Flocks of birds, swarms of locusts, schools of fish, in these seemingly chaotic groups of creatures, order miraculously emerges. The group behavior of different species varies in detail, but they roughly follow the laws of swarm motion that physicists have summarized over the centuries. Now, thanks to the latest technology, researchers are able to study the behavior patterns of these animals more closely than ever before. This article is a conversation between evolutionary ecologist Iain Couz·in and applied mathematician Steven Strogatz · applied mathematician who discuss animal group behavior and the reasons behind it, as a form of biological computation that can interact with each other to adjust the structure of the network and put the system in a critical state that is both flexible and stable. In addition, the group behavior of animals may also shed light on our understanding of brain intelligence: how the brain processes sensory information, simplifies complexity, and makes decisions.
撰文 | Steven Strogatz, Iain Couzin
Translation | He Anxia
Proofreading | Dynasty will
文章题目：How Is Flocking Like Computing?
Article link: https://www.quantamagazine.org/how-is-flocking-like-computing-20240328/
Steven Strogatz: Throughout the animal kingdom, from tiny flying insects to fish, birds, gazelles, and even primates like us, groups of organisms tend to form patterns of mass movement in pursuit of a seemingly spontaneous collective goal. Often, none of these biological groups look like leaders directing this massive movement. Instead, the animals simply line up seamlessly.
Despite the perceived chaos or instability of such a system, these groups are able to move in an extremely coordinated and targeted manner, as anyone who has seen a murmuration of starlings or fish swim can attest. So, what are the forces that drive this behavior? In this episode, we'll take a closer look at why animals behave in groups. How can the latest technologies such as artificial intelligence and 3D cameras provide new insights? What can studying animal behavior tell us about ourselves, both individually and collectively?
We enlisted evolutionary ecologist Iain Couzin to unravel these mysteries. Iain is Head of the Department of Cluster Behaviour at the Max Planck Institute for Animal Behaviour and a full professor at the University of Konstanz. He has received numerous honors, including the National Geographic Emerging Explorer Award, the Lagrange Prize, the highest honor in complex science, and the Leibniz Prize, Germany's highest research honor.
Are there common characteristics of fish, birds, insects, and animal behavior?
Strogatz: I guess we should start by talking about, who are you working with? What are some of the animals you study and the various swarm behaviors they exhibit in the systems you've studied?
Couzin: That's one of the most amazing things about the behavior of research clusters. Swarm behavior is essential to many life processes on Earth, so we study a wide range of organisms, from the simplest animals on Earth – placozoa, a basal phylum that is probably the simplest multicellular animal on Earth, made up of thousands of cells and able to move like a flock of birds or fish, to invertebrates, such as ants with surprisingly coordinated behavior, or the largest and most destructive locust colonies. to vertebrates, such as schools of fish, birds, hoofed mammals, and primates, including us humans.
Strogatz: So, it does seem to cover all the ranges, and I have to admit I've never heard of this before, Flat Disc Animal Phylum, right?
Couzin: Yes, the phylum Zoa. This small creature was found while crawling on the glass of a tropical aquarium. To the naked eye, it is about a millimeter long, and if it is large, it can reach a millimeter and a half. It is only recently that scientists have begun to pay attention to this particular creature. Because this peculiar small population of cells actually has a genetic complexity that would normally be considered to belong to more complex organisms. For example, despite the absence of neurons, it has a large number of neurotransmitters.
It has the Hox gene. In developmental biology, the Hox gene is implicated in complex body structures, but the phylum Platozoa does not have complex body structures. So you might think that this creature may have once evolved to a more complex form and then evolved again to simplify itself, thus retaining these complex characteristics.
However, genetics researchers published a landmark paper in the journal Nature that proved that this is actually the most primitive group of cells. Moreover, the clustering behavior of cells clumping together to form a living organism is one of the most wonderful examples. That's one reason we're studying it, trying to understand the central role of swarm behavior in the origin of complex life on Earth.
Strogatz: It was so interesting and novel for me, I was stunned. They have properties associated with the nervous system, but no nervous system? They have a developmental biology gene that helps evolve a complex body structure like Drosophila, but in reality, they don't have that body structure?
Couzin: Exactly, that's it. So, they can really give us clues about the origins of intelligence. Our 2023 study in PNAS shows that the body structures they possess do behave very much like a flock of birds or fish, with local interactions between cells and a tendency to align the direction in which they move. They are attracted to each other and are connected together like an elastic cloth, but also move. They have small cilia at the bottom, allowing them to flow through the environment. Align the direction of movement of the cells by applying force to the neighbors.
