
This is Lorenzo, and I’m your host here in the Psychedelic Salon.
00:00:23 ►
And today I’m going to play another one of the tapes that my friend Mac Larson gave me a little while back.
00:00:29 ►
This one is from August of 1985, and it features Terrence McKenna and Ralph Abraham.
00:00:36 ►
I asked Ralph if this was their first Esalen conversation, and he said, well, it may have been,
00:00:43 ►
although it wasn’t the first time that they spoke
00:00:45 ►
together. Before this talk, they appeared, well, at the Omega Institute in upstate New York and at
00:00:51 ►
Open Center in New York City. In any event, this is a rare old recording, apparently made by Paul
00:00:58 ►
Herbert, according to the label, and this one is labeled number seven unedited so it may be a new one I don’t know now before I
00:01:08 ►
listened to this recording well I was feeling kind of bad about the fact that I had to tell
00:01:13 ►
Ralph that I didn’t feel I was any longer up to the task of interviewing him about his latest book
00:01:19 ►
which is amazing by the way but in recording, as he was interviewing Ralph,
00:01:25 ►
Terrence said, and I quote,
00:01:27 ►
I don’t fully understand Ralph. No one does.
00:01:31 ►
And then he went on to say that he would try to interview him.
00:01:35 ►
Now I don’t feel quite so badly about losing my edge for doing interviews.
00:01:40 ►
Even in my prime, I still needed somebody to help me interview somebody like Ralph.
00:01:46 ►
And the irony here is that he is so charming and unassuming that he doesn’t even realize how blindingly intelligent he is.
00:01:54 ►
And, of course, that intimidates me.
00:01:56 ►
Now, in this conversation with Terrence McKenna at Esalen in 1985, if you listen closely and have read Schism, Ralph’s latest book, I think you’ll see
00:02:07 ►
that way back in 1984 he was already laying the foundation for it. Now, as to the recording I’m
00:02:14 ►
about to play for us, I think it’s a great example of the freewheeling camaraderie that existed
00:02:20 ►
between Terrence and Ralph, and it also shows how laid-back and relaxed the workshops were at Esalen, and still are, I assume. Around 18 minutes
00:02:31 ►
into this talk, Terence asked Ralph about the possibility of modeling psychological
00:02:37 ►
states or social states, and that is where they really grabbed my attention.
00:02:42 ►
You see, Ralph’s book, Schism, does just that.
00:02:46 ►
The full title of this book is, and are you ready for this?
00:02:51 ►
Schism, The Madness of Crowds, Toxicity of Social Media,
00:02:56 ►
Social Polarization, and Political Violence, a Cybernetic Approach.
00:03:02 ►
Now, try to keep that in the back of your mind
00:03:05 ►
as you listen to this conversation.
00:03:07 ►
And also be sure to remember
00:03:08 ►
that this conversation took place 38 years ago.
00:03:16 ►
Can I stick this on?
00:03:19 ►
For those of you who haven’t had the pleasure,
00:03:22 ►
this is Professor Ralph Abraham, mathematician,
00:03:28 ►
dynamicist, author of the Foundation of Mechanics, and rascal.
00:03:36 ►
Primarily known as a friend of Terence.
00:03:39 ►
God forbid.
00:03:44 ►
Well, we’re very glad that
00:03:46 ►
you’re here to bring order out
00:03:48 ►
of the chaos. Where am I?
00:03:51 ►
Well, that’s
00:03:52 ►
what we’re hoping you’re going to tell us.
00:03:58 ►
I was making something up
00:04:00 ►
here. Let me see. What was it?
00:04:02 ►
I have no idea.
00:04:04 ►
Anyway, what Ralph was saying to me before we came down
00:04:07 ►
here was that the that the fractals are interesting but beyond the fractals lie uh
00:04:14 ►
attractors basins of attraction hot bifurcations lorenz attractors these kinds of things, which are obviously more high-powered tools yet
00:04:30 ►
for the modeling of the ubiquitous complexity that is the foundation experience of being.
00:04:40 ►
So we’re going to try and talk about this a little bit.
00:04:43 ►
I don’t fully understand Ralph.
00:04:46 ►
No one.
00:04:49 ►
So don’t feel lost.
00:04:51 ►
But I will try to interview him and we’ll carry it along a while.
00:04:56 ►
Now that’s a very bad start.
00:04:59 ►
Because you have, and everyone has, I firmly believe,
00:05:02 ►
the excellent chance, the outstanding chance,
00:05:06 ►
to understand everything, because it’s really simple.
00:05:12 ►
Well, I hope you make it simple.
00:05:15 ►
Well, we shall see.
00:05:17 ►
But I think that you more or less have to listen with the idea that this is easy, and then it’s easy.
00:05:23 ►
And it is easy.
00:05:24 ►
Ralph has written two books
00:05:26 ►
or now three books
00:05:28 ►
that are the greatest breakthrough
00:05:31 ►
in visual and the teaching of mathematics
00:05:33 ►
in probably centuries,
00:05:36 ►
maybe since Euclid,
00:05:37 ►
because he and an artist named Christopher Shaw,
00:05:41 ►
who has the amazing psychedelic ability to portray and draw perfectly
00:05:48 ►
these extremely complex objects undergoing transformation,
00:05:53 ►
they produced three books.
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Each book has an attendant piece of software
00:05:57 ►
that allows you to actually have an experimental dynamics laboratory on your home computer
00:06:03 ►
and draws beautiful figures.
00:06:06 ►
And so if anyone can make it simple, you can, Ralph.
00:06:11 ►
Why don’t you review from the point of view of people who are interested in what this means
00:06:16 ►
about modeling, neurology, psychology, and nature,
00:06:21 ►
what dynamics is it about and what its promise is.
00:06:25 ►
Yes. Excellent. Well, we could start with, you know, a few moments ago in the history
00:06:34 ►
of consciousness, about 6,000 years ago, when there was an event very similar in its
00:06:42 ►
import and the style of its presentation to
00:06:46 ►
what is happening now, at least in my personal view.
00:06:51 ►
And that was the discovery of the wheel.
00:06:57 ►
Now the wheel first presented itself in human consciousness.
00:07:01 ►
We don’t know when. There are, for example, cave paintings of 10,000 BC
00:07:08 ►
and Altamira, where you see the phases of the moon. So obviously, if they’d figured out the
00:07:14 ►
phases of the moon, which is a sufficiently challenging problem, that every time we look
00:07:19 ►
at the moon, we are reminded that we don’t really understand which way it’s going, which way
00:07:25 ►
the shadow’s going, what is happening next. Is it waxing or waning? Oh, I forget. Some people have
00:07:30 ►
got this down, but everybody knows that it’s not all that easy. And when it was figured out,
00:07:35 ►
there must have been in consciousness the idea of cycle. That is to say, the period, the periodic recurrence of a sequence of
00:07:47 ►
different states, always in the same period of time. This is a revelation,
00:07:55 ►
discovery of the wheel on the cognitive level, as a cognitive strategy. It was
00:08:00 ►
significantly later, horrendously later, when a real wheel first appeared.
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And apparently the first one was the pottery wheel.
00:08:11 ►
That’s nice.
00:08:13 ►
It was only a few years after the pottery wheel
00:08:16 ►
that the war chariot wheel arrived on the scene.
00:08:19 ►
So it’s nice that the pottery wheel was first.
00:08:22 ►
Now, the fractals.
00:08:23 ►
You’ve seen the pictures of a certain kind of fractal
00:08:26 ►
called the Mandelbrot set or the Julia set.
00:08:29 ►
And that is as the pottery wheel is to the cognitive wheel.
00:08:36 ►
It is a single episode,
00:08:39 ►
a single application of a certain abstract concept.
00:08:43 ►
So probably the important event,
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most important event,
00:08:48 ►
is the emergence of a new concept
00:08:50 ►
as fundamental and basic as wheel or cycle
00:08:54 ►
into consciousness,
00:08:55 ►
which probably requires
00:08:56 ►
that the evolution of the brain and mind
00:09:00 ►
themselves reach a certain level
00:09:04 ►
that is able to manifest the complexity of
00:09:08 ►
cycle, you see, before it could come in.
00:09:12 ►
Otherwise, it came in before, wherever it did, if, in fact, we are evolving, which some
00:09:17 ►
people, of course, including myself, seriously question.
