#1088 Karoline Wiesner: What Is a Complex System?

00:00 Ricardo Lopes: Hello, everyone. Welcome to a new episode of the Center. I'm your host, as always, Ricardo Lopez, and today I'm joined by Doctor Caroline Wisner. She's professor of Complexity Sciences at the University of Potsdam. And today we're going to talk about her book, What is a Complex System. So, Doctor Wisner, welcome to the show. It's a pleasure to everyone.

00:22 Karoline Wiesner: Pleasure is with me. Thank you for having me.

00:26 Ricardo Lopes: OK, so let's start perhaps with a bit of background here just for people who are not really familiar with the topic to get a little bit more familiarized with it. So, what is complexity, science, how old is it? Tell us a little bit about it and its history.

00:47 Karoline Wiesner: Yeah, happy to. Complexity science is not a very old science. On the contrary, it's it's quite a recent science. And I suppose one of the reasons is that it relies on other sciences to have been around at the time. 11 could say that complexity science really is from the 1980s, and of course that can be debated. Um, AND the sciences that were very, you know, played a big role at the time where things like well dynamical systems theory. Uh, CYBERNETICS, um, systems theory in general, cellular automata, and, and more broadly, um, computer science, which was quite a recent science at that time, actually. So, um, the origins of complexity sciences are, I would say, found in the 1950s, 1960s, 1970s around those sciences which I'm happy to talk more about. Um, THERE was a theory of probability, statistical methods, that, that was all, um. PLAYED a huge role for complexity science to come about. And the uh availability of computational power really made it possible to think about complex systems the way we do now. Because complex systems, or yeah, um. Complex systems are very much data driven, because they rely so heavily on a myriad of uh observations to be made about tiny little things that together give something. Interesting, um, and I'm sure we, we talk about more examples throughout our conversation. Um, THAT I would say is complexity science is the Observation, modeling and prediction of systems that consist of many, many elements with many, many interactions that together form something that the individual elements could not, um, some behavior that is emerging out of these interactions. If you like, I say a bit more about um. What people would consider the starting point of complexity science, and that can be argued, of course, um, but one point in history where we could pin it down if we wanted to is in the 1980s where people started to think about, well, these, I'm not sure I used the term emergence yet, but sooner or later we will have to um emerging phenomena that were Observed in computer programs were seemingly from very simple rules, suddenly something emerged that was not built into these rules directly, but it came out of um out of these rules as a as a consequence of many interactions, feedback. Um, NOISE to, uh, in, in some cases. And these cellular automata were studied and people started to think about, well, how come that these very simple things can can exhibit such interesting emergent behavior, the um The game of life might be familiar to some people in the audience, which is, which is really just a computer program, and it doesn't even have any noise in it. And yet it looks like on the screen things start to become alive, whereas really underneath it it's just a collection of a small collection of very simple rules. So they studied this. At a workshop in uh Los Alamos in the United States, um, and pretty much in parallel to that, there was a workshop also. In in uh in New Mexico, where they wanted to find out the synthesis between the sciences. So these similarities that we're observing in terms of emergent structure. Uh, THEY were, you know, pioneers really in the field that, that, um, got together asking, well, what are the, what is, is there a new kind of science here? Um, AND in particular, So this, I'm, I'm just, I'll, I'll say a few names, um. The workshop was organized by uh John Cohen, and uh White House science advisor, and um what, I think I said John Cohen, George Cohen is the right name. And out of this, they thought, well, actually, is there, you know, is there something else that can emerge in terms of bringing sciences together? So here we have the science of dynamical systems, computer science, and of course biology. And they wanted to think further ahead even is there emerging sympathies in the social sciences, in anthropology, psychology, I mean this was really pioneering, um, a pioneering effort, and that happened in 1983 and 1984. So here one can argue that the the complexity signs as a research field began in earnest. Standing on the shoulders of dynamical systems, um, computer science, system science, cybernetics, of course. And one thing I find interesting about this initial effort was they were really not just thinking about what is the science here that that is, you know, is, is, is asking us to be done. But can we make a teaching institution out of it? And so PhD granting institution, that was the original idea and the ambition of these these people. Um, SO these people, I, I mentioned one of them, George Cohen, Phil Anderson, uh, Charles Bennett, uh, Murray Gell Mann, um, um, Stan Farmer, uh, Stephen Wolfram. So it was, it was really a collection of, it was physicists, computer scientists, economists. Who had this idea And this is part of what complexity science is. It is a way of thinking, which is why it's so important to have it as a, you know, as a teaching subject because you learn to think in certain ways. How, how does structure emerge in systems. That are seemingly very different, but appear to have a similar process of um structure emergence.

