Hello. So, we've talked about globalization, and we've talked about how globalization can be understood as a set of pipes, a set of flows, et cetera. And maybe the best way of calling it is a system of pipes, a system of flows. Now, the word system is used pretty loosely in all sorts of fields. I'm going to give you a pretty precise, or I hope fairly precise definition of what we mean by systems in this context. Now, the first thing to admit is I am not an expert on this. I am not a system scientist. I certainly don't have the math to be able to understand a lot of system science. But I'm interested in using it as a way of better understanding globalization. I'm interested as a hermeneutic device for understanding globalization. It's a little bit like you don't need to know everything that's in your car's engine to be able to drive it. Well, I assume—assume I'm one of those pretty well educated yet ignorant drivers. Okay? So, how do we define systems? Systems is, it's a framework with which we could describe a set of objects or actions that work in concert. That is, it's really a framework that allows us to understand how different parts are working together. It's a way of studying interconnected actions or agents, all right? So, you've got a whole bunch of parts, you've got a whole bunch of agents, you've got a whole bunch of subsystems, you have a whole bunch of networks, et cetera. How they come together is what systems theory is about. And it's a way of making sense of what these different interactions produce. So, we have all these various parts. We have some input. We have a process. We've got output. We've got feedback, which goes to the input. All this underneath some environment. And we want to understand how these various parts come together, how they function together. And that's what systems theory is about. It is imposed on interacting and interrelated activities. It doesn't necessarily have to be an actual system. That is, it doesn't necessarily—it doesn't mean that someone has designed it as a system. There's lots of systems that people have designed as systems. Engineering systems, factories, transportation devices, et cetera. But we can also use systems theory to understand how all these various parts come together, even if no one thought of them coming together, even if no one designed it to come together. But basically what we want to see is how all these various parts produce something new. Okay? Now, it's ecological in nature. That is, and a lot of the early systems theory comes from ecology, and a lot of the language that we use comes from ecology. And ecology is the one place where we have developed probably the most sophisticated systems. That is, we look at the interconnectedness, or the interrelatedness in a particular environment. So, an ecologist, again, might not be so interested in who these trawlers are, or how these trawlers work, or they certainly might not be interested in the identity, let's say, or the action of any of these various creatures. What they're really interested in is how, from the bottom of the food chain up, these come together, and how the harvesting, okay, might affect it. So, for example, we would be interested if the harvesting is beginning to interfere with the very, very basic food source, then that's going to affect on the system. All right? So, that's, that's what we mean by systems, just sort of think of it as the satellite view of any phenomenon where you can actually see how these various parts come together. We are interested in dynamical systems. That is, systems that aren't static, but can change and flow. A simple engineering system doesn't change. It does what it is designed to do all the time. The kind of systems theory that we're interested in has these— It changes, the system adapting to what's going on inside of it, and what's going on in the environment— the system itself can change. So, the system—we don't want to think of systems theory as a photograph. Rather, it's a movie. And it's a movie that might have multidimensional parts. It might be five movies going on simultaneously, four with individual parts of the systems, and a movie that shows you the whole part. So, again, we're interested in flows and how these can change. Now, these are very, very hard to understand. As systems get larger and more complex, it becomes increasingly challenging to model interaction, causalities, and outcomes with any accuracy. That is, as the machine, or as the system gets more complicated, the ability to actually tell what's going on or how one part is related to another becomes very difficult. So, for example, let me give you a very simple demonstration. This is a system, I think it's in Toronto. What this does, what a map does, it gives you a representation of how if you want to get from the Allandale Waterfront all the way to West Harbour, well, you could describe this, or you can simply look at a map and go, "oh, I'm going to do this, and then there's an exchange here, and I'm going to go down this way." All right? So, maps, graphs, these are guides by which we can understand how you get from one part of the system to another. Because these things get very, very, very complicated very, very, very quickly. And they're all about the inter-dependence. This is the famous scene from I Love Lucy. Actually, this is a very simple linear system. The candies are flowing down, and you have to put some wrapping on them. Okay? That's a very simple system. There's an interdependence between whoever is producing the candies and who is wrapping them, and then on and on and on. But what we're really interested in is how individual parts are reliant on other parts of the system. If you're sitting here—again, in a very simple linear system, okay?