So, if we track these cells under a microscope and look at their alignment and inter-individual attraction, the techniques, models, and ways of thinking that we're using are very similar to what we would do with other types of swarming behaviors like birds or fish, just applying them to these animals. I think one of the most amazing things about cluster behavior is that whether it's cells or birds, even though the properties of the system are very different, when you look at the behavior of the cluster, the properties of the cluster, the underlying math is very similar. Thus, we can find universal laws that connect these seemingly disparate systems.
[1] Davidescu, Mircea R., et al. "Growth produces coordination trade-offs in Trichoplax adhaerens, an animal lacking a central nervous system." Proceedings of the National Academy of Sciences 120.11 (2023): e2206163120.https://dx.doi.org/10.1073/pnas.2206163120
Strogatz: That's what fascinates me with the study of swarm behavior, the universal mathematical principles that seem to apply at different scales, from the cell to ourselves as humans. You mentioned "flocks" and "schools" (fish), and sometimes we hear people talk about "swarms", like insects. Why do we have three different words for the same thing? When we talk about cluster behavior, aren't they really the same phenomenon? Is there a reason why we shouldn't say "schooling birds" or "swarming fish"?
Couzin: No, I think we created these words, and different languages have different vocabulary expressions. German is a language with a large vocabulary, but in reality there are relatively few related words. In English, we have many different words to describe swarm behavior, for example a flock of crows is also called "a murder of crows". You've just used a great word, "a 'murmuration' of starlings." I think it's the beauty of this fascinating clustering that gives rise to beautiful words that can be associated with specific examples ("flocking", "schooling", "swarming", etc.).
So I think it's very useful. Because just now I highlighted the commonalities in mathematics, but there are also differences. There is indeed a difference between a cell flock and a flock of birds. In order to understand these systems, we need to consider both their common principles and the differences between them. In a way, language captures these differences as humans naturally divide them into different categories.
Strogatz: Interesting. You mentioned "cell population" and "insect population", and I guess that's what you said, although we use the same vocabulary, there may be some differences between them. So, what are some of these examples that we should differentiate?
Couzin: yes, I think what's really exciting is why there are commonalities, because the differences are so profound. Animals have brains, they receive complex sensory information and try to make decisions based on their environment. Overall, animals are capable of exhibiting much more complex behaviors than cells. But the cells themselves also have complex internal processes. Their interactions are largely governed by physical forces, influenced by the scale of their action and the physical tensions that form in the cell population. For animals and birds, the interaction in the group is intangible, and there is no physical entity. So people will think at first that this is just an analogy. Actually, about five to ten years ago, I also thought it was just an analogy. I think these differences must be very important. But what we're starting to understand is that the common feature they share is computation.
These elements come together to compute the environment in a way that they can't do alone. Each individual, even if you have a very complex human brain, and you live in the world, unless you have social interaction with others, or go further, and this is only possible after the cultural complexity that has accumulated and established when we were born into life, our abilities will be very limited. So here are some deep and fascinating questions, and we're just beginning to explore the question of computation and the emergence of complex life.
Strogatz: That's a very interesting point. I don't know what you're going to say when you say they all have something in common. I can't guess, but I love it: calculation. This reminds me of a famous scene that you may have seen on YouTube or TV. It was a flock of birds, perhaps starlings, and suddenly a falcon or falcon was rushing towards them. Can you give us a picture of what happens next and explain why this example is related to calculations?
Couzin: Okay. As far as I'm concerned, if you look at these groups, when there's a predator that comes along and attacks them, whether it's a flock of birds or a flock of fish, you'll see that the flock behaves like an undulating fluid. You will see light or ripples passing through them. This suggests that individuals can actually spread information about the location of predators very quickly through social interactions. For example, at first only a few saw a predator, but by turning, this behavior was imitated by other individuals, and changes in density and steering spread extremely quickly through the group.
If we use advanced imaging tools to quantify and measure these steering waves, they propagate about ten times faster than the predator's maximum speed. So individuals can even react to predators that they can't see. It's a bit like neurons transmitting information through electrical signals. In this case, it is not an electrical signal, what really works is the change in density and the individual's turn, which gradually spreads out in the group, but which gives distant individuals information about the location of the source of the threat, allowing them to quickly start moving away from the threat.
Strogatz: I think that's a very vivid example of what it means to calculate in this context. We can see how the volatility of panic or avoidance flows through the flock. This is very interesting because it is much faster than an individual acting alone, and I guess, faster than the predator can achieve on its own.