07:40 Ricardo Lopes: So, but you mentioned different sciences or scientific disciplines like systems theory, cybernetics, dynamical systems theory. How do they relate to one another and to complexity science more generally?

07:57 Karoline Wiesner: Yeah. The um So cybernetics is um you know, it's founded by Va and Osenblut in, in the 40s. And they called it a a theory of control and communication in living and non-living systems. So Vena is was an engineer, was was a biologist, and they got together realizing that it looks like there is a commonality between these non-living engineered and the living biological systems, and they made this actually quite um precise also using mathematics, calling it cybernetics. Because the principles of, of control in these systems, you know, how do I control the hand movement versus how do I control the flight of a rocket, which of course in the 40s was quite, quite um At the forefront of people's thinking. Appears to be very similar and What does that have to do with complexity science? It is this synthesis between two fields, in this case, engineering and biology, where by digging down one finds common principles that hold in in both and that can be described with similar, if not even the same mathematics. And that is true for this is something that complexity science strives for to find these synthesis, so, uh, using the same mathematics, using the same type of model for systems that are physically speaking very, very different. So, that is cybernetics in the 40s. Um. And then there is uh the systems theorist Ludwig von Better Landy, who, I mean, he already started before um before the 40s, and his, I suppose his, his main contribution is the book General Systems theory. Where he does exactly that, he's looking for. A general approach, or yeah, a new approach to science as such in not being specific in these disciplinary silos, but looking for general principles that that hold across uh different systems, universal principles and laws. And that also is something that complexity science strives for, um, so the modeling, the mathematical description, but also identifying general principles that hold across different systems, universal principles, um, and laws. And then last but not least is dynamical systems. That um took off, well, you know, one could say in the 70s um with Lorenz's work on the uh on, on chaos. It is, so there are two connections, I would say between complex systems or complexity science and dynamical systems theory. One is that complex systems generally are dynamical systems. Just broadly speaking, they move, they change over time. And because of that, many of them are modeled using the tools from dynamic assistance theory. The other connection between the two is that both of them rely heavily on the availability of computational power. The the types of, you know, strange tractors that people discovered at the time. They could not be studied in earnest without computational simulation, and the same is true for complexity signs. It could not exist without computational simulation, and of course, in addition to that, more and more important data analysis, computational data analysis. So I would say those are the main strands between those sciences and uh complexity science.

12:07 Ricardo Lopes: But now to address the question posed in the title of your book, what is then a complex system and what characterizes a complex system?

12:19 Karoline Wiesner: Hm. Yes, that is the question. And there is Well, we, we didn't want to give a single sentence answer because systems, complex systems. They, you know, by nature cannot possibly be captured in a single sentence. And yet in the end we we condense everything down to a single sentence just to help the discussion along, and I will start with that simple sentence and then I'll unpack it a little bit to hopefully communicate that there is a lot inside of that sentence, so. Complex systems are systems that exhibit some, but not necessarily all of the features, um, spontaneous order and self-organization, nonlinearity. Robustness, nested structure and modularity, history and memory, and adaptive behavior. So I admit that was a very nonsense. Um, AND what we're doing in the book is we're, first of all, you know, distilling these features and to this day, nobody has come along and said, look, you've forgotten a feature. Um, SO, so far so good. And the main idea in distilling these features is also asking what are the principal ingredients, so something that actually all complex systems, but not only them exhibit is numerosity, disorder, and diversity. Feedback And non-equilibrium. Now, out of these emerges, and here comes the term emergence in uh these other features that I've just mentioned, um. And I Suppose it's an open question. Um, TO ask whether these features I just mentioned numerosity disorder feedback. ARE Only necessary or are they also sufficient conditions. So if you find them in any system, then necessarily other things will follow such as self-organization or um or memory. So that's an open question. um, BUT this in a nutshell is what complex systems are. The key is really the key starting point is in the numerosity. So complex systems involve many interactions among many components. That is also something which um We try to distill in Well, no, the, the last thing that the last sentence doesn't quite make sense. I, I'll, I'll take that out. Um, SO far, so good. Short answer to what is a complex system.