00:09:22 ►
However, let us accept the current paradigm. It is evolving. It started from nowhere. At some point our
00:09:29 ►
consciousness expanded from a steady state.
00:09:33 ►
Like in Greek philosophy, all
00:09:36 ►
concepts are nouns, basically. They’re these static things. They don’t
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change in time. That dynamic process was not, you know, a concept that
00:09:46 ►
fit into their technology or philosophy at that time very well. So the ideas, idea of
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Plato is a slide collection, not a movie collection, okay? So when the wheel came in cycle,
00:10:06 ►
as far as cycle of different states in time,
00:10:08 ►
such as the phases of the moon,
00:10:10 ►
that represents a certain pattern in space and time.
00:10:15 ►
It’s a very complicated kind of thing,
00:10:17 ►
and the brain has to be wired up
00:10:18 ►
sufficiently complicated structure itself
00:10:21 ►
to engulf or present,
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portray such a notion as pattern
00:10:28 ►
in space and time. So the first thing past the platonic idea would be a movie, a whole movie
00:10:36 ►
gets named by a word. So like this movie, you know, then we give it a name and maybe that name
00:10:41 ►
becomes a noun. But when experienced, it would have to be experienced over some interval of time,
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which could be terrifically compressed by playing the movie real fast.
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So the pottery wheel in Mesopotamia, up north a little bit,
00:10:59 ►
the war chariot wheel of the Hurrians,
00:11:01 ►
who then used it to come down and conquer,
00:11:03 ►
obliterating the pottery wheel and so on, were different examples of the same abstract concept.
00:11:09 ►
This is just a little reconstruction of the history of science, which is very well done
00:11:14 ►
in different books by masters of the history of science who spent many years studying enormous
00:11:19 ►
quantity of archaeological data in order to reconstruct this little story.
00:11:24 ►
of archaeological data in order to reconstruct this little story.
00:11:29 ►
Now, what I’m saying is that now something else similar to that is happening, which is the biggest one since that one,
00:11:32 ►
as far as this kind of catastrophic growth in consciousness
00:11:37 ►
and cognitive ability and modeling strategy and so on is concerned.
00:11:41 ►
And I am not speaking about the computer revolution,
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which is itself a thing of such a magnitude, I do believe,
00:11:49 ►
but I’m speaking of something else,
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and that is simply the emergence into consciousness
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of a model, a cognitive strategy, a way of thinking
00:11:57 ►
that our brains are now okay to manage,
00:12:01 ►
and that is the chaotic attractor,
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all of which have been, as far as their explicit
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structure is concerned, have been studied. So that one, the wheel, this one, the chaotic attractor,
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I’m sure a different name will emerge in the long run, and it’s a strategy, a capability,
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it’s a cognitive level tool, in other words other words an abstraction and it has
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its various manifestations they all have this have been studied with computers
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which is a kind of a microscope functions as a microscope for the study
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of space-time pattern because it has the capability of enormous compression of
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the sometimes very expanded scale of space-time pattern into a swallowable,
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conceivable, manageable form, which is not compressed as much as a name. This is a
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Ressler attractor. This is a Shaw attractor. It’s not compressed that far. you get a kind of a picture, which is a movie, like an object,
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like a galaxy, and there is a little spacecraft or satellite which is whizzing around it.
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Then it has a static structure, and it has a dynamic structure, and that is the thing.
00:13:19 ►
That is the thing portrayed by high-speed special-purpose computer graphic device, which is a kind of a microscope
00:13:26 ►
for these dynamical concepts. So it is not like, well, it’s like a microscope that it is,
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the computer used to study the thing is of not the same interest as the thing itself. On the
00:13:42 ►
other hand, you can’t see it. You actually cannot see these patterns without a computer.
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Without the computer revolution, this might never have come in,
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as clay was necessary for the pottery wheel to evolve.
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So this is silicon. That was clay.
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And the object is this abstract thing,
00:14:00 ►
which has special cases.
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So these fractals that you’ve seen on the cover of the Scientific America and the Julia set, the Mandelbrot set, and so on, these are special cases. All of the
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chaotic attractors studied with this macroscope of computer graphics have fractal structure.
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So fractal structure is just one of the interesting properties,
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that you take a little teeny piece and you zoom in on it
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so it fills the whole picture on the computer screen,
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and you then have exactly what you had before,
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which still has this infinite regress of reentrant form, and so on.
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Although, isn’t it true, Ralph, that the point that this Scientific American article was
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making, that was choosing different points of entry into the Mantelbrot set, there seemed
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to be tremendous variety in what you saw?
00:14:56 ►
Well, we’re talking about the most complicated forms that have ever come into consciousness
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on abstract conceptual level.
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We see more complicated forms in everyday life, every day.
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So the expansion of consciousness so as to encompass the simple and familiar objects,
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these objects having sufficient complexity to actually serve us as conceptual models for things in everyday life,
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such as difficulty in our relationships, jealousy, emotional things, conflict between nations,
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progress of the arms race, and so on. The emergence, the availability of these strategies
00:15:40 ►
enormously empowers us to deal with ordinary life. So looking back again at the wheel,
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though we have 6,500 years or something of experience with this concept, there are still
00:15:51 ►
people on the planet today who are discovering the wheel for the first time. And in several
00:15:56 ►
thousand years, maybe there will still be people discovering that. But meanwhile, some of us are
00:16:01 ►
discovering these chaotic states.
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The discovery of, you know, the understanding, grokking this and feeling familiar with these models for chaotic states,
00:16:19 ►
then makes them feel as unexciting, as unthreatening, as periodic phenomena.
00:16:24 ►
So you look at your checkbook, you awake in the dream of paranoia at 3 a.m., and you think, I’m not going to have the money to pay the bills.
00:16:27 ►
And, of course, that’s true.
00:16:28 ►
You do the arithmetic over and over again.
00:16:30 ►
You’re not going to have the money to pay the bills,
00:16:32 ►
and it’s going to be terribly embarrassing,
00:16:34 ►
and everything is going to go to pieces.
00:16:36 ►
But then in the morning when the sun comes up,
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you realize that the salary check is going to be automatically deposited
00:16:41 ►
on the first of the month while doing the arithmetic in the middle of the night.
00:16:44 ►
You forgot that.
00:16:49 ►
So if it was, as seen in the small, this downward trend,
00:16:54 ►
when projected indefinitely, would indeed be a catastrophe. But once we realize it’s cyclical,
00:16:57 ►
it’s no longer threatening anymore. Of course, it’s going down now. Then it’ll be going up,
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then it’ll be going down. It always goes up and down. It stays within limits. So the important thing for the removal of anxiety from the observation of our surroundings
00:17:05 ►
is that it stays within limits.
00:17:08 ►
All of the chaotic attractors,
00:17:09 ►
which provide amazingly good
00:17:12 ►
models for historical
00:17:14 ►
data, like earthquakes,
00:17:16 ►
variation of the magnetic field
00:17:18 ►
of the Earth, the weather, sunspots,
00:17:20 ►
things like this, things that seem
00:17:22 ►
so unpredictable.
00:17:24 ►
Who would? There are good models for that.
00:17:26 ►
And yet all of these, almost all, are bounded. They go ta-da, ta-da, ta-da, ta-da. And when they get to there,
00:17:34 ►
they always turn around and go back. When they get to there, it’s always different times. We no longer have
00:17:38 ►
the repetition, the recurrence within the same time. And it is, in fact, in the context of chaotic attractors,
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the time between the repetition
00:17:49 ►
of a given state,
00:17:51 ►
which is unpredictable.
00:17:52 ►
That varies.
00:17:53 ►
That’s what appears to be random.
00:17:54 ►
So we can’t predict that time exactly,
00:17:56 ►
but it always does, you know,
00:17:57 ►
it follows around this general pattern.
00:18:00 ►
That is the nature of the thing.
00:18:01 ►
So, this is,
00:18:03 ►
is this too complicated?
00:18:05 ►
Of course, we’re talking about this thing.
00:18:07 ►
We don’t know what it is, but I’m saying it’s in the can.
00:18:10 ►
Worry not.
00:18:13 ►
Well, let me see here.
00:18:15 ►
A bunch of different things.
00:18:16 ►
First of all, what you’re implying is that time has this characteristic of self-embeddedness.