Couzin: We think it's probably because although natural selection works on individuals, and the key is their individual adaptations, if the group acts in a certain way, the whole group will benefit.
This has to do with what we learn from physical systems, especially those that are close to phase transitions. So, a system that is close to transitioning between different states, such as between a solid state and a liquid state, if you're freezing water and it suddenly turns solid, around that transition point, the clustering behavior of the system is very significant. This bifurcation is your field of study. We now know unequivocally, and there is strong evidence that natural selection pushes the system closer to these bifurcation points, because of the significant clustering characteristics exhibited at these bifurcation points. When we first measured these properties, the individual's behavior seemed to defy the laws of physics, and its information traveled so quickly.
In the early 20th century, Edmund · Selous was a staunch Darwinist, but he was also attracted to the Victorian fascination with telepathy. He speculated that there must have been some kind of thought transmission or telepathy in the flock that allowed them to communicate so quickly. Of course, people will think, "This is ridiculous, how can telepathy exist." "But actually, despite the controversy that may exist, I think we still don't have a good understanding of sensory patterns and the way this information travels so quickly through the system.
I'm certainly not suggesting telepathy. What I'm trying to convey is that by adjusting the population system to get close to a tipping point, or a bifurcation point, some significant cluster features may emerge. To the observer, this looks quite fantastic. The physical phenomena in these fields are so bizarre, mysterious, and amazing, although science can explain them.
Related Reading:
Books by Selous, Edmund (sorted by popularity)：http://www.gutenberg.org/ebooks/author/45735
Why are groups at tipping points?
Strogatz: So I'm thinking, in terms of swarm behavior, if nature adjusts a group of animals to something close to some sort of instability or borderline, do you think that's one of the reasons why swarms are so effective?
Couzin: yes. For example, in one of our 2021 papers, we explored how to get the best results in a variety of situations. In general, you want to be stable and robust, but sometimes, you need the system to be highly sensitive. In natural selection, biological systems must balance this seemingly contradictory state, both robust and sensitive. So how is it done? We believe that this can actually be achieved by adjusting the system to near a tipping point. Because if the system deviates, it actually stabilizes itself. But when it is pushed to that tipping point, it becomes very flexible and extremely sensitive to inputs, such as a predator.
If a school of fish moves away from that tipping point – for example, if they are very closely aligned with each other – when they detect a predator, it actually takes a lot of effort to get all the individuals to turn. They influence each other so strongly that it is difficult for external inputs to change their behavior patterns. If, on the other hand, they are very chaotic and each fish moves in a different direction, then a fish changing direction will hardly be noticed by other individuals. Therefore, this change does not propagate in the system. Thus, in this intermediate state, they can actually optimize the ability to act as a group, both flexible and informative. It's a theory from physics, but the real use of computer vision to track how animal groups change the way they interact when they're in danger is a recent thing.
As biologists, we often think, "If the world becomes dangerous and unstable, I will become more sensitive to incoming information." I would become nervous and more prone to false positives. "This is true for animals or humans who are acting alone. But when we tested this theory in groups, because these groups evolved in a cluster setting, we found that this did not apply to them. What the group does is change the network, the network of connections through which information flows through the system. They adjust the network to balance flexibility and robustness, i.e., to adjust the system to the critical state we predict.
Related Reading:
[2] Sridhar, Vivek H., et al. "The geometry of decision-making in individuals and collectives." Proceedings of the National Academy of Sciences 118.50 (2021): e2102157118. https://dx.doi.org/10.1073/pnas.2102157118
Strogatz: What kind of animals did these studies take place on?
Couzin: Our research was mostly done on small, gregarious fish because they needed to solve the same type of problems — avoiding predators and finding suitable habitats. And these fish are easy to handle in an experimental environment. In fact, fish have a chemical called "schreckstoff", which literally translates to "scary thing" in German. When a predator attacks a fish, this chemical is naturally released. So we can add frightens to the water, so that even if there is no information about the location of the predator, the individual's judgment of the environment will change, and the world will become more dangerous.
So what do you do? Do you change the activity in your brain? Or is it changing the way we interact with the environment? Or, as we usually think animals do, become more fearful? Or, you can imagine that in a network system or a cluster system, would you change the topology of a social network? Change the way you communicate with others? Because this also affects the ability to react to threats, like the steering wave that we discussed earlier.