15:17 Ricardo Lopes: And are there different kinds of complex systems?

15:22 Karoline Wiesner: Yes, short answers, yes. Long answers just by thinking about um these features, so. If I start At the end of the list I mentioned, at the end of the list there was the term adaptive behavior. And in fact, Um, some would always use the term adaptive together with complex systems, adaptive, complex adaptive systems. And adaptivity, however, presupposes something which is um alive in, you know, in the broadest sense. And to us, complex systems don't have to be alive, so there are systems that we classify, we meaning my uh co-author James Layman, the philosopher, and I. Um, CALCIFIED complex. Um, BUT they're not living, so just there you have different types of complex systems. You have those that have exhibit adaptive behavior and those that do not simply because they don't exhibit any behavior. Um, SO that's, that's one way of, of thinking about different kinds of complex systems. Generally, you could say, well, those that exhibit, for example, nested structure versus those that do not. Um, YOU could say there are different kinds of complex systems.

16:46 Ricardo Lopes: And I mean earlier you mentioned emergence. How does emergence relate to complexity and in what ways are complex systems non-reducible?

16:59 Karoline Wiesner: Yeah. Emergence is often mentioned as a kind of a definition, right? So, um, a system is complex if it exhibits emergence, and then you ask what emergencies, and they say, oh, emergencies if something complex suddenly arises. So that's not terribly helpful. Um, We, if we take emergence to be just, you know, the very simple, um, Uh, Phil Anderson put it as more is different, which is a beautifully condensed way of thinking about it. Um, Which means that Uh, The whole, so a collection of particles, a collection of people, a collection of something, many elements behaves in a way that the individual parts, so the parts in isolation. Cannot, do not, and, and possibly cannot. Then you have something that emerges. No. What does that have to do with what I just said about features? The, um, if you take as the basics that you have a system that has many elements, uh, with many interactions, there is stochasticity in there, which in nature there always is. And because there's so many interactions, there is then also feedback between them, um. And out of that emerges something which is not. Um, SORT of captured by Talking about many elements interacting, and that is, for example, the emergence of structure. Um, And that is such a key, the emergence of structure spontaneous self-organization is a key feature of complex system, and that emerges out of the individuals, individual elements. And emergence of structure. And even structure at different scales, which makes it even more interesting. There is one type of emergence that we observe in complex systems that now we study. There is also emergence of dynamics, uh, structure is something static, but it could also be structure in time, right? And then you have emergence of dynamics, you have emergence of laws, universalities, um. I mean, physicists love phase transitions, so you could say that's a kind of an emergence of a universality, universal behavior. Out of the many, you know, local interactions um between atoms, molecules. So that is the main connection between emergence and a complex system. And your question about the non-reducible. Is Is a good question and it's directly answered by that the systems as a whole behave in a way that the elements in isolation cannot. So. The thing is you find these non-reducible properties in Not just in living systems. So in living systems, I mean, one of my favorite examples is an ant colony where you put thousands of ants of the same species together. And they form a social system, right? They have distribution of labor, they, they, um, um, you know, they stay alive over years and years, although the individual ants do not stay alive for that long, but the colony as a whole has has a lifespan longer than the individuals, um, and all of this, you know, very fascinating um behavior that they exhibit. Cannot be exhibited by a single ant. And even more so if you put just a few ends, you know, I don't know, a handful or so often. They would not form that social structure and they would simply die of starvation, which, which is a, you know, I think it is in a nutshell what um why why these systems are not reducible. And similarly in in a physical system which we can also think of as complex systems, um, we could say that the the ideal gas laws, so that is a relation between pressure and temperature and volume, which is super fundamental physicists learn it in the first year, um, at university, if not earlier. And these properties are simply not assignable to a single atom, so something has emerged here, a form of, well, in this case, a law which is not reducible to individuals, individual atoms.

