It's a great pleasure to welcome you to our CC Mei Distinguished

seminar series.

This is a series that is sponsored

by the Department of Civil and Environmental Engineering

and the CC Mei Fund, and this is our first Distinguished seminar

of the term.

It's a great pleasure to see it's a full house.

Hopefully for the people that will be late,

they will still find some seats.

And so for today's inauguration talk of the term,

we will be hearing from Professor George Sugihara,

and George Sugihara is a Professor

of Biological Oceanography at the Physical Oceanography

Research Division, Scripps Institute of Oceanography

at UC San Diego.

I'm co-hosting Professor George Sugihara with Professor Serguei

Saavedra here in CEE.

So professor Sugihara is a data-driven theoretician

whose work focuses on developing minimalist inductive theory,

extracting information from observational data

with minimal assumptions.

He has worked across many scientific domains,

including ecology, finance, climate science, medicine,

and fisheries.

He's most known for topological models in ecology,

empirical dynamic forecasting models, research

and genetic early warning signs of critical transitions,

methods of distinguishing correlation

from causal interaction time series,

and has championed the idea that causation

can occur without correlation.

He provided one of the earliest field demonstrations of chaos

in ecology and biology.

Professor Sugihara is the inaugural holder

of the McQuown Chair of Natural Science at the Scripps

Institute of Oceanography at UCSD.

He has won many other awards and recognitions,

including being member of National Academies

Board on Mathematical Sciences and their applications

for a few years.

And today, he will discuss understanding nature

holistically and without equations.

And that's extremely intriguing for all of us.

And so without further ado, please join me

in welcoming Professor Sugihara.

[APPLAUSE]

This is in my presenter notes, so I'm reading it

off of the screen here.

I want to make a disclaimer, however.

In the abstract, it says that these ideas are intuitive.

Are you good?

Are we good?

OK.

So the abstract says that the ideas that I'm going to present

are intuitive, but this is not entirely true.

In fact, for whatever reason, at one point,

the playwright Tom Stoppard approached me,

and he said that he was interested in writing something

about these ideas and wondered if it

would be possible to explain these to a theater audience.

And just read the dark black there.

His response was that if he tried to explain it

to a theater audience, they'd probably

be in the lobby drinking before he

got through the first sentence.

So the ideas are in fact decidedly counter-intuitively.

And this is a fact that in a sense

goes against how we usually try to understand things.

So I'll explain what that means in a second.

So we're all familiar with Berkeley's famous dictum,

but despite this warning, correlation

is very much at the core of Western science.

Untangling networks of cause and effect

is really how we try to understand nature.

It's essentially what the business of science

is all about.

And for the most part and very much

despite Berkeley's warning, correlation

is very much at the core of how we try to get a grasp on this.

It's an unspoken rule, in fact, that within science

and with how we normally operate,

it's a correlation is a reasonable thing to do.

It's innocent until it's proven guilty.

Thus, distinguishing this intuitive correlation

from the somewhat counter-intuitive causation

is at the crux, and it's the topic of this talk today.

So I'm going to develop a discussion

for making this distinction that hinges on two main elements.

First, the fact that the nature is

dynamic in the temporal sequence matters.

Meaning that nature is better understood as a movie than as

snapshots, OK?

And secondly is the fact that nature is nonlinear,

that it consists of interdependent parts that

are basically non-separable, that context really matters.

That nature can't be understood as independent pieces

but rather each piece needs to be studied

in the context surrounding it.

So let's start with a nice, simple example.

All right.

Consider these two time series.

One might be a species, or these might

be two species interacting, or one

might be an environmental driver and responding species,

or a driver and a physiological response,

or money supply and interest rates, something like that.

So if you look at 10 years of data,

you say your first hypothesis is that these things are

positively correlated.

You have this kind of working model for what's going on.

If you roll forward another dozen years,

you find your hypothesis holds, but then it

falls apart a little bit here and in the middle,

right in here.

And then it sort of flips back on here towards the end.

So out of 18 years of observations, actually

more like 22 years of observations,

we find that our hypothesis that these things are correlated

is a pretty good one.

If this was an ecology pattern, if this

was a pattern from ecology, we'd say that this

is a really good hypothesis.

So we might make an adaptive caveat here, kind of an excuse

for what happened when it became uncorrelated, but more or less,

this looks like a pretty good hypothesis.