00:18:25 ►
We have a pattern in space and time.
00:18:29 ►
And if you try to slice this pattern by time alone,
00:18:32 ►
then you will not see the pattern.
00:18:34 ►
Right.
00:18:35 ►
So it’s necessary to have the computer.
00:18:38 ►
This branch of mathematics could not exist without the computer,
00:18:42 ►
but it has nothing to do with the computer fundamentally.
00:18:46 ►
The computer is simply a tool which is allowing it to be seen.
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Yes, my notion has been that historical phenomena
00:18:55 ►
can be subject to this kind of analysis
00:18:59 ►
from a very fine level to a very gross level.
00:19:05 ►
In other words, if you look at the entire history of the universe,
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the major tendency is toward increase of complexification.
00:19:16 ►
But in any subset of time within the life of the universe,
00:19:21 ►
that movement toward complexification will be seen upon magnification
00:19:26 ►
to be a series of starts and stops or up and down movements. And you see this, this is
00:19:35 ►
why observations like every day is somewhat like every other day is a kind of fractal
00:19:43 ►
observation. It’s also true that every year is very much like every other year.
00:19:48 ►
Nevertheless, over longer and longer periods of time, more and more bizarre patterns emerge
00:19:56 ►
out of the sameness and self-similarity of the situation until when you look at all of history, you see there is a larger design that is not
00:20:06 ►
contained in the incremental portions of it that you might look at. But that’s true at every level.
00:20:15 ►
At every level, there is a way of looking at it which discerns its uniqueness, and there is also
00:20:22 ►
a way of looking at it which sees it as not only very
00:20:26 ►
incrementally different from states
00:20:28 ►
which precede and follow it
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and this is
00:20:32 ►
not been included
00:20:34 ►
in western sciences
00:20:36 ►
definitions of time
00:20:38 ►
time has been simply a smooth
00:20:40 ►
function and duration
00:20:42 ►
was the notion
00:20:44 ►
that Newton and 19th century science was using when
00:20:48 ►
thinking about time but duration is not sufficient Einstein showed that you have to view space and
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time as two aspects of a single medium that can be deformed by forces in the universe rather than the time of some kind of an abstraction.
00:21:08 ►
How much do you think that these kinds of things can be applied to modeling psychological
00:21:14 ►
states or social states?
00:21:16 ►
Why don’t you talk a little about the terrorism problem and modeling?
00:21:22 ►
Because this is something that people can grasp, because it’s in the
00:21:26 ►
societal dimension that we share.
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Everything is.
00:21:35 ►
Well, the progress of understanding in the history of consciousness is a very mysterious process. We see that after
00:21:49 ►
some time everybody understands, and some centuries before nobody understood the certain thing,
00:21:57 ►
that the understanding seems to grow. That is good. And there are strategies when we struggle with a problem or try to do crossword puzzle or
00:22:07 ►
play chess or something, we feel we have little familiar tricks that we use that, of course,
00:22:13 ►
computer scientists try to extract for the use by machine in artificial intelligence,
00:22:19 ►
unsuccessfully most of the time. But nevertheless, we seem to know what we’re doing when we come across a problem. For the understanding of complex systems, like several people, a committee,
00:22:31 ►
a large company, country, planetary civilization, ecosystem, the biosphere, and so on,
00:22:40 ►
the main strategy, it seems, that has been used to understand these things is modeling.
00:22:44 ►
The main strategy, it seems, that has been used to understand these things is modeling.
00:22:51 ►
That means that you are very satisfied to throw out all the most complicating factors.
00:22:54 ►
You try to retain the most essential features.
00:23:00 ►
That means that you give up any hope of making a real model which is very much like the actual system.
00:23:05 ►
And you make some highly oversimplified thing and try to think about that.
00:23:10 ►
What if we forget the personalities of all the people, their genders, their ages, and so on. Just consider this committee to be an organism itself, or something like that. These tricks
00:23:14 ►
of thinking and solving problems, cognitive strategies, I don’t know. People probably study
00:23:19 ►
that. But I think that a central tool, one of many, but one which has been extremely important in the study of complex systems,
00:23:27 ►
is model building.
00:23:29 ►
In the model building area, so this word mechanics,
00:23:32 ►
which became a technical term of mathematical physics and mathematics,
00:23:37 ►
originally at the time of Thales,
00:23:40 ►
at the beginning of the scientific trip that we belong to,
00:23:44 ►
that we all live in the middle of.
00:23:46 ►
Mechanics meant model building,
00:23:49 ►
including carpentry and stuff like that.
00:23:51 ►
What you do, you have a pivot,
00:23:53 ►
and you put a little ball there,
00:23:56 ►
and then you have a wire and a smaller ball,
00:23:58 ►
and here’s the Earth is going,
00:23:59 ►
and then you add this other little ring around here,
00:24:02 ►
and then that’s the Moon,
00:24:02 ►
and finally you can get a good understanding of the phases of the Moon, also the eclipses of the sun and moon, and so on.
00:24:08 ►
This kind of model building. We can view the whole of mathematics
00:24:12 ►
erroneously from the point of view of most living mathematicians as one of many strategies of model
00:24:20 ►
building, which is one of many strategies of understanding or dealing with complex phenomena.
00:24:28 ►
So mathematical modeling is a strategy for understanding things.
00:24:37 ►
Of all the mathematical strategies for modeling things that emerge, this one that started
00:24:41 ►
by Newton, particularly to understand gravitation in the solar system, turned out to be a real good one, because it has been used to model
00:24:49 ►
practically everything. And almost all the currently used models in mathematical physics,
00:24:56 ►
applied physics, mathematics, applied mathematics, all these things, biology, physiology, mathematical
00:25:02 ►
modeling, simulation of social systems, all this stuff is all one idea
00:25:07 ►
Newton’s strategy for building models
00:25:09 ►
greatest mechanic in history
00:25:11 ►
and so
00:25:15 ►
yes
00:25:16 ►
and so if you say
00:25:18 ►
let us make a model for terrorism
00:25:20 ►
what about this I’m having the problem of jealousy
00:25:23 ►
with my partner or whatever. Yes,
00:25:25 ►
you could take Newton’s strategy and make a model for it and it would help understand it to some
00:25:32 ►
extent. So particularly, for example, to make a model of terrorism seems like it could be good
00:25:41 ►
because here’s the situation where, like the bank account, you wake up in the middle of the night,
00:25:46 ►
and in the short run you’re seeing a situation where if this tendency were to continue, it would totally be disastrous.
00:25:54 ►
We see that terrorism is expanding more than exponentially.
00:26:00 ►
It is growing faster than AIDS or faster than syphilis did in the 17th century and so on.
00:26:08 ►
So this is very frightening.
00:26:10 ►
So maybe having a model that was relatively predictive, reasonably qualitatively predictive in the short run,
00:26:20 ►
that gave us some glimpse of what the long-run behavior,
00:26:22 ►
it might be illuminating in some way and either help to deal with the problem or something.
00:26:26 ►
So I have tried to apply the technique of Newton to terrorism,
00:26:32 ►
to make a mathematical model that would work something like this.
00:26:36 ►
It’s a model, right?
00:26:37 ►
So it’s not to be thought of as being related in any way to the real thing,
00:26:40 ►
other than how the other models are related to their real things.
00:26:44 ►
So on the computer screen, you can move your finger around thing other than how the other models are related to their real things.
00:26:45 ►
So on the computer screen you can move your finger around and put it down and create an
00:26:52 ►
instant of terrorism, an act, a bomb somewhere in the planet and then sit back and see what
00:26:58 ►
would happen according to hypotheses that are in the model, then you would see this
00:27:02 ►
red stain spreading across the screen,
00:27:06 ►
behaving like gonorrhea, behaving like an infectious disease, and following the model
00:27:10 ►
rules, which have been the most successful ones for modeling the spread of infectious disease,
00:27:16 ►
which were, as a matter of fact, introduced by Ronald Fisher in 1930. The first model
00:27:22 ►
in mathematical biology, the beginning of mathematical biology,
00:27:26 ►
this guy tried to make a mathematical model and it turned out to have a very good relationship
00:27:31 ►
with experimental data for the phenomenon of the spread of mutation in a population of fruit flies.