21:55 Ricardo Lopes: So, uh, I mean, we're going to walk through some examples of this, but what kinds of scientific disciplines this complexity science applied

22:06 Karoline Wiesner: to? I would have to think hard to find a discipline where it cannot be applied to. I could find disciplines where it has been applied to successfully and for a while. Um, AND that is certainly biology, neuroscience, um. And um more and more uh social systems, um, In, in the, to the extent where we can get quantitative data. It is beginning to be applied to um Psychology. And other related uh areas. So, The reason it's so broadly applicable is that this type of emerging structure, emerging dynamics, emerging laws, is so universal. Um, SO one could rather ask, You know, why has it come about so late in the in the history of, of science, which is, you know, it's just what, 50 years ago, um. Um, AND Yeah, I mean that it's a difficult question. I don't think I have the full answer to that. Um, IT'S very much data driven, so as we get in more and more data on systems where we, we do observe some phenomena on the, what I like, you know, what, what physicists and and others like to call the macroscopic um level. We simply because we don't have the means to, to measure on the microscopic level. So let's let's take this, um, let's take the example of The ideal gas law where on the macroscopic level, you know, people have been measuring for centuries, pressure and volume um and temperature and then realized, oh, there is actually, you know, a very clear mathematical relation between them, but it took a while until it was first hypothesized and then actually measured in the lab the constituents of these gasses, the individual. Atoms and molecules. Um, AND And that led to, you know, first thermodynamics and macro uh um uh statistical mechanics came along as the underlying microscopic theory that explained the macroscopics. And a similar development, I would argue we're observing in these other sciences, so we can now measure, you know, movement of individual people simply because we have all these, you know, every single one these days has has a mobile phone is often giving access to where they are, whom they call, and so on and so forth. So suddenly we can start to think about the microscopic theory of these things, um, and we realize that. Yes, there are similar emerging laws. In, you know, be it, even, even in physics, we can still think about it that way, chemistry, biology for sure, uh, behavioral biology, and all the way up to um all sorts of human behavior.

25:19 Ricardo Lopes: So let's go through some examples of scientific disciplines that complexity science applies to. Uh, HOW about ma and physics more generally? I mean, could you tell us a little bit about how complexity science applies to physics?

25:38 Karoline Wiesner: Yes. That's a that's almost the hardest question. You're starting with the hardest question because um Um, it's easy to answer in what way does physics, you know. In what way does physics aid the field of complexity science, um. And that is easier because so many tools from sorry from physics are being used to study complex systems that are not considered to be just physics systems, um, and statistic mechanics which I've just described or which I've just mentioned, a theory of, of phase transitions, critical phenomena. They have all been studied for decades in physics, and now the same concepts and Uh, even more important to say mathematics can be used for non-physics systems. Um, And In what way does complex systems apply to physics? I almost went down with it. Even try to answer this. Um, Rather, I put the question slightly differently, which is in what way do the concepts of complex systems apply to physics? And here, um, I mean, here we're right in the heart of self-organization, right? Self-organization of non-living things because physics generally is about non-living things matter. Um, SO if, if we go to the universe, which is full of structure, I mean anything from, you know, galaxies to star systems to uh clouds of dust and me and whatnot, I'm not an astrophysicist, but there's plenty of that, I know that. Um, WELL, how did it form? It formed through self-organization and the different forces that are involved, you know, we, we know a lot about them now, um, of course, gravity. Uh, IS, is an important one in this context. They form the interaction between these particles. And you just put in, that's the beauty of physics, you just put in a few very simple laws, not trivial, but uh. Um, VERY compact laws and out comes a myriad of different structures. So that is self-organization, and that is why also physics and the theory of non-living matter is effectively um. Can be thought in through the concepts of of complexity science. Have I answered your question.