We found that the individual did not change. It's the web that's really changing. Individuals change the structure of the network by moving, and this change makes the group suddenly more sensitive and flexible. Individuals who used to think of each other interacted more closely with each other. But you can imagine that in everyday life, you may sit next to a stranger in the bus, and in fact you do not form a strong social relationship between you. As a result, an individual's social network can be very different from a network that is easily measurable.
So what we're doing, it's actually quite complex, and we can reconstruct the world from their perspective. We used a technique from the field of video games and computational graphics, called ray casting, which projects light onto an individual's retina so that a computer can see what they see at each point in time. But the problem is that we don't know exactly what they do with that information.
Therefore, we can use machine learning methods because every brain evolves for the same purpose. It receives complex sensory information – just like those who listen to us today. It's a complex sound message, but they may be driving or cooking, so at the same time they have to process complex visual or olfactory information. But their brains have to simplify all this complex information and reduce it to decision-making, or to decide, "What am I going to do next?" ”。 We don't know much about how real animals do this process. But we can reconstruct their field of view, and then we can use the same type of techniques to reduce dimensionality, understanding how the brain reduces these complexities to motor decisions.
The fish we studied had only a small number of neurons in the back of their brains that controlled all the movements. Therefore, the brain has to take all this complex information, simplify it, and then make a decision. I think it's a very interesting question in biology: how does the brain do this process?
Strogatz: First of all, I can say definitively that I need to read your paper more often. You mentioned seeing what fish see by casting light on their retina, or giving us a sense of what they're looking at? Am I right?
Couzin: Actually, it's not really projecting light, it's all digital. Imagine that at one point, you took a snapshot of a school of fish. Our software can track the position and posture of each fish. We can then create a three-dimensional computer version of this scene, just like in a video game. Next, we can ask, what does each fish see? So we can place a virtual camera in each fish's eye.
So, raycasting is a bit like ray tracing in computer graphics, which is to trace the path of light on the retina. We're all doing it digitally, so we can create a digital simulation of reality. We can see how light falls onto the retina in a virtual scene, a photo-like realistic observation. This gives us the first layer of information: what is the information that the individual receives?
Of course, the important question we want to ask is: How does the brain process this information? How does the brain simplify this complexity and make decisions? For example, schools of fish and birds can move so easily and gracefully, with little to no collision, while cars on the road struggle to move in groups? Will we be able to learn something from thousands of years of natural selection and apply it to vehicles and robots? Therefore, there is also application value in trying to understand this. I want to understand it mainly because I find it fascinating, but at the same time, in some cases, this can really translate into real-world applications.
Herd behavior of locusts
Strogatz: I'd like to go back to what you mentioned in your introduction, from cells to primates. You may not be very familiar with the example of locusts, and I wonder if we can talk about the real-world and even economic impact of swarming behavior, because locusts have a significant impact on the world, much more than I thought. I saw some statistics that in locust plague years, locusts invaded more than one-fifth of the world's landmass. They affect the livelihoods of one tenth of the planet's population. So, can you tell us a little bit about the research and how it relates to global food security?
Couzin: Yes, you're absolutely right. I was also very surprised. As you said earlier, they affect one tenth of the planet's population by causing food shortages and food insecurity. And this often happens in countries such as Yemen and Somalia, which themselves have major problems, major conflicts, civil wars, etc. Due to climate change, the range of locusts is expanding. As a result, the food-producing regions of Afghanistan are currently facing a major crisis. Madagascar suffered such a catastrophe a few years ago. A year or two before that, Kenya experienced its largest locust invasion in 70 years.
So why do locust plagues become more violent and severe when we have all the modern monitoring technology at our disposal? One reason for this is climate change. This is how the locust plague is formed - and the audience may be surprised by this, but in reality locusts do not like to be close to each other. They are shy, secretive green grasshoppers that prefer to be alone. So if there is plenty of food, keep them apart from each other and avoid contact. Only when they are forced to come together will they switch states. So they are often referred to as "solitary types" because of their solitary lifestyle. But if they are forced to gather together, they will evolve the ability to shift quickly, and within an hour they will quickly change their behavior to a social type, starting to follow each other and move closer to each other.
Another thing that you may not know is that locusts don't actually have wings for a few months after birth. So when locusts are born, they can't fly. These flightless hatchlings only grow wings when they reach adulthood. So, when rain falls in Africa, India or elsewhere, there is lush vegetation, and small swarms of locusts can thrive as hidden grasshoppers, and the population size grows rapidly. As the population grows, they eat more and more, often accompanied by drought.
