Subtitles section Play video Print subtitles 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.