28:32 Ricardo Lopes: Yes, I, I think so. I understand that it's a bit complicated. So, uh, let's move on to another area. How about the climate science? I mean, what is it about the climate that we can have a better understanding of through complexity science?

28:50 Karoline Wiesner: Yeah. There is climate science is, is really one of the areas where complexity science plays a role. And there are several reasons for that. Um, ONE is that what complexity science is very good at is The one thing I talked about already is this this form of self-organization. There's something else which is that a complex system is, is, is never isolated from its environment. Um, WHICH physicists like to do, so they're very good at. Isolating a system, putting it in vacuum or whatever, and then um studying it, and that is simply not possible with the the climate as a whole, we can't put it in the lab and isolation, obviously. But even parts of it, um you can um To some extent you can isolate certain processes, so um. Let's say, I mean, you can isolate the uh dynamics of the weather from the dynamics of the climate because there are different time scales. But even that is, um, the time scales are overlapping. So 11 challenge with climate is that it acts on Time scales and length scales that are overlapping. So you can't say, oh this happens on the nanoscale, this happens on the, you know, dissolvable meters, and the rest is happening on thousands of kilometers, and in between is nothing. That is simply not the case. Which means that All of these time scales and length scales do interact with each other. That is one of the challenges, if not the challenge in in Describing modeling predicting the climate. And here complexity science is equipped because it is, it has the, the concepts and also to some extent the methods to think about that. Um AND the methods, you know, they involve dynamical systems, they involve um network signs which is explicitly bad interactions where you can then talk about different. Um, um. Lengths and time scales and model explicitly how they interact with each other, so. Um, That's why complexity science plays such a big role in, in, in climate science. Not least through network theory. Mhm.

31:23 Ricardo Lopes: So earlier you gave the example of ant colonies. In what ways are social insects complex systems? And I would imagine this would also apply to other kinds of animals, but focusing now specifically on new social insects, uh, could you tell us about that?

31:44 Karoline Wiesner: Yeah. You social insects are just super. I'm not an expert, but I was, I was lucky enough to read, you know, people's, uh, books by, by people who really know. Um, Deborah Gordon, for example, um, knows everything about ants, I think, and Um, what makes them a complex system is that they, they really, in a nutshell, Exhibit what complex systems do is, which is you put a lot of entities together that individually Can't do that much. Um, SO ants, for example, um. An individual ant, well, it, it can walk and it can, you know, it has senses, so on, but it, it could not survive on its own. Um, YOU put 1000 of them together and they have, for example, a modular structure in terms of the division of labor, right? DIVISION of labor by definition, a single and it can't divide labor. Um, SO that is something that emerges out of the interactions, out of stochasticity, um. And The That's, that's the, the social aspect, right? DIVISION of labor is something that is social. Group Exhibits or can exhibit. Um. And they I mean, we can go through all of the features, in fact, that I mentioned earlier on and think about ants, and they have it all. I mean, the numerosity is, you know, is a given, thousands of them together. The disorder is actually something which is key here. Uh, IT'S, it's there in every complex system, and here it's there because the, I mean the way an ant walks is not deterministic. Um, THERE are, you know, always disturbances, perturbations, um, it either meets another ant or it doesn't, um. And all of that leads to feedback between how ants move and what they perceive in their local behavior. So there might be, for example, looking for food and then they meet another ant which signals that it has just found food, which then leads to coordinated behavior between these two ants. So there is It starts with the disorder, feedback comes in, and then out comes uh spontaneous self-organization and um That's, I mean, that leads to nonlinearity, which is an abstract term, but it, one way to think about it is that um You know, more leads to more so. A few ants discover a good food source. Well, they recruit more and more ants to the same food source and so what started out with one ant becomes a lot of them uh going to the same food source. Um, And that is a is is is a form of feedback which leads to nonlinearity. And It suggests that it might make these systems very unstable, right, because all the ants suddenly run out to the food source, none of the ants will be left in the nest and, you know, an intruder comes and destroys it, whatever. Um, BUT that is not what's happening. um. Because there is also another type of feedback. I mean, there's so many types of feedback here, but certainly to, uh, there's a positive feedback and the negative feedback. So the negative feedback means that the whole thing stays stable. Um, ON, on the level of the system. Because there are other needs inside the nest that are communicated through these often stochastic interactions, always stochastic communication. Um, AND that leads to a robustness of the system as a whole. I mean, there could be, um, You know, you could, for example, uh. Scoop up Ants from outside of the nest. So suddenly no further food is coming in and the ants that were originally there to, uh, you know, to tend to the nest and to the eggs and so on. They realize no food is coming in, no water is coming in, and then eventually, you know, stochastically one after the other will. VENTURE out to get water. Um, SO they will actually change. The uh task they had because of the interaction with the environment. And that is a form of feedback, um, and that leads to a robustness. So, and, and on and on and on, of course, adaptive behavior, that's, it's one form of adaptive behavior. They have it all, um, eusocial insect colonies have all the features of complex systems.

