Welcome again. Well, what I did is I'm in communication with new students but also with previous students and I asked, What would be the kind of thing that you want to get out of this course? I got no different remarks. They said, Well you know, it's a transdisciplinary methodology and I want to understand more about that. And someone said, I want to understand suicide. Okay, and then once someone said, I want to understand organizational leadership, or I want to have a better understanding of market best practices using HR flow modelling. And someone said, No. Actually I want to understand self-organization, and another one said, "No I want to understand financial markets, and economic behavior, and the thinks that negot econophysics. And another someone said, No. Actually I want to have much more insight to data complexity in data analytics. It has been all like that. complexity, economics, e-health, relevance of complexity to national security, and it went on and on like that. And so, this actually shows that you do understand what it's about. It's about life, the universe, and everything. And, there's of course a risk in that, the moment you say you claim that you can do life, the universe, and everything, you're actually doing anything. So, what we will try to do is to zoom in into particular aspects of complexity science, and hopefully address even some of those aspects as shown over here. And, we also look for the common denominators that you will find across those fields. Okay, so and this is a set of lectures. This is not a set of talks, it's a set of lectures, you know, we're supposed to teach you something. So, at the end, you just you know, when you go home you actually say, Oh, I learned this, and I learned that. So, what we try to do is actually teach you something in a way that in the end you will be able to teach yourself. And, that's why we have these afternoon sessions where Mike Lees will lead all the afternoon sessions, and he will try to teach you how to fish yourself. Okay. So, what is the goal? What is the goal we have with this course? So, we want to introduce the basic concepts of complexity, complex systems, some of the applications. We want to provide some kind of theoretical framework, and a kind of a technical background that allows you to reason about complexity. So, you will be able to solve things yet, but you will be able to reason about things. And, that's actually the first part of trying to understand things. Your goal is also to discuss and experiment with complex systems and tools that mainly will be done in the afternoon sessions. And to somehow create a kind of a tutorial that actually helps you then do teach yourself to fish. And, today is then the introduction of complexity, and it's my pleasure to introduce you to that, and I'll do that in a bit of a hand-waving. I will touch upon different topics of complexity, some I might say a bit more technical things and so much stay at the kind of the bird's eye view. And so, what is a complex system? Everybody has his favorite definition of death, and these are the two ones that I actually picked up in the last many years and the ones that I like most. You see two examples. One, is a flock of bird seeds or Sterling's, and I have these beautiful microscopic patterns, and they're all based on what's in an individual bird decides to do. And, that's one of the characteristics of complex systems, I'll get back to that later. Another example, a beautiful example that actually makes you a life that's affected you are here that you are actually breathing in your life is your immune system. One of the most complex systems we can think of. With everything interacting with everything billions, and zillions, trillions, whatever Google amount of molecules, constantly interacting with each other keeping you alive and keeping the invaders out. So, two of the partial definitions that you might want to think about. One is that complex system is not simple. That means that the moment I try to dissect it to the moment, I kind of have a reductionistic approach I'm trying to take the elements apart, that moment I lose the very thing that I'm looking for. So, the moment you try to make it simple you break it down in its elements, you lose the aspect that is actually driving complexity. And the other one is and it's the same to some extent, it's about elements that are interacting with each other and with the environment that they create themselves. So, there you see these kind of big loop happening. So, they're interacting with each other, they create an environment, and they react to that environment, and that the environment influences themselves, and it goes on like that. So, it's like the bird flock, the birds are interacting with each other that might be something happening, might be a hawk coming by that some birds in the end, react to that. That gives you different wave happening and that actually makes them acting differently again. So, you get interaction among each other with their environments and acting on that environment. So, these are like kind of two hand-waving things of descriptions of complexity but you will notice that even though it's hand-waving they are actually already quite accurate and we can quantify that, we'll do that later. So, these two examples of complex systems are already really complex. I mean, just that whole interaction of birds, because all the individual behavior, all these individual characteristics of the molecules and the proteins that are expressed in the cells is really complex. But, we'd also look at human based systems that show complexity even if we start out very simple. And, here's a nice example. Where we look at a set of cars they are positioned at the same distance when they start, and the only instruction we give the drivers is stay away from your neighbors, from your front and your back neighbor. Just to try to keep the distance constant. But, what you will see for some time is actually that you get a kind of a density change in that cluster so you will end up in a set of cars. Here you see already some kind of traffic jam happening, even though you started out very very simple just drive, right? Probably, if you would ask a Google car to do that it will probably be better. And, the reason why these things are not like that because there are people in there, and they act and they react, and the way they act and react might not be completely the same for each of them. As a consequence of that, you've got small differences, and then they tried to compensate for those differences, and that actually done to keep these waves. So, they are to some extent particles, agents, individuals that are reacting to their environment and to each other, and creating an environment because of that. So, there you see an example from well, real life, already showing these kind of emerging patterns as we call them, in this case, the density wave, and the traffic jam, and these emerging pattern showing them from very simple, they're relatively simple interactions. And, we find them everywhere. And people have been studying and trying to understand complex systems, or trying to define it one way or another for many, many years. And here's an example from the Chinese culture, and some of you can actually read this, I guess. I mean the Chinese things. And, there's this beautiful one that I found that says, Look at a tree, a mountain, or the foam on the water when it hits the shoreline. All amazingly beautiful, and all kinds of wild and crazy patterns. All of it has an order to it that we simply cannot measure or describe. This is Li, the organic pattern. So, this is the text from 6000 years ago, and it talks about how those patterns that we see in the world around us, how they keep on amazing us, and how difficult it is to understand why they showed. Why these patterns are like they are? Where do they come from? Can we predict them? Can we understand them? And here actually, they say we cannot understand them. Maybe we cannot but we can at least try. Let's put science as well if we try to understand those things. So, this is a text from 6000 years ago. And here is a text and here's some of those patterns. And, here's the text from 1917. From one of my heroes, D'Arcy Thompson, who basically says the same. And, he is the guy who first tried to quantify biological patterns in terms of numerical models. He talks about he says, The waves in the sea, the ripples on the shore, the sweeping curve of, it's all a pretty romantic almost. I like it. Sweeping curve of the sandy bay between the headlands, the outline of the hills, the shape of the clouds, all these are so many riddles. It's in beautiful book, On Growth and Form. If you ever, you know, come across that try to get a copy of it, it's really worth your while. It is the first try, at least the first one that I could discover were really in a quantitative numerical way try to understand biological patterns in terms. I'm trying to come up with models to do this. And then, the last example about these patterns comes from another hero of mine, Richard Feynman. And Feynman said, and this is also a very beautiful quote, and again, it expresses already this whole concept that we are talking about today. Nature uses only the longest thread to weave her patterns, so each small piece of her fabric reveals the organization of the entire tapestry. Isn't that beautiful? It's in beautiful way of saying how you know, this total tapestry comes about from local things that actually somehow we can find back elements of the local thing into total tapestry again. And the trick is of course, can we quantify that? Can we understand what's happening there? Well, what you see with these three examples the Chinese example, D'Arcy Thompson, and Richard Feynman, is it's really shows that people are passionate about these things, right? The way they formulate these things you already see that the passion, and almost the poetry of science happening here.