00:27:38 ►
You’ve got these petri dishes full of fruit flies and then you put a mutant there and then they have
00:27:43 ►
sex with each other, whatever happens in the successive generations
00:27:46 ►
and it’s visible because they have four legs
00:27:48 ►
instead of two or whatever it is
00:27:49 ►
and then there was already a lot of data
00:27:52 ►
because people were very concerned
00:27:54 ►
at that time with Lamarckian
00:27:56 ►
versus Darwinian evolution and stuff like that
00:27:59 ►
so they had a lot of data
00:28:00 ►
and here he made this mathematical model
00:28:02 ►
he was a geneticist not a, and it was really good.
00:28:06 ►
So it’s that same kind of model applied to terrorism
00:28:09 ►
under the very simple idea that it’s some kind of catching disease.
00:28:15 ►
That is to say, a person, an example of the modeling process.
00:28:18 ►
You say, well, how will I make a model for this?
00:28:20 ►
Let’s see, maybe there’s a lot of different kind of people.
00:28:22 ►
I know that.
00:28:24 ►
But I know some people who could never be a
00:28:26 ►
terrorist. And then these other people, they are
00:28:28 ►
obviously doing it. So could
00:28:30 ►
this one actually turn into that one, or do you have
00:28:32 ►
to be born of that? Well, I don’t know. I mean,
00:28:33 ►
we don’t know these things, but we’ll just guess something, because
00:28:35 ►
you are at liberty to do that when making models.
00:28:38 ►
Say, well, I’m going to guess that.
00:28:40 ►
I’m just going to try this out. This would be model number
00:28:41 ►
70 in a list of 10,000.
00:28:44 ►
Suppose that a person can be an okay person and then get sick in the middle of life at any time,
00:28:50 ►
catch this disease and become a terrorist.
00:28:52 ►
It could also happen to you or me.
00:28:54 ►
What could be one way of catching this disease?
00:28:56 ►
Well, to have contact with a sick person.
00:28:58 ►
Well, how would you know that person was sick?
00:28:59 ►
You would see them doing it, right?
00:29:00 ►
Doing well maybe to you.
00:29:02 ►
So if you get caught and hung on the parrot perch
00:29:05 ►
in Brazil for two weeks and then released into the streets or something, maybe you had become
00:29:08 ►
a terrorist. I mean, I don’t know. It’s just an idea for the model. Very much like mutation.
00:29:14 ►
Then could such a person, already a terrorist, then he’s employed by the secret police and get
00:29:19 ►
the great deal of pleasure out of doing the unthinkable, which I couldn’t even believe
00:29:23 ►
that it became my lot in life to have to read immense number of stories about actually what they do in
00:29:29 ►
order to carry on this project. But then the person having been tortured becomes a sick
00:29:37 ►
person and has the capability of being… Then such sick person might never become well
00:29:43 ►
only by dying and replacing this one with a new one
00:29:46 ►
that had not had the experience.
00:29:48 ►
But people do die and they are replaced,
00:29:50 ►
so it could be that the disease could stabilize,
00:29:53 ►
let’s say, 60% of the population or something,
00:29:56 ►
even under this hypothesis.
00:29:57 ►
Well, would it or wouldn’t it?
00:29:58 ►
Well, I’ve described all the hypotheses,
00:30:01 ►
and you can look up a Ronald Fisher model
00:30:03 ►
in the literature of 1930
00:30:04 ►
and plug this into the computer and run it
00:30:06 ►
and actually see the red stain spreading from this one spot in the Middle East, or say in Germany,
00:30:13 ►
and then spreading until it fills a large part of the European continent.
00:30:18 ►
And then because of the institutionalization of a new air route from Southern Europe to the Middle East,
00:30:25 ►
that a red stain begins to grow on a new continent,
00:30:29 ►
and then some other people flew from Germany to South America,
00:30:31 ►
and so then you see it growing.
00:30:33 ►
So you compare this movie with the actual records,
00:30:36 ►
very dutifully kept by Amnesty International,
00:30:39 ►
and see if it’s a good model or no.
00:30:41 ►
Actually, it’s a terrible model. What will we change?
00:30:44 ►
Well, let’s suppose that people can get better by having an
00:30:47 ►
insulin massage. And we’ll try that out in the model.
00:30:51 ►
In this way, you come to understand the phenomenon by playing with the
00:30:55 ►
regulatory models. That’s the idea of how
00:30:59 ►
this kind of modeling is applied to a given situation. You don’t take the
00:31:03 ►
models too seriously.
00:31:07 ►
That seems natural to us.
00:31:09 ►
But you see these mathematical physicists take their models so seriously that they actually confuse the model with the target system,
00:31:14 ►
with the observational universe.
00:31:17 ►
We don’t do that.
00:31:18 ►
So would it be fair to say, Ralph,
00:31:20 ►
that the first modeling system that had real power
00:31:24 ►
was this Newtonian modeling system,
00:31:27 ►
and that probability theory is something else which does another kind of modeling,
00:31:35 ►
but that what you’re talking about is third-generation modeling systems,
00:31:39 ►
which are beyond probability theory, or are they a subset of it?
00:31:43 ►
probability theory, or are they a subset of it?
00:31:51 ►
Well, I think that Newton’s method, carried on for a while, includes all these things,
00:31:56 ►
includes probability theory and quantum theory, and these are all special cases of the same strategy.
00:31:57 ►
There is no big distinction.
00:32:01 ►
Of course, it happened for a while briefly in the history of science that many people
00:32:04 ►
thought that probability theory was an alternate
00:32:06 ►
scheme but one of the
00:32:08 ►
interesting things happening recently in the context
00:32:10 ►
of these fractal attractors
00:32:13 ►
is
00:32:14 ►
that it is understood how the
00:32:16 ►
probability theory is itself
00:32:18 ►
an attractor of another dynamical system
00:32:20 ►
so it’s all one
00:32:22 ►
it’s all one strategy
00:32:24 ►
although it seems so various in its presentation
00:32:26 ►
by different scientists in different fields and so on. It’s basically a single strategy,
00:32:32 ►
which is as simple as a wheel. You could think of it like that. I mean, there’s wheels in
00:32:37 ►
different sizes and shapes and so on.
00:32:40 ►
What do you think of, or what do you make of the parallelism between the computer-generated graphics and hallucination as we experience it on psychedelic drugs?
00:32:52 ►
Do you think that’s a trivial or a profound connection?
00:32:56 ►
Well, Einstein was saying there in his address to European Scientific Society in the 30s, that he had had this experience personally
00:33:07 ►
where he had learned some abstract mathematical system
00:33:10 ►
which had been exclusively the creation of the world of thought.
00:33:15 ►
And the people went into the closet and thought,
00:33:17 ►
and they thought of this stuff.
00:33:18 ►
And then he found that it was the perfect fit
00:33:20 ►
with some observed phenomenon,
00:33:23 ►
very important phenomenon to some people,
00:33:26 ►
like the advance of the perihelion of Mercury or something. To him, that this was astounding.
00:33:32 ►
You see, that this purely mathematical structure and this physical system, a large part of
00:33:38 ►
the observable universe, in fact, happened to fit like hand in glove. How could that
00:33:43 ►
be? This caused awe in him. It was actually the basis of his whole religious feeling,
00:33:49 ►
as he expressed in many writers.
00:33:51 ►
And I think many of us, when you see something from that planet,
00:33:55 ►
way over another galaxy, something from this planet that are identical,
00:33:59 ►
it really is awesome.
00:34:01 ►
Well, why does mathematics work so well to describe nature?
00:34:07 ►
Well, there you’re asking for a private speculation.
00:34:12 ►
Lay on the couch.
00:34:13 ►
Because of course we don’t know.
00:34:17 ►
Well, it seems when you do mathematical research that you’re exploring some distant landscape
00:34:25 ►
and that every time you travel there
00:34:28 ►
you see the same things
00:34:29 ►
and other people have traveled there
00:34:30 ►
and marked out different parts
00:34:31 ►
and you see their footsteps and so on.
00:34:33 ►
Now you have exactly the same experience
00:34:36 ►
with psychedelics
00:34:36 ►
if you take multiple trips
00:34:38 ►
and pay good attention to them and so on
00:34:42 ►
then you feel that you’re visiting this landscape
00:34:44 ►
that other people have been there,
00:34:45 ►
that what you are experiencing is more or less constant
00:34:51 ►
or resonant with the expression that somebody else made about their trip
00:34:55 ►
in the distant mystical literature of the Far East or something like that.