36:51 Ricardo Lopes: So there, there are also, I suppose, many kinds of different social phenomena that occur in human societies that we could talk about here as examples, but let me ask you specifically about the economy. Is the economy also a complex system, and if so, in what ways is it a complex system?

37:14 Karoline Wiesner: Short answer to is the economy a complex system is yes. And why is it a complex system? It's Yeah, I'll give a short summary to begin with and then we, we could go a bit more into detail of the features, um, because the list of features is helpful. So the short answer is that the economy exhibits. Um, EMERGENT behavior, emergent dynamics that the individuals cannot. So for example, a boom or a bust of a stock market, a sudden crash of, you know, certain stocks, is something that emerges out of a myriad of individual decisions that are usually the result of interactions between different traders. So none of these traders individually decides, OK, uh. Uh, WELL, they, even if they decided to, they couldn't make it happen on their own, right? So the, the trader in isolation cannot exhibit a boom or a bust, can only be in the collection of many traders together. And the simple rules that exist between them, which is If there's something to sell, then somebody else can buy it. So that in a nutshell is, is the reason why I talked about the financial economy, but the economy in general is a complex system. And um the longer answer would be then to go through these different features. So, for example, Um, one feature I mentioned is nested structure. Um, SO at different scales, do we have structure that is then part of, you know, structure at a bigger scale. And certainly with the economy we start with individuals that, you know, every household is an economic entity if you like. Um, THAT is part of a bigger economic entity, which would be a neighborhood where, you know, there's a certain number of shops and people go and buy stuff, um. And that is part of a bigger entity, which is, say, the national economy, which is part of a yet bigger economy, which is the international economy. And the um. The interactions are of different types. Uh, SO one interaction is just, you know, buying and selling actual goods. Another interaction could be um financial dependencies, um, or other dependencies dependencies uh in terms of, of raw materials and what not. So there are all these different interactions within a level and between levels. Which means that it becomes actually quite, on the one hand, quite difficult to predict. Um, SO if everybody suddenly starts to buy tulips, then, uh, you know what happens to the tulip market? Well, it's probably gonna crash. Um, But That the economy usually is not that simple, and so predicting something such as the economic crash or crisis in parts of the world in 2008. WAS was actually predicted by some, but the system as a whole was not prepared and did not see it coming. Um, BECAUSE it was the result of many individual interactions. In this case, interactions as in the financial dependencies because some had bought debts of others packaged in complicated ways, and out of these myriad of interactions, suddenly a housing crisis emerged and a bigger financial crisis as a result of that.

41:11 Ricardo Lopes: So you've mentioned earlier on that in more recent times, complexity science has also been applied to the study of human psychology. So, could you give us perhaps some examples or tell us more broadly about how complexity science also applies to human psychology and the study of the human brain in neuroscience?