— If you're sitting here but no chocolates are coming down, okay, then there's nothing for you to do. Or if the chocolates, once they're wrapped or whatever they might be, go into a garbage, well, that's not doing very much. It's the interactive networks that depend on one another. It's how the various parts come together. That means, okay, and this is very important, start thinking about on what we were talking about globalization, that means if any part of the system disappears, if any part of the system fails, the subsequent parts of the system may not function. Sometimes they can, and we'll talk about this with modularity or redundancy, et cetera. But usually systems are designed in such a way that if one part of a junction doesn't come together, the next parts can. Let me just go back to our subway map. If there isn't a train between Maple and Rutherford, for example, okay, if this link is broken, then the connection between this stop and this stop is broken. The relationship is the key unit of analysis. The phenomena can be viewed as relationships. Again, let me just go back to my subway map. We don't really care when we're talking about a system where any of these locations might actually be. That's why, for example, subway maps tend to be very abstract. Okay? The importance is not King City or Maple or Rutherford or York University, et cetera. It's how they relate to others. Oh, you have to go through Maple to go from Allandale down to West Harbour. All right? That's what's really important. It's not so much that we care about any of these particular stops, let's say, but how they fit into a system from our origin to our destination. Now, that means that we're largely ignoring the elements themselves. Okay? For example, in this illustration, we don't really care about these individuals. What these illustrations are doing is showing a type of relationship. Do we really care whether these people really like each other, or are they in love with each other, or whatever it might be? No. This is simply an illustration of some sort of affection, as is this. This is an illustration of some sort of possible cooperation, playing with a ball. This might be an illustration of the various parts of the system going along together. But, again, we don't really care about the individual agents. In this way, systems can be—and we can talk a little bit about this— Systems can be alienating. A systems perspective can be alienating. Because we don't really care so much about you or the person that is involved as the relationship between you and the other people in the system to other people in the system. So, relationships are the key unit of analysis. We could care less about the elements of the agents. We don't care about each one of these individuals of this crowd. What we care about is this crowd as a system, how it might respond, let's say, to someone calling out "fire" or seeing some smoke or some wonderful news coming over the PA system. Then we don't really care how each individual person might feel about that, or responds to that. What we care about is how that collective response, whether it's fear, whether it's euphoria, et cetera. So, very simply, this is not about defining "X," okay? This is not about defining any individual. But about how "X" exists and interacts with the system to produce the possible states "Y." All right? So, again, we're not interested in individuals in a crowd. We want to understand the crowd as a system and how it's going and how it's behaving. One of the things we do when we're observing systems is that all relationships have patterns. Okay? What we're interested in is patterns. We're not interested in laws. All right? We're not interested in saying this system will always behave this way, or there is a physics that explains the causality. What we're interested in is patterns. Do these kinds of people tend to come together? Do these colors tend to organize themselves by color? Or do they do so by size? Et cetera. And what we want to do is to understand those properties of a particular system, and when we get into more comparative work, let's say we have four or five systems that we want to compare, we want to say— we want to see if the same properties apply across these various systems. And there are basic rules of behavior that a system might follow. Let's say in a personal network. People always call back. That might be one of the patterns that we find, that whenever there's a contact between one agent and another, there is a reciprocal contact back from that second agent. Or you might only call back if someone is more powerful than you. And we observe this pattern in a system. Again, a system of phone calls, a phone tree, if you will, maybe you only, you respond to every single phone call, or maybe you just respond to those of those people who are higher in the hierarchy than you. Or, given the last eight months, you stay a certain distance and angle from someone else. We've all sort of internalized over the last eight months this sort of two meter, six feet distance, where without being aware, so if someone was taking a photograph of all human movements along a street as a system, they would see this pretty consistent pattern of some distance. And if they were comparing it to the same street, let's say in November of 2019, they might observe that the parts are behaving differently with each other, that they're keeping more distance apart. So, again, what we're interested in is these patterns of relationships. How do we find that parts of the system relate to one another, and what is the consistency, if any, and that's an important question, if there's any consistency in those relationships? Now, a particular kind of systems that we're going to go to next is complex adaptive systems. And that gets us even into, you know, a little bit more difficult cognitive territory, but I hope I can guide you through that.