00:35:01 ►
And likewise in science, you look through the microscope,
00:35:04 ►
if you’re the first person to look
00:35:05 ►
like who is that hacker or something
00:35:07 ►
that’s very revolutionary
00:35:09 ►
it’s a hell of an experience
00:35:10 ►
to look through an instrument
00:35:11 ►
to be the first one to land on this planet
00:35:14 ►
are you kidding?
00:35:16 ►
so they certainly
00:35:18 ►
you know these are similar things
00:35:19 ►
but you ask why
00:35:20 ►
would that landscape
00:35:23 ►
visited by mathematicians and that landscape visited by the psychedelic
00:35:28 ►
pioneers, and that landscape visited by the early scientists, and so on, actually be coherent?
00:35:36 ►
That they are resonant? What is the reason for that? They must be part of the same thing,
00:35:42 ►
right? Or there’s only one thing and it looks almost the same from
00:35:45 ►
every angle or what. So this mathematics, what is brought back from a mathematical journey
00:35:51 ►
is that which fits in this radio receiver, more or less. When we come back from a psychedelic
00:35:59 ►
journey, we actually cannot remember in this reality all of the detail of the space-time patterns experienced during that journey.
00:36:10 ►
When we shut down the microscope and go back to the laboratory, it doesn’t matter, you draw, you photograph your film or whatever,
00:36:18 ►
it’s just a very low dimensional representation of a little fragment of the thing which you can bring back. So what is
00:36:27 ►
experienced, remembered, and brought into collective consciousness and ordinary reality by these
00:36:34 ►
journeys is very determined by the structure of this radio receiver. That’s one thing. There is
00:36:42 ►
an enormous filtration that the ultimate reality is perhaps one wave,
00:36:47 ►
and our experience of it is some kind of filtration. And so what is filtered through
00:36:53 ►
this filter seems very similar no matter what we’re looking at. In this sense, at least,
00:36:58 ►
we are looking at the structure of the filter, that that mathematical reality, that psychedelic reality, that ultimate reality,
00:37:07 ►
which we view through the filter, is only functioning as a projection lamp.
00:37:14 ►
Or it could be that in the ultimate reality itself there is a terrific resonance, and it is all one.
00:37:21 ►
It certainly seems like that to any person who has made several of these journeys,
00:37:26 ►
different types of journeys, I would say,
00:37:28 ►
on personal experience,
00:37:29 ►
seems very much the same place.
00:37:32 ►
It’s even the same place.
00:37:35 ►
And there could be the resonance between,
00:37:39 ►
this is what Rupert Sheldrake is talking about
00:37:42 ►
under the name Morphic Resonance,
00:37:43 ►
that the one way of reality, the thing, the cosmos itself,
00:37:50 ►
and the filter, our consciousness, our capability to perceive it,
00:37:55 ►
even they are pretty much identical.
00:37:58 ►
So it’s not that we’re seeing some really foreign object projected through this filter.
00:38:03 ►
It actually is a perfect fit. It feels like that. I don’t know that it would make sense, you know, because why would
00:38:09 ►
this radio receiver evolve in a hundred million years into a shape totally different from some
00:38:17 ►
objective reality if there was one? So I think it’s all very harmonious, and to make a long story short,
00:38:29 ►
what you are seeing in any of these journeys is actually it.
00:38:35 ►
So, Ralph, is the way to see different parts of it, or to reconfigure the receiver, is to evolve languages of description?
00:38:42 ►
Languages and tools and models. I mean, cognitive
00:38:45 ►
language is but one of
00:38:48 ►
cognitive strategies.
00:38:50 ►
Well, by language, I mean I would consider
00:38:52 ►
a model of three-dimensional language.
00:38:55 ►
Now that’s really cheating,
00:38:56 ►
Terrence.
00:38:59 ►
To appropriate
00:39:00 ►
the whole of it as an
00:39:02 ►
expert linguist, you would like to…
00:39:04 ►
Well, that’s perfectly okay.
00:39:05 ►
I don’t care what you call it. My feeling is, though, that good models go beyond language as
00:39:14 ►
spoken, written, or experienced under the concept of language, just as we feel that our actual experience, let us say, in making love, in a great ski run, or in deep powder, or something like that, we have the feeling that no matter how this would be reduced to language, even in the sense of computer model or whatever, that the essence would basically be lost.
00:39:41 ►
would basically be lost.
00:39:44 ►
So… Do you believe that the nervous system
00:39:46 ►
is organized the way it is to process information?
00:39:51 ►
Absolutely.
00:39:51 ►
The information is organized that way?
00:39:53 ►
Yes.
00:39:54 ►
Well, I even suggested that the wheel came in
00:39:56 ►
after loops were built.
00:39:58 ►
You see, you have…
00:39:59 ►
This nervous system has a very interesting structure.
00:40:03 ►
Now, we don’t know that the neural network, for example,
00:40:07 ►
is actually essential for receiving the information.
00:40:11 ►
I mean, doesn’t it seem a bit odd
00:40:13 ►
that these ants are actually able to carry out their life
00:40:17 ►
with that ant brain they’ve got?
00:40:22 ►
So it could be that a single cell, fact i would say it’s certain a single cell has a living cell
00:40:30 ►
a biological cell has the complexity of the entire universe there is no reduction you know it doesn’t
00:40:39 ►
matter we have a lot of cells here basically the complexity of one cell is the same kind of thing as the entire universe. However, just to look at something, let us think of the neural net.
00:40:52 ►
Then in the neural net you have a whole bunch of things that are vaguely similar if you
00:40:57 ►
don’t look too closely, which is again the kind of thing we have to do in modeling or
00:41:02 ►
any cognitive strategy to try to understand the things. So we’ve got a bunch of, let us suppose, even though wrong, identical neurons are then connected
00:41:09 ►
in a network.
00:41:10 ►
Well, you look at the thing with a microscope and they’ve got all these stains and stuff
00:41:14 ►
and very good pictures and use computer graphics to reconstruct the pictures into a three-dimensional
00:41:18 ►
model which you can rotate in front of your eyes and understand and was projected on PBS
00:41:22 ►
last year, right? And then you see that there are clumps.
00:41:29 ►
And then there are clumps of clumps, and there’s clumps of clumps of clumps.
00:41:33 ►
And there’s a lot of communication between nearest neighbors,
00:41:36 ►
and then there’s pipes or telephone wires that goes from one clump of clumps
00:41:39 ►
to another one over a long distance.
00:41:40 ►
So it has this kind of hierarchical structure.
00:41:40 ►
over a long distance.
00:41:43 ►
So it has this kind of hierarchical structure.
00:41:49 ►
And that’s the same kind of structure that we see in everything else,
00:41:50 ►
in social organizations.
00:41:52 ►
I mean, there’s systems,
00:41:53 ►
there’s networks,
00:41:54 ►
there’s what’s it called,
00:41:56 ►
cybernetics, general systems theory,
00:41:58 ►
that people look at this
00:41:58 ►
and their whole technical language
00:42:00 ►
just consists of stuff like
00:42:01 ►
clumps of clumps and wires between
00:42:03 ►
and diagrams of, and that’s
00:42:05 ►
about as far as they got in understanding the thing.
00:42:08 ►
But on that level, everything in the
00:42:10 ►
phenomenal universe has the same structure.
00:42:12 ►
So, these mathematical
00:42:14 ►
models are very
00:42:16 ►
good tools for modeling these complex
00:42:18 ►
systems because they are
00:42:19 ►
microscopic working models
00:42:22 ►
of a complex… that is all. I mean, what else?
00:42:24 ►
Now, the fact that the behavior
00:42:26 ►
could be mapped in this way, that you
00:42:28 ►
can grok it very simply,
00:42:30 ►
that’s the miracle of the computer revolution
00:42:32 ►
plus the
00:42:33 ►
increase of our understanding, plus
00:42:36 ►
without a doubt a whole bunch of
00:42:38 ►
revelation, where people are receiving
00:42:40 ►
instructions from another
00:42:42 ►
planet or something that says
00:42:44 ►
connect it up in the following way, don’t ask.
00:42:48 ►
Yes, I mean, we’ve been talking about visible languages.