If the population density is high and all of a sudden the food is gone, then the locusts will change to a social type and start moving together and moving together. These swarms of locusts can be billions of them, and everywhere they see are locusts acting in unison, as if they have a common goal. Once they have grown wings, they can fly. The situation can be made worse by the fact that they can use trade winds or other environmental conditions to migrate long distances, forming large groups over a range of hundreds or even thousands of kilometers. This is one of the largest and most destructive swarm behaviors on our planet.
Strogatz: I can't say I'm very familiar with the locust march. We are used to imagining them as clouds flying in the sky. But please share a little bit more about the locust process, I vaguely remember you having an amazing study of locusts, including the cannibalism between them. Is this term right?
[3] Collective Motion and Cannibalism in Locust Migratory Bands：https://dx.doi.org/10.1016/j.cub.2008.04.035
Couzin: yes, that study was done in 2008. We don't know much about these large swarms of locusts that can migrate long distances, whether you call them swarms or clouds, because we don't have the technology to study them sufficiently. In fact, we still lack the technology. So, it's not that it's not important, it's that it's extremely important. But we also know that before these flying locust swarms appear – flying locust swarms are a bit like wildfires that have gotten out of control, and once they start raging, they are hard to control. But if you control them before they grow wings, while they are still forming colonies in deserts or other environments, there is a good chance of success.
So, for practical reasons, we focused our research on these wingless locust swarms. In fact, you're right, I started working on this in the mid-2000s, and now I'm back to studying locusts. Earlier this year, we created the world's first truly laboratory locust colony. We set up a 15m×15m×8m imaging environment in Konstanz and tracked 10,000 locusts in it. So it's interesting that you brought up this topic because my research is now back to the system.
But, as you said, the question we find is, why do these insects travel together? We initially thought they must be like schools of fish and birds. This definitely has to do with information, it must involve cluster intelligence. However, we were wrong. This perception carries significant risks. If you see a swarm of ants moving, forming a circle, as if rotating; You see a school of fish spinning, forming a ring or doughnut-like pattern; Or you see a whirlwind, and the patterns all look the same, but they can be driven by very different phenomena. I think I've been misled into thinking that when I see a cluster of motion, there must be a similar process at work. But in locust swarms, this is not the case, it is not the messaging that is at work. In fact, in these desert environments, when there is a sudden shortage of food, you will be in dire need of basic nutrients, especially in the desert, including protein, salt, and water.
What could be more suitable for you than another individual in such a hostile environment? Because they have a perfectly balanced nutrient profile. So these locusts will be attracted to each other and tend to prey on each other. They have evolved to follow the behavior of those locusts that are leaving, and try to bite the back of their abdomen, which is difficult to defend against. The head is protected by heavy armor, but the back end of the abdomen is a weak point, apparently because it is easier to attack there. As a result, they attack this weakness while also avoiding themselves from being targeted by others. Following those who flee from you and dodging those who approach you, this behavior causes the entire locust swarm to begin traveling together through the desert environment.
They also benefit by leaving undernourished areas together. Because if you put a person in the desert, it will be easy for the person to get lost and wander around. The same applies to locusts. But if they are in groups, collectively aligned and synchronized between individuals, hundreds of millions of individuals aligned with each other, they can leave these nutrient-poor areas very directionally. They can also overwhelm predators. Predators have little to do here.
Strogatz: How did you get interested in these studies when we were discussing these examples, and how did you get started in the early days? You mentioned that it was in 2008? You've been working on this before then, right?
Couzin: yes, I did my PhD research on ants in the late nineties. I'm fascinated by the behavior of ants. To be honest, it started with my love of nature and my obsession with naturalism, where I wanted to observe everything around me. When I was a child, I thought that there must be experts who could explain why swarms of locusts, schools of fish, flocks of birds, etc. were formed. I think it's something that everybody is looking at. I was an artist as a kid and was very interested in creative writing, poetry, and art. So I was initially attracted to the purity of these things and was fascinated by their beauty.
In high school, I wasn't a good student in science. I'm doing pottery and painting. When I was in college, I remember my dad saying to me, "Son, you should do what you're good at." Learn English or art. You're not a scientist, but you're a nature observer. He was right. Later, when I was doing my biology degree, I knew in my first biology class that this was the right thing for me, and I was convinced. I entered the world of statistical physics. The papers published during that time completely opened my mind to the esoteric mathematical principles that permeated the various systems.