41:38 Karoline Wiesner: The second one is much easier than the first. Um, OK, I cannot talk much about psychology. Well, I, I, I can't really talk about it at all. I know that psychologists are beginning to use complexity science, um. Because there is structure such as Um, you know, psychological, psychological traits are sort of emerging as key, key categories in how people behave, but really I feel I know way too little about that to comment properly. Um NEUROSCIENCE is much easier. To me Um, because the obvious complexes, and the brain is, is, in my view, is the most Complex system. Although it a brain on its own doesn't, you know, can't survive. It has to be part of a a body. Um, BUT the brain consists of these, um, depending on, so humans, um, you know, millions of tens of millions of neurons. Um, And they are as individual cells, you know, they can't do anything. Um, I mean, potentially they could conduct some current and that's it. Um, YOU put tens of millions of them together and you get something like consciousness, and that is, is mind boggling, right? And we haven't fully understood that, but we, we, I say we in, in the sense of the scientific community, uh, neuroscience community. Um, UNDERSTAND a lot of about the, the ways in which the brain self-organizes, uh, which is a hallmark of a complex system. And for example, uh, you know, we, we know quite well that the brain has different areas of, um, which is, which is responsible, which are responsible for different parts of of brain activity, uh, some of them for consciousness, some of them for instinctive behaviors, some of them for immediate, you know, um, reactions, um, unconscious. And They are made of the same stuff, and yet they do different things. Um, SO that's emergence right there. And um, One property, you know, out of exhibits all of the properties of of complex systems, all of the features. One of them, which to me is is really fascinating is robustness. Which is, I mean, one type of robustness that the human brain has is It keeps the same temperature within just a few tens of degrees, always, no matter whether it's summer or winter, um, humid or dry, um, so it has a self-regulating. Um, PROCESS constantly going on, which is an emergent feature because individual neurons couldn't do it. Um, So that's one basic type of robustness, which is really important for survival, the way we we've evolved. And the other type of robustness, which I find fascinating is a uh a kind of a Well, it's a kind of an error correction. If parts of the brain are damaged, other parts are able to take over the tasks, not always and not generally, but that is possible. So, um, that is a form of of adaptive behavior combined with the robustness that the brain exhibits, um. And Yeah, I'm not a neuroscientist, so I couldn't tell you exactly how these things are are explained and modeled, but the idea is, which is why it's so much fun to be a complexity scientist, because you can, um, you can actually, you're being paid to know a little bit about a lot of things. And that is what I know about neuroscience is, you know, these, these different aspects which are Truly concepts from complex systems are being studied and applied in, in, for example, human brain. Mhm.

45:59 Ricardo Lopes: Now, I also have other episodes where I've talked about complexity science applied to different kinds of scientific disciplines. So if people want, they can go and watch them. So let, we've already gone through examples of scientific fields and certain specific phenomena that complexity science applies to. Let me uh now ask you a different kind of question. Can the behavior of a complex system be predicted?

46:27 Karoline Wiesner: It sounds like the answer should be no, because we had all this stochasticity and emerging things that individuals, you know, elements, uh, do not exhibit. And this is. Where these, there are two phrases that are being used and That are, I think confusing people. One is complexity comes out of simplicity and simplicity comes out of complexity. It seems like, you know, either one or the other has to be false. But both of them are true in certain senses. So what I think what we've talked about so far is how complexity can come out of simplicity. So you put a bunch of things together that individually can't do much. So a bunch of ants individually, you know, they run around in circles and die. Together they form these fantastically complex societies. Um, THAT looks like complexity comes out of simplicity. At the same time, we see examples where simplicity comes out of complexity in the following sense, where, you know, you put 1023 atoms in a container. And close it off, and you will observe that the pressure, the temperature and volume relate to each other in a very precise mathematical way. That seems. I mean, that is something terribly simple that comes out of. 10 to 23 elements that interact with each other in, you know, uh uncountable ways. And so the beauty of complexity science is that it can deal with both. So what it does is, you know, out of these myriad of interactions between ants, let's say, a simplicity emerges at the same time as a complexity, and the challenge of the science is to extract these simplicities and to make them into models, and these models are then The good models are able to extract just as much as they need out of the emerging structure, for example, and simplify it as such so that it does become predictable. And, and economics that is being done more and more, um, I mean, Don Farmer is a beautiful example of of extracting the, the regularities out of this seemingly terribly complicated system that makes it. Not deterministically predictable, but it, it makes us able to predict um. Probabilities. Mhm.