00:42:53 ►
What the computer does is it takes these descriptive equations
00:42:57 ►
of various curves and things and turns it into something beheld,
00:43:02 ►
which is much more so to the essence of what it is.
00:43:05 ►
Maybe we could think of the different cognitive strategies as ways of building a bridge
00:43:10 ►
from the complexity of experiential reality itself,
00:43:15 ►
all the way down to, and is quite a ways to, language.
00:43:21 ►
Is the film that you’re going to show today used through the macroscope,
00:43:26 ►
or is it a computer graphics reconstruction of a tractor?
00:43:31 ►
This is the use of computer graphics to explain, to portray,
00:43:38 ►
to visualize the structure of a chaotic attractor
00:43:41 ►
by showing not just what it looks like itself, the chaotic
00:43:47 ►
attractor, but also the invisible matrix around it, the womb, which gave it its form.
00:43:54 ►
So this is devoted to one attractor, which was the first one discovered in actuality,
00:44:00 ►
in the phenomenal universe, and through computer simulation in 1961
00:44:10 ►
that came up when somebody’s trying to model the weather to predict the weather
00:44:14 ►
and he made the model and ran it on the computer which was then newly available for this purpose
00:44:19 ►
and he obtained this it would be something like the accidental discovery of LSD.
00:44:27 ►
It was this revolution
00:44:28 ►
happened to this guy who,
00:44:30 ►
just by coincidence, happened to be
00:44:31 ►
perfectly trained for this task.
00:44:37 ►
That he had been
00:44:38 ►
a graduate student of the first mathematician
00:44:40 ►
to discover this kind of thing
00:44:42 ►
in mathematical models through the
00:44:44 ►
use of mentation alone. George D. Burkoff, chairman of mathematics at Harvard University in the
00:44:52 ►
early part of the century, the first famous American mathematician, and a follower of
00:44:57 ►
Poincaré, who sort of invented everything that makes this technology possible. Anyway,
00:45:02 ►
that first one, which now there’s a list, and they have names, so you could say
00:45:06 ►
it’s been bridged down into
00:45:07 ►
ordinary language. The Lorenz attractor,
00:45:10 ►
that’s the first one,
00:45:12 ►
- The
00:45:13 ►
Rüsseler attractor, that’s the next one,
00:45:15 ►
- The
00:45:17 ►
Shaw bagel, and so then there’s a growing list
00:45:20 ►
of these objects. It’s not
00:45:22 ►
like the wheel exactly. There is a family
00:45:24 ►
of them which are sort of
00:45:25 ►
sort of being wheel-like. They are chaotic attractor-like things. No, no, there is an
00:45:31 ►
infinite list. There’s an infinite list of catastrophes also. And at this point already,
00:45:38 ►
infinite list is known, some of which are almost indistinguishable from each other. So you might
00:45:44 ►
look for a new way to group them
00:45:46 ►
and then they would only be free.
00:45:47 ►
Anyway, of this lift, the one, the first one, the Lorenz attractor,
00:45:51 ►
its physical appearance is as an almost two-dimensional object
00:45:55 ►
in three-dimensional space.
00:45:57 ►
It is this sheet that goes around like two holes
00:46:00 ►
and it goes around and then it comes back in.
00:46:03 ►
Both of these come back in and and I’m glued onto the original
00:46:06 ►
sheet again. But each
00:46:08 ►
seat is a little thick, like cardboard.
00:46:10 ►
And when you look with the microscope,
00:46:12 ►
you see the infinite number of layers there,
00:46:13 ►
one of which is actually a little thick,
00:46:16 ►
like thinner cardboard, and if you look
00:46:18 ►
with a better microscope, you end so on.
00:46:20 ►
So it has that fractal structure in there.
00:46:22 ►
And the movie,
00:46:23 ►
which is not explained enough so that you can understand,
00:46:27 ►
but you will see, so I tell you now what you will be looking at,
00:46:30 ►
is first of all the experimental situation in which it came up,
00:46:35 ►
which is the simulation of a two-dimensional gas.
00:46:37 ►
You’ll see the molecules moving.
00:46:39 ►
The red ones are hotter, but unfortunately this is not the color version of the film.
00:46:43 ►
So they just appear a little brighter.
00:46:45 ►
There are hotter ones and cooler ones,
00:46:47 ►
and when the hotter one catches the cooler one,
00:46:49 ►
the cooler one gets hotter.
00:46:50 ►
All this is simulated.
00:46:52 ►
And then that’s on one side.
00:46:53 ►
On the other side, the mathematical model for it.
00:46:55 ►
So this is to give the idea of what modeling is about.
00:46:59 ►
There’s a target situation.
00:47:01 ►
Molecules of gas are moving around in the atmosphere,
00:47:03 ►
trying to model the weather, right?
00:47:05 ►
So when the sun goes down
00:47:07 ►
and the earth is still warm, it cooks
00:47:09 ►
this layer of atmosphere
00:47:11 ►
from below, and it boils.
00:47:14 ►
It actually boils. It simmers.
00:47:16 ►
So you get this, rises up,
00:47:18 ►
comes down, and these,
00:47:19 ►
like those simmering in the bottom of the pan there,
00:47:21 ►
these little rings where the water is coming up
00:47:23 ►
here and going down there, toroidal rings. And so you see that is happening in the fluid. The
00:47:28 ►
mathematical model of anything like that, that is over here. The relationship, the model is
00:47:33 ►
three-dimensional, and the actual situation is sort of infinite dimensional. And the relationship
00:47:38 ►
between, they try to explain. And then that’s just history, and then the study of the mathematical
00:47:44 ►
object, its fractal structure, and then the study of the mathematical object its fractal structure
00:47:45 ►
and then the revelation of the invisible matrix
00:47:48 ►
by a technique that took an enormous number of hours
00:47:50 ►
on a supercomputer
00:47:51 ►
at Brookhaven National Lab
00:47:53 ►
I need somebody who knows how to thread the projector
00:47:56 ►
yeah, why don’t we take a break
00:47:58 ►
and everybody can get water
00:48:00 ►
and recondition
00:48:01 ►
and we’ll get the projector threaded
00:48:04 ►
well thank you very much Ralph that was amazing We can get water and recondition and we’ll get this protective residue.
00:48:08 ►
Well, thank you very much, Ralph.
00:48:09 ►
That was amazing.
00:48:15 ►
Explain, if you can, what R is.
00:48:17 ►
Oh, R.
00:48:20 ►
R is just how much you’re cooking it.
00:48:24 ►
You mean how much energy is being put into the system? What is driving
00:48:26 ►
this system is the temperature
00:48:27 ►
difference basically between the top
00:48:29 ►
and the bottom.
00:48:32 ►
Yeah.
00:48:34 ►
And can R have any value
00:48:35 ►
from negative infinity
00:48:38 ►
to positive infinity?
00:48:39 ►
Yes.
00:48:41 ►
And these
00:48:42 ►
will continue to generate
00:48:46 ►
an infinite number of system responses
00:48:50 ►
to the shift in R?
00:48:51 ►
Well, understanding R is very important
00:48:54 ►
because it has to do with the basic idea
00:48:57 ►
of this modeling scheme.
00:48:59 ►
And there is sort of input and output.
00:49:03 ►
And input is R.
00:49:05 ►
You set R.
00:49:07 ►
Bruce was setting R when he made the film,
00:49:09 ►
and so he’d know where he set it.
00:49:11 ►
He put it in the upper left-hand corner,
00:49:13 ►
R equals so-and-so, and then it keeps changing.
00:49:15 ►
So you have to think of R as being a knob.
00:49:18 ►
There’s the R knob.
00:49:20 ►
And as far as what it corresponds to,
00:49:23 ►
as long as we’re interested primarily in the abstract something, it doesn’t matter what it corresponds to, as long as we’re interested primarily in the abstract something,
00:49:26 ►
it doesn’t matter what it corresponds to.
00:49:29 ►
In this particular modeling context,
00:49:32 ►
it meant the temperature difference between the hot earth and the freezing ionosphere.
00:49:39 ►
So when you turn the heat up under the pot, it simmers faster.
00:49:45 ►
So that’s a knob on the stove.
00:49:47 ►
You turn the knob, behavior changes.
00:49:49 ►
When you’re not turning the knob, the behavior is fixed.
