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  • Welcome, everybody.

  • 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.