This is, however, what we see if we roll forward

another couple of decades.

In fact, for very long periods of time,

these two variables are uncorrelated.

They're totally unrelated.

However, they appear from a statistical sense

to be unrelated, but they were actually

generated from a coupled two-species difference

equation.

So this is a simple example of nonlinear dynamics.

We see to two things can appear to be coupled

for short periods of time, uncoupled,

but for very long periods of time,

there's absolutely no correlation.

So not only does correlation not imply causation,

but with simple nonlinear dynamics, lack of correlation

does not imply lack of causation.

That's actually something that I think is fairly important.

In retrospect, what I just showed you,

you might think this is obvious, but apparently this

is not well known, and it contradicts a currently held

view that correlation is a necessary condition

for causation.

So this was Edward Tufte who said

that empirically observed variation is

a necessary condition for causation.

OK.

So the activity of correlation, I think,

reflects the physiology of how we learn.

And one can argue that it's almost wired

into our cognitive apparatus.

So the basic notion beyond Hebbian learning

is that cells that fire together wire together.

So the mechanism of how we learn is really

very sort of supportive of the whole notion of correlation.

So I think it's very fundamental to how we perceive things

as human beings.

OK.

The picture that emerges is not only

that correlation does not necessarily imply causation,

but that you can have causation without correlation.

OK, and this is the realm of nonlinear systems.

This is interesting, because this is also

the realm of biological systems.

So within this realm, there's a further consequence

of non-linearity that was demonstrated in the model

example, and that's this phenomenon

of mirage correlation.

So correlations that come and go and that even change sign.

So here is a nice, simple example of mirage correlation.

This is an example not from finance but from ecology.

This is a study by John McGowan, and it was an attempt

to try to explain harmful algal blooms at Scripps,

these red tides.

So these spikes here are spikes in chlorophyll

found at Scripps Pier.

And what we see in the blue at the bottom

are sea surface temperature anomalies.

And so the idea was that the spikes in chlorophyll

were really caused by the sea surface temperature anomalies.

This is about a decade's worth of observations.

They were about to publish it, but they

were kind of slow in doing so.

And in the meantime, this correlation reversed itself.

And not only did it reverse itself,

it then became completely uncorrelated.

So I think this is a classic example

of a mirage correlation.

OK.

So here's another example from Southern California.

Using data up to 1991, there's a very significant relationship

between sea surface temperature here,

and this is a measure of sardine production,

so-called recruitment.

So this was reported in '94 and was subsequently written

into state law for managing harvest.

So if you are above 17 degrees, the harvest levels are higher.

If you're below 17 degrees, they were lower.

However, when data-- if you add it to this existing data,

data from '94 up to 2010, this is what you find.

The correlation seemed to disappear in both cases.

So these are two different ways of measuring productivity,

and the correlation disappeared in both of them.

So this statute that was written into state law

has now been suspended.

And this is where it now stands.

All right, so another famous example from fisheries

was this meta-analysis on 74 environment recruitment

correlations that were reported in the literature.

So these correlations were tested

subsequent to the publication of each original paper

by adding additional data to see if they were upheld.

And only 28 out of the 74 were.

And among the 28 that were upheld

was the sardine, so we know what happened there.

OK, so relationships that we thought we understood

seemed to disappear.

This sort of thing is familiar in finance

where relationships are uncovered but often disappear

even before we try to exploit them.

OK.

So how do we address this?

The approach that I'm going to present today

is based on not only your state space reconstruction, which

I refer to here with a little less technical

but I think more descriptive name,

which is empirical dynamics.

So EDM, Empirical Dynamic Modeling,

is basically a holistic data-driven approach

for studying complex systems from their attractors.

It's designed to address nonlinear issues

such as mirage correlation.

I'm now going to play a brief video that I

think is going to explain all.

This is something that my son actually made for me when

I tried to explain it to him.

And he said, no, no, no, you can do this--

it doesn't take three hours to explain this to someone.

You can do this in like two minutes

with a reasonable video.

So he made this nice video for me.

The narration is by Robert May.

[VIDEO PLAYBACK]

- This animation illustrates the Lorentz attractor.

The Lorentz is an example of a coupled dynamic system

consisting of three differential equations, where each--

[END PLAYBACK]

Oh, technical difficulties.

Sorry.

Let me start it again.