00:49:54 ►
But what it’s fixed at is a movie.
00:49:56 ►
You saw it in the lower left corner.
00:49:58 ►
It was rolling counterclockwise, or it was rolling the other way.
00:50:04 ►
So it would roll this way, and then it would roll
00:50:06 ►
this way. When it shifted back and forth between rolls was some unpredictable kind of change of
00:50:13 ►
behavior. But the entire movie, that is one movie. When you don’t change the knob, when you do change
00:50:21 ►
the knob, the entire movie changes. It might be periodic, meaning three rolls left, one roll right,
00:50:28 ►
three rolls left, one roll right, three rolls…
00:50:31 ►
That’s periodic.
00:50:32 ►
Then this other thing, 42 to the left, one to the right, 12 to the…
00:50:35 ►
And that’s a random number generator, basically,
00:50:39 ►
although it’s a totally deterministic system in the sense of Newton.
00:50:43 ►
So R, that’s the knob,
00:50:46 ►
and the movie with this
00:50:48 ►
mast-shaped thing with a little satellite
00:50:52 ►
rolling around it, that movie is the output.
00:50:56 ►
Now, you might have a more complicated system
00:50:58 ►
where you’ve got a stove,
00:51:01 ►
then you’ve got the atmosphere over it,
00:51:03 ►
then you’ve got a plane in there, then you’ve got people looking out from the other planet, you’ve got all stove, then you’ve got the atmosphere over it, then you’ve got a plane in there,
00:51:05 ►
then you’ve got the people looking out from the other planet,
00:51:07 ►
you’ve got all these different things,
00:51:09 ►
where this one’s output
00:51:11 ►
turns this next one’s knob.
00:51:15 ►
You see, and that’s how this technology would be applied
00:51:18 ►
to the problem of modeling a complex situation.
00:51:21 ►
You’d have a bunch of modules.
00:51:23 ►
Each one would be this complicated. And then you would interconnect them in a certain way that this one is the
00:51:31 ►
controller for that one. Of course, this one might be the controller for the first one.
00:51:35 ►
And then you get a so-called hypercycle out of which is made models for the evolution of life in the first epochs of time called prebiotic biology.
00:51:47 ►
Yeah.
00:51:47 ►
So in this case, R, the important thing is that’s the knob.
00:51:53 ►
Now what would happen if you had two knobs, R1 and R2?
00:51:58 ►
And what is R2?
00:52:01 ►
Well, if R1 is the temperature temperature R2 might be the ambient
00:52:05 ►
electromagnetic field
00:52:06 ►
might be the degree of ionization of the gas
00:52:08 ►
might be the weight of the gas
00:52:11 ►
might be the size of the
00:52:13 ►
pressure or it might be anything else
00:52:15 ►
but controllable so you would have
00:52:17 ►
a knob for each
00:52:18 ►
and didn’t you as an experimentalist
00:52:21 ►
actually build
00:52:22 ►
machines
00:52:24 ►
this wasn’t all on the blackboard, was it not?
00:52:28 ►
Well, I built a machine to study the bifurcations with two knobs.
00:52:33 ►
And these two knobs were, this is in the category of,
00:52:37 ►
it’s called nonlinear oscillations,
00:52:39 ►
one of the main branches of experimental dynamics
00:52:43 ►
since its beginning with Galileo.
00:52:46 ►
It’s called nonlinear oscillation, so it
00:52:48 ►
means an oscillator, like here’s
00:52:49 ►
Foucault’s pendulum, a giant pendulum
00:52:51 ►
hanging from the ceiling, started moving,
00:52:53 ►
goes for years, reveals the
00:52:55 ►
rotation of the Earth on its axis or something.
00:52:58 ►
So now you take…
00:52:59 ►
You all understand what that refers to?
00:53:02 ►
The Foucault
00:53:03 ►
pendulum experiment is if you have a
00:53:05 ►
pendulum swinging and you
00:53:07 ►
set up little chalk
00:53:10 ►
pieces or something for it to knock
00:53:11 ►
over in a circle
00:53:13 ►
in 24 hours, it will
00:53:15 ►
have knocked them all over because
00:53:17 ►
the pendulum stays still but the earth
00:53:19 ►
is turning beneath it.
00:53:21 ►
Because of this sort of gyroscope trip.
00:53:24 ►
So now they’ve got a big one I’ve seen
00:53:26 ►
in the Museum of Science in, I forget
00:53:28 ►
what city, maybe Paris.
00:53:30 ►
It’s Chicago, that’s right.
00:53:35 ►
So science
00:53:36 ►
museums have to have a Foucault pendulum.
00:53:39 ►
Even New York.
00:53:41 ►
So now you take the
00:53:42 ►
Foucault pendulum and you hold it by the fulcrum,
00:53:44 ►
that’s that ring
00:53:45 ►
set in the ceiling that it hangs from, and instead of hanging from the ceiling, you hang
00:53:49 ►
it from your hand, and now you begin to wave it back and forth like this.
00:53:54 ►
So there’s two variables here.
00:53:55 ►
One is the so-called amplitude, that’s how far you move it before turning back, and the
00:54:00 ►
other is frequency, which means how many total round trips within the span of one minute.
00:54:07 ►
And then if you do this mechanically, then you have an actual gadget with two knobs,
00:54:12 ►
the frequency knob and the amplitude knob.
00:54:14 ►
So this is a classical experiment with Foucault’s pendulum, actually,
00:54:18 ►
that began around the turn of this century.
00:54:21 ►
And it’s still going on today that people do work with a real pendulum
00:54:26 ►
because they don’t trust computers or something. I went to Kyoto to visit the greatest experimental
00:54:33 ►
dynamicist since Galileo, whose name is Hayashi. And so he’s retirement age plus, you know,
00:54:42 ►
he’s in his middle 70s or something. And he’s not a doddering old man,
00:54:46 ►
but he’s pretty well slowed down.
00:54:48 ►
And I said, well, what are you doing now?
00:54:50 ►
So he knows what I’m doing.
00:54:51 ►
I send him my papers.
00:54:52 ►
So what are you doing now?
00:54:53 ►
He says, I’m building a pendulum.
00:54:58 ►
A real pendulum.
00:55:01 ►
So he had done the most amazing experiments
00:55:03 ►
with analog computers.
00:55:04 ►
In fact, invented, created the first amazing experiments with analog computers.
00:55:11 ►
In fact, invented, created the first analog computer for dynamics research during World War II, which, of course, nobody ever found out about until much later because it was secret,
00:55:15 ►
just like the ones in England and the ones in Germany.
00:55:20 ►
And this changing the frequency and the amplitude with which you oscillate and oscillate it,
00:55:29 ►
which is called a forced oscillation, causes it to do the most amazing things.
00:55:34 ►
Like, it’s oscillating like this.
00:55:37 ►
You’re pushing it, of course.
00:55:38 ►
Maybe it would, like, slow down.
00:55:40 ►
It’s got a lot of friction or something.
00:55:41 ►
It would slow down.
00:55:42 ►
So you keep, see, you move the fulcrum back and forth,
00:55:47 ►
and then the thing keeps oscillating instead of running down.
00:55:50 ►
And now, as you push it back and forth,
00:55:52 ►
you gradually slow down the speed at which you’re doing it.
00:55:54 ►
Suddenly, it gets a big, with no apparent cause,
00:55:58 ►
a huge increase in the amplitude of the resultant swing,
00:56:01 ►
which Dufing discovered in 1908,
00:56:04 ►
was the beginning of hysteresis,
00:56:06 ►
this famous concept of mechanical engineering, which also goes under the name of memory,
00:56:12 ►
as in the memory of the fender of your car when you bend it, and then you can never unbend
00:56:17 ►
it completely, that kind of memory.
00:56:21 ►
But which has actually been applied to this very thing, hysteresis or memory,
00:56:26 ►
mechanical memory, discovered by Dufy in 1908, applied to this memory, mathematical model
00:56:33 ►
for transfer from short-term to long-term memory by Christopher Newman in the 1960s.
00:56:40 ►
So it’s a classic, forced oscillation.
