Okay, so let's get a little bit deeper into this. What do we mean by complexity theory? And you will hear a lot about complexity theory, for example, in climate studies, again, in ecology, there's quite a bit of it in the IT world. Let's, I'm going to take you, again, just, just a little bit and give you some basic foundations of complexity theory. So, one of the ways we can talk about is different levels of complexity, if you will, or different results of that complexity. And let me take you through three. One is organized simplicity we might call it. Another one is disorganized complexity. And the last one is organized complexity. What do these mean? Organized simplicity, let's think of the two terms. The organization, there is an order here. Okay? These, these four individuals are not sort of randomly related. Each one is related, let's say if these were birds flying, you are watching or you are following the bird at a particular angle. Okay? But there are still, there are relatively few agents, and the pattern seems to be fairly obvious. Okay? That's an example of organized simplicity. Disorganized complexity is when you have many, many agents, but there doesn't seem to be a simple rule that is assigning them a particular role in the system. Okay? It's apparently random. Each agent is sort of going in their various directions. Okay? It is complex, but it's not organized. You're not getting any kind of organization out of this. You're not getting any patterns. And that's the key thing is the absence of patterns. Organized complexity is where you have various players, but you seem to have an organization of patterns. That is, that you can make sense of this. You can predict how long it's going to take for this person to hear this message based on these links. Again, these links are not being produced by some master systems god, but each agent is deciding with whom he or she is going to have some contact in one way or the other. But there is an organization where you can start seeing, for example, you might see a hierarchy of contacts. Some people might be more popular than others. Or one group is popular over here and another group is popular over here. It's very hard to understand. It's very hard to understand, for example, the entire clique structure of an American high school. But you can find that there's organization, that there's patterns behind it, again, dictated by the agents themselves. Now, disorganized complexity, in some ways, this is just what we mean by complicated, okay? And disorganized complexity is simply a function of the number of parts involved. And allow me to read this, because I think it explains it very nicely. "The motion of a single ivory ball as it moves about on a billiard table. One can, with surprising increase in difficulty, analyze the motion of two or even three balls on a billiard table. But as soon as one tries to analyze the motion of ten or fifteen balls on the table at once, as in pool, the problem becomes unmanageable." Now, less so, less so now that we have massive computing power. But it still requires that you go through every single possible iteration. And with some situations, you might have so many iterations that even modern computing power might not be able to do it. It's "not because of any theoretical difficulty," we understand the physics pretty much, ". . . but just because the actual labor of dealing in specific detail with so many variables turns out to be impractical." Now, again, this kind of complexity is being reduced to a certain extent by computing power. But there is always a limit to that. And, again, let me just give you a couple more examples of what we mean by organized simplicity, organized complexity, and disorganized complexity. So, the organized simplicity, you see it's a very— there is an organization, but it's very, very simple, okay? You can have disorganized complexity where you have all these various units, but there seems to be— there seems to be, this is important, in just a little bit we'll see, no pattern at all. And then you've got organized complexity where you've got many of these units, and they do form a pattern. Again, we could think of this as a series of cliques in a high school. Or zones where some people go and some people don't go. And, again, it's having the property— if we talk about organized complexity, and this is what we're really interested in, it has to have emergent properties. That is, the organization arises from itself; Show the essential feature of organization, some kind of pattern that we can discern and we can analyze and that it repeats. And these are problems that deal with a sizeable number of factors, which are interrelated into an organic whole. Now, I'm hoping you see that our conversation about globalization plays very nicely into this. So, you have thousands, if not millions, of firms, or billions of individuals. You have roughly 200 countries, et cetera, each behaving, each making its own decisions as it were, but they become organized, they get—they grow into alliances, or they grow into trading partners, whatever it might be. But we start seeing the pattern that one country is more likely to trade with another, or one country is more likely to send people to another country. The key, again, and I keep emphasizing this, is emergence. It's non-random or correlated relationships that create a differentiated structure, which, as a system, can interact with other systems. What does that mean? It means that you've got this—these individual parts, okay, that come together to produce some kind of product or some kind of action, which, in