49:06 Ricardo Lopes: So, I have one last question. Then earlier on, we talked about uh the relationship between different, these different kinds of scientific fields like uh cybernetic systems theory, dynamical systems theory, and complexity science itself. But is complexity science one single scientific theory, or is it more of a collection of branches from different sciences?

49:36 Karoline Wiesner: We argue that complexity science is. Well, it's it's a scientific theory that relies on other theories. So what is, what does that mean? Um. I, I would say the and it. You know, 11 answer to the question is complexity science is a way of thinking. Which is why to link it back to the initial very early ideas of making it a teaching institution, I think is still so relevant. Because we're not thrown by um By the idea of stochasticity. Um, GENERATING order. And that is a way of thinking. And using models from other disciplines um for a system is a way of thinking and approach to science, and that was also the idea of the 1980s that we want a new approach to science, using statistical mechanics to study voting behavior, for example, is, is a new approach to the science of social systems. Um, And that, in that sense, one could say complexity science is not a single theory. Because it is. It is sort of, you can think of it as a a hub and spokes. So a hub would be all the different mathematical and computational tools that we have coming from physics, from mathematics and computer science, and the spokes are reaching out to the different disciplines. Um, AND out comes something like computational neuroscience, which connects mathematics and networks and uh concepts of computer science with theory of the brain. Um, WHICH is now its own scientific theory sort of emerged out of ideas from complexity science, I would argue. Um, SO that is a longer way of saying complexity science is not a scientific theory, um, it's a connection of scientific theories. It's, it's one way to think about it.

51:59 Ricardo Lopes: OK. Since we still have time, let me just ask you one more question then. I mean, since you mentioned earlier on that complexity science is a fairly young science, um, how do you look at its future? I mean, do you have any ideas about that, about what to expect from complexity science in the near future?

52:22 Karoline Wiesner: My outlook for complexity science is very positive. We are, I mean, we are in a in a phase where the problems we're facing are, are all about complex systems. We spoke a little bit about climate already, um, you know, we're in the midst of climate change, and that requires the concepts and tools of complexity science, um. And, and that is what is going on in, in research institutions uh that do complexity science. They, they involve complexity sciences more and more. But yeah. Um, THE same is true with social systems. Um, I mean, by now we are so many people on this earth that it's, there's hardly any community that's disconnected from the rest of, of, uh, of humanity. And that leads to Well, opportunities, but also to lots of problems, um, you know, how do we, how do we agree on using the resources that the Earth offers us. I mean, there's a connection between climate science and social science, um, and that to attack these problems, complexity science in my view is key because it provides ways of thinking about how many interactions, so negotiations between individuals, between individuals. Uh, COMMUNITIES, individual countries, um. Can lead to an outcome which we want, right? So the idea is here or the, the challenge here is to to not just describe what is going on between uh the different actors. But to provide models that can help not just predict also control. Control in the sense of achieving an outcome that we want. And there it's a huge challenge for complexity signs. And it's the challenge that we're absolutely Yeah, we, we have to take on and we are taking on uh to tackle. Climate change to tackle, you know, polarization in in the political systems that we're observing, uh, more and more, uh, writing the year 2025 and Um, Massive changes are are ongoing. So, the future of complex designs is bright in the sense that the problems we're facing are problems of complex systems.

54:55 Ricardo Lopes: Mhm. Great. So the book is again, what is a complex system. I am leaving a link to it in the description of the interview. And Doctor Wiesner, just before we go apart from the book, are there any places on the internet where people can find your work?

55:15 Karoline Wiesner: Yes, there is my personal website, Kavia.org, and there is also the website of my research group at the University of Potsdam, uh, which is uni minus Potsdam./complexity minusscience.

55:30 Ricardo Lopes: Great. So I'm also adding that to the description down below. So thank you so much again for taking the time to come on the show. It's been a really informative conversation.

55:42 Karoline Wiesner: Thank you very much.

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