00:56:44 ►
When you change the frequency and the amplitude,
00:56:46 ►
what do you see? So what I built was a machine in which a fluid is vibrated, and you have
00:56:51 ►
these two knobs, and then you look, you know, like what happens using the human pattern
00:56:57 ►
recognition capability as the observing tool. Because what’s going on in this layer of fluid
00:57:02 ►
as it’s vibrating is a movement that’s so complicated there would be no other
00:57:05 ►
way to actually register what it is other
00:57:07 ►
than trying to grok it.
00:57:10 ►
Henry, did you think, did it change
00:57:12 ►
or is it still doing the same thing?
00:57:13 ►
Kind of research. Very hard to publish.
00:57:16 ►
That I call the macroscope.
00:57:20 ►
Yes.
00:57:21 ►
That’s a good idea.
00:57:23 ►
So you should, yes, all yell out things.
00:57:31 ►
I went over that rather quickly.
00:57:37 ►
So this is an actual published paper by somebody which is readable,
00:57:42 ►
and he’s one of the, in my opinion, the great geniuses in the history of applied mathematics,
00:57:48 ►
and his name, Christopher Zeman.
00:57:50 ►
So he became a household word in the middle 70s
00:57:53 ►
when his mathematical colleagues
00:57:56 ►
more or less publicly crucified him
00:57:58 ►
for using the latest stuff
00:58:02 ►
from the mathematical research frontier
00:58:04 ►
in a lot of popular applications,
00:58:07 ►
and publishing the results in technical journals
00:58:10 ►
such as Newsweek, Nature, and stuff like this.
00:58:15 ►
So he had a model in which the relationship
00:58:18 ►
to something happening in the brain
00:58:20 ►
was a very distant analogy.
00:58:24 ►
What he tried to motivate like this. We have
00:58:27 ►
neurons that oscillate. Like most of the typical thing, what you call a neuron, that people call
00:58:32 ►
a neuron, does this thing called the nerve action potential. That when you give it an input on one
00:58:37 ►
end, it responds with a solitary wave that travels down the axon. And when it gets near the other
00:58:43 ►
end, it does something to somebody else just once and then stops.
00:58:47 ►
But there’s another kind that does that all the time,
00:58:50 ►
and they’re called bursters.
00:58:51 ►
So that’s something that’s oscillating,
00:58:53 ►
and when you do a recording with an EEG single-cell electrode,
00:58:58 ►
you find that there’s this train of spikes
00:59:01 ►
that is more or less endless.
00:59:03 ►
And if you listen to it in the earphone,
00:59:05 ►
it’s a bunch of clicks, like a porpoise.
00:59:08 ►
It’s called a burster.
00:59:10 ►
And the bursters can be turned on and off,
00:59:12 ►
which means it then stops, silences.
00:59:15 ►
So something’s turning the burster on and off.
00:59:18 ►
So that’s one kind of oscillator
00:59:21 ►
known to exist in the neural net.
00:59:23 ►
There’s zillions of them,
00:59:24 ►
especially if you include glial cells in the neural net. There’s zillions of them, especially
00:59:25 ►
if you include glial cells in the brain picture instead of just neurons. Then there’s another
00:59:31 ►
thing. There are circuits, so-called circuits, where neurons are connected in a net. Each neuron,
00:59:39 ►
in fact, is a distributed processing system with literally millions of computers processing away in its
00:59:46 ►
dendritic net. A single biological cell is already an incredibly complicated net from the point of
00:59:52 ►
view of computer science kind of net. You take a bunch of those that might actually have a loop in
00:59:58 ►
there, where this one tickles, this one tickles, and a loop. And even though each one of those is
01:00:04 ►
a single-shot type of neuron,
01:00:05 ►
the fact that they are in a loop can set up
01:00:08 ►
a solitary wave that more or less travels
01:00:10 ►
around endlessly, and then you get an
01:00:11 ►
oscillator. So his analogy
01:00:13 ►
was, we don’t know exactly where
01:00:16 ►
they are or something. There’s a bunch of oscillators,
01:00:17 ►
like you get alpha rhythm, beta rhythm,
01:00:19 ►
theta rhythm, so the oscillators.
01:00:22 ►
And then there’s another circuit.
01:00:24 ►
There’s this circuit, and then there’s another one, and maybe this one is sort of driving this one. And then there’s another circuit. There’s this circuit and then there’s another one.
01:00:25 ►
And maybe this one is sort of driving this one.
01:00:27 ►
And then as this driving one slowed down,
01:00:30 ►
its rate of oscillating would cause this other one to do a certain something.
01:00:34 ►
So that was the kind of level of application that he suggested.
01:00:37 ►
And then he applied hardcore knowledge about this Dufing bifurcation
01:00:41 ►
in the context of electronic oscillators.
01:00:45 ►
That was the idea.
01:00:46 ►
And catastrophe theory came in there, so that’s why he was crucified.
01:00:49 ►
He was associated with this debacle of catastrophe theory that happened in the 70s.
01:00:56 ►
He’s been forgiven now.
01:00:58 ►
He was appointed director of whatever it’s called,
01:01:01 ►
essentially National Science Foundation in England last year.
01:01:06 ►
Well, the applications are good ones.
01:01:09 ►
There’s still no one doing research in catastrophe theory.
01:01:12 ►
There are a few papers, but basically everybody is afraid to touch it.
01:01:18 ►
The father of catastrophe is René Tom.
01:01:22 ►
Yeah.
01:01:24 ►
No, no.
01:01:21 ►
catastrophe is Rene Tom.
01:01:22 ►
Yeah.
01:01:24 ►
No, no.
01:01:27 ►
How would you define catastrophe?
01:01:30 ►
Well, this is a technical term from this mathematical theory.
01:01:32 ►
And it was inspired,
01:01:34 ►
I think, Tom, the father of catastrophe
01:01:36 ►
theory, is spelled with an E, see, because
01:01:38 ►
he was a great classicist. I mean,
01:01:39 ►
among scientists. He studied,
01:01:42 ►
he knew Greek.
01:01:43 ►
So it just refers to the situation where you
01:01:46 ►
have the box with the knob, or knobs, and you change them, but gradually, smoothly,
01:01:54 ►
and ever so slightly. And then suddenly, there is a disastrous, sudden, radical change in
01:02:03 ►
the behavior of the system. There’s nothing catastrophic about it in the sense of, you know, something bad is happening.
01:02:09 ►
But like that is the one where you’re pushed slightly and suddenly it lets go.
01:02:16 ►
And I’m afraid that’s where this tape cuts off.
01:02:19 ►
It’s also maybe a way for me to better explain why I’m intimidated about interviewing Ralph.
01:02:26 ►
You see, in the discussion immediately preceding the cutoff,
01:02:29 ►
there were dozens of openings for questions about the complex topics he’d just mentioned.
01:02:35 ►
And I’m sure you were thinking of some yourself.
01:02:38 ►
Yet, had I been interviewing him, my question would have been about the Foucault Pendulum
01:02:43 ►
at the Museum of Science and Industry in Chicago. You see, I was 12 years old when I first saw it, and
01:02:49 ►
it was all my Aunt Anne could do to pry me away from that exhibit. I can still clearly see in my
01:02:55 ►
mind exactly where I was standing and watching it swing back and forth, trying to feel the earth
01:03:01 ►
rotate under me. It was a powerful childhood experience that, well, it actually launched my interest in science.
01:03:08 ►
So, do you see how easy it would have been for me to divert the conversation from science to nostalgia
01:03:15 ►
and change the tone of this conversation?
01:03:19 ►
That’s why I no longer do interviews without a co-pilot to keep me on course.
01:03:24 ►
Now, for me, there was a lot of new information in this talk.
01:03:27 ►
In fact, I’ve already listened to it twice,
01:03:29 ►
and after I reread my underlines in Ralph’s book Schism,
01:03:33 ►
I’m going to listen to it once again.
01:03:35 ►
And my guess is that you will most likely do the same thing.
01:03:39 ►
If modeling the future has some interest for you,
01:03:42 ►
then be sure to read Schism.
01:03:43 ►
It takes a cybernetic approach to explore the impact of social media,
01:03:48 ►
political polarization, and collective behavior.
01:03:52 ►
And by taking a cybernetic approach to explore these topics,
01:03:55 ►
you can get a better look into the interconnection of these phenomena.
01:03:59 ►
And I think you’re going to find it fascinating.
01:04:02 ►
So for now, this is Lorenzo signing off from Cyberdelic Space.
01:04:07 ►
Namaste, my friends. Thank you.