turn, deals with other systems or with its environment. The coordinated system manifests properties that cannot be carried by or dictated by individual parts. This is critical. Think back on that crowd. Even if I knew the psychology of every single individual in that crowd, I might not be able to predict that crowd because of the various possible interactions. The organized aspect of this form of complexity vis a vis other systems, this is what we're talking about: emergence. So, if you want to think about emergence, it's the opposite of a guiding hand. Okay? Emergence is—you might have some guiding hand all the way down, but its ability to actually predict what's going to be happening through these emergent properties gets more and more and more limited. And this is the key thing. Again, in terms of globalization. Can we understand globalization by understanding each particular country? Can we predict what's going to happen in globalization by understanding each particular society, each particular country, each particular firm? Or do we have to accept that globalization might lead to such aggregation and such complex behavior that phenomena or behavior starts emerging from globalization itself? That globalization is not something that happens to or that systems engage in. Globalization becomes, in and of itself, the subject of study. Very simply put, the whole is greater than the sum of its parts. All right? So, you can have a series of individuals, and somehow they come together to form a part, which you would not predict on the basis of those. So, the termites in a mound have physiology, biochemistry, and biological development that are at one level of analysis. So, we can take a look at each one of these termites and say, well, this termite has this characteristic and this termite will respond to this signal this way, and this termite has a life expectancy of so long, and so long. But their social behavior, when they come together in mound building, is a property that emerges from the collection of termites. Okay? That is, you cannot understand how this mound is going to occur just by understanding a single termite. It is the interaction of all these termites. Again, start thinking of the implications for what we were talking about with globalization. Chaos, there's lots of definitions of chaos, including the evil agency in Get Smart. But chaos describes phenomena that appear to be random, but there is some systemic logic of what's going on. We might see this as completely random. But if we actually break it down, we can start noticing some basic rule that is being followed. Okay? That there is some principle, and it's not completely random. There is a pattern, okay, to this. Now, the thing about chaotic systems is they're very sensitive to starting conditions. That is, depending on where you begin, if you apply the same rule, but you begin—think of it, a simple rule of taking two steps to the left, one step to the right, and then forward. Okay? Now, that's the—and let's say we get people to start moving that way, and it's going to look very chaotic. Now, depending on if we have them start over here or over here, they're going to achieve—following the same rule—they're going to arrive at very, very different destinations. And the best is if you want to Google this, the Lorenz experiment with weather data. Where you can come up with basic principles of weather, but depending on what your starting condition, or what the starting measure, or the precision of your starting measure, it's going to be different. It's non-linear. That is that, and we're going to be talking about nonlinearity, that the result is not going to be proportionate to the cause, or the effect is not proportionate to the cause. You can have a very small, very small change, and it will produce completely different results. You can have a small change of a stone in the way of these people walking, or a wind which forces them slightly to one angle, or a temperature difference that might make them walk faster or slower. Depending on these, you're going to have these massive effects. You could change the temperature of this environment by point one degrees, and you're going to come to very different locations. This is why, for example, when we're talking about climate change, it is so important to understand that a very, very small change could produce extremely important results, potentially catastrophic results. And it's the repeated iteration of a simple formula. But, again, depending on what those conditions, those starting conditions, and depending on what happens at every single particular moment, that can shift the direction. Now, this is really difficult. Okay? Trying to understand these things. A complex system is one that almost by design, although of course it's not design, but by definition or function, is both difficult to understand and verify. That is, if you can pick it up right away, it's not a Complex Adaptive System. And moreover, it is very difficult to verify that you actually have found what the logic behind the system—that you can prove, all right, what it is that consistently is going on. It's a very difficult enterprise. And we haven't even begun to understand what these system dynamics and what these system structures might be for globalization. We're just beginning. But let's expect that it's going to take a while, because it is inherently very, very difficult. So, what are the mechanisms of complex systems? Let's take a look, you know, one step further inside this system, and try to understand what the mechanisms are that produce this kind of emergent behavior.