My PhD supervisor told me that in order to get a job, you should become a world expert on a certain species of ant so that you can be valuable. But I've read that some scientists have done the opposite. They have studied a wide range of things, from physical to biological systems, and have seen the principles behind them. Moreover, the patterns, structures, and results they found were natural and wonderful. So I guess this must be the right way to do scientific research. So that's when I was drawn into the world of physics.
Strogatz: Did you have a chance to talk to your father about the change in your research direction?
Couzin: I never thought my father remembered it. Then, when I was promoted from assistant professor to full professor at Princeton University, the department chair called me and said, "Congratulations, Professor Couzin. "You know, I was completely shocked at that moment. So I immediately called my father and mother to tell them the good news. It turned out to be a phone call from my father, and then he said, "Remember that I used to call you a nature watcher." "That was the only time in decades. I never knew he remembered this conversation.
Group behavior helps to understand the brain's spatial decision-making
Strogatz: It's a really good story, a great story. In this show, we like to discuss some big questions that have yet to be answered. So, in your opinion, what are some of the biggest unanswered questions about flocks of birds, flocks of fish, and flock behavior?
Couzin: Absolutely. Which brings me to a topic that I'm very excited about right now. Early in my career, I used to think that the brain was a very wonderful cluster computing entity – one of the best examples. So, how does the brain make decisions? It is made up of neurons, and we can see that various individuals such as ant colonies, locust colonies, bird colonies, or fish colonies interact to form a system. So, is there some deep connection between these different systems? What fascinates me at the moment is the problem of group decision-making, especially in space.
So, how does the brain represent space and time? Why is this important in decision-making? What does this have to do with the group's behavior of animals? About five years ago, I realized that I thought there was a deep mathematical similarity, and that there were also deep geometric principles about how the brain represents space and time. One of the most exciting aspects is the reuse of mathematics. You know, I gave up math at 16, but I had just taken an academic sabbatical as a distinguished fellow at the Isaac · Newton Institute for Mathematical Sciences at Cambridge University. However, I don't know how to solve equations, you know?
I love working with great mathematicians. By working with physicists, mathematicians and biologists, and experimenting with animals in virtual reality – we have built a system of technology. We can't put a headset like the Meta Quest 3 on a fish that's less than a centimeter long, but we can create a virtual, immersive holographic environment, so we have full control over the input. That is, we are able to have complete control over cause and effect.
If you know, I'm influencing you, and you're influencing me, and then there's a third person involved, are they directly influencing me or are they indirectly influencing me through you? Or both? Or when it comes to the fourth or fifth person? In our virtual reality environment, we can put these individuals into a world like the movie The Matrix, where each individual interacts with the holograms of the other individuals in real-time in their own holographic world. But in this world, we can adjust the rules of physics at will. We can even change the rules of space and time to better understand how the brain integrates this information.
So, it really blew my mind because we could show that the brain doesn't represent space in Euclidean terms. It uses a non-Euclidean coordinate system to represent space. And then we can explain mathematically why this is so important, because when you start dealing with three or more options, you actually distort space-time so that space becomes a non-Euclidean form that significantly reduces the complexity of the world and transforms it into a series of bifurcations. Near each bifurcation point, it magnifies the differences between the remaining options. So there's a wonderful internal structure here.
So we think we've found a universal theory of how the brain makes spatial decisions that we'd never get without studying the behavior of creatures like fish, locusts, and flies in these types of virtual reality environments, and that's why I'm so excited.
Scholar Profile
Iain Couzin is Head of the Department of Cluster Behaviour at the Max Planck Institute for Animal Behaviour in Germany and a full professor at the University of Konstanz. His research aims to uncover the fundamentals of evolutionary swarm behavior, studying a wide range of biological systems from insect populations to fish schools and primate populations. In recognition of his research achievements, he was awarded the 2019 Lagrange Prize (the first and most important internationally recognized award in the field of complex science) and the 2022 Leibniz Prize (Germany's highest research honor).
Personal homepage: https://www.ab.mpg.de/person/98158/2736
Podcast host
Steven Strogatz，Susan and Barton Winokur杰出教授，Stephen H. Weiss 总统学者，康奈尔大学数学系教授。 研究重点是将动力系统研究应用于物理学、生物学和社会科学。 出版书籍包括《非线性动力学与混沌》（Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering）《微积分的力量》（Infinite Powers: How Calculus Reveals the Secrets of the Universe）《同步》（Sync: The Emerging Science of Spontaneous Order）等。
Personal homepage: https://math.cornell.edu/steven-Strogatz
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