Wisconsin Public Television University Place  Iain Couzin The Principles of Collective Behavior  November 14, 2012 UP810   cc >> Thank you so much for coming. Welcome to the DeLuca Forum in the Wisconsin Institutes for Discovery. I'm Jessica Flack, the co-director of the Center for Complexity and Collective Computation, and before I introduce tonight's speaker and the topic, I'd like to take care of a few notes of thanks. First, thanks so much to the people in WID who make these lectures happen. That includes Maria Peot, Marianne English, Sarah Shapiro, Jenny Eagleton, and Beth Misco. They're WID's outstanding administrative team. And I'd also like to thank the IT team, and Alan Ruby in particular, here at WID for their support tonight, and also Wisconsin Public Television, which is in the back filming and has been filming a number of these lectures. And, finally, the Center for Complexity and Collective Computation is indebted to John Wiley and to the John Templeton Foundation for critical financial support of the lecture series. It's support like that that makes these lectures happen, so we're really grateful. So, tonight's lecture is the third in the John von Neumann Public Lectures in Complexity and Computation, and the lectures, just so you know, run roughly once per month during the academic year. 

The next one is December 12, by Seth Lloyd who's a physicist from MIT, and he's going to be talking about the past and future of quantum computation. I think it's going to be a great talk. So please, please join us for that. Okay, so back to the present. The themes of this lecture series, as the title suggests, are complexity and computation. And complexity is a focus because a goal of much of the research in C4, and more generally in the WID, is to account for why life is hierarchically organized. So, why cells form tissues and tissues form organ systems and organs form individuals and individuals live in societies. And we're also interested in how the many beautifully ordered living systems that we see in nature have arisen over the history of life. Computation is a focus because our results and data increasingly indicate that nature computes. So, you often think about experimental scientists performing computations on data sets, but the data also suggests that nature computes. 

And what I mean by that is that we think living systems, through their evolution and development, organize themselves into hierarchical multi-scale systems to minimize uncertainty. And by minimizing uncertainty, they allow the components, or the individuals in the case of society, to develop better strategies for getting by in the world. And tonight's talk is about how structure the group level arises from individual behavior. So, how beautiful patterns form at the group level and how this is a consequence of decision-making by individuals. And so it fits really nicely into the computation and complexity themes. Now, the talk's going to cover how, for example, three-dimensional structures like schools of fish and flocks of birds form these beautiful three-dimensional structures that might seem to require incredibly complicated coordination and communication but, in fact, can emerge from relatively simple rules and individuals using only local information, which is maybe kind of surprising. Now, I'll just say before I introduce Iain, our speaker, that this kind of problem is often viewed as a problem maybe unique to animal behavior and in particular to how organisms that form collectives that move through space, like fish and birds, but when one steps back and thinks about the problem and some of the more fundamental issues, I think one realizes that the problem is actually a really deep one in the history of life. And that is, consider the fact that our bodies are collections of cells. Our brain, for example, is a collection of neurons, a collective if you like. Consciousness, the ability to reflect, that's a consequence of these neurons interacting in all sorts of interesting, complicated ways. 

We don't know how yet, but that gives you the sense of the depth of the problem. Societies, of course, are collectives of individuals. And sometimes the collective behavior is really easy to see. It's easy to observe, as in the case of a fish school. You can see that school moving its trajectory through space. You can measure its velocity. And sometimes the collective behavior is actually quite hard to see. Sometimes it's almost an invisible reality, as in the case of social structures, like power structures, that have to be inferred, or that has to be inferred. So whether it's invisible or it's beautifully visible, in the case of these incredible patterns that we see in nature, relatively little, if you can believe this, is understood about how coordinated aggregations of components, whether they're cells or animals, monkeys, primates, or humans, how they form. And we don't know why there's an incredible diversity of forms in the world either. 

So, tonight's speaker, Iain Couzin, is going to tell us about some of the cutting-edge work on these issues that's been coming out of the Princeton Collective Animal Behavior Group that he runs. Iain is a professor in the Department of Ecology and Evolutionary Biology at Princeton, and he received his PhD from the University of Bath in England and was recently named by the magazine Popular Science as one of the 2010 Brilliant Ten. So that's young scientists who are "dreaming up the solutions to some of the world's most vexing problems." And this year, he was a recipient of the National Geographic Emerging Explorer Award. So Iain gives a great talk. It's my pleasure to have him here tonight, so please join me in welcoming him. [APPLAUSE]  >> It's great to be here. I've never been to Wisconsin. I've never been to Madison, Wisconsin. It's been absolutely wonderful so far. So, I would like to talk to you today about collective animal behavior, and ever since I was a kid I was fascinated by TV programs that showed footage like this. 

Here we see fish schooling on a reef and birds flocking in the sky. And as was said just previously, we still only have a very rudimentary understanding both how and why animals like this coordinate their behavior into these remarkable patterns. But collective behavior is all around us and also within us. So these are the cells that heal your wounds if you cut yourself. They have to get into collective action to heal before infection sets in. So by understanding collective patterns at one level, we may be able to understand other levels. And in my lab, we want to try to understand the principles, the common underlying principles in nature. So we study a range of organisms, we study cells but we also study organisms like these ants, and this is real data, real ants forming dendritic foraging trails, and this is probably the most complicated organism we study. These are humans. And we've been performing manipulative experiments in train stations and in streets where we have actors in the crowd to subtly, unbeknown to you, manipulate your behavior to try to understand. 

We think of ourselves as very intelligent organisms, but perhaps when we're in crowds, we're not quite so. And, really, what we want to understand is how the brain works which is a collection of cells performing these types of computations. So how can we scale from individual level of cognition up to collective cognition? And so in today's lecture, I hope you learn a few things. I hope you learn what this is and why this was important to my life. [LAUGHTER] I also want to tell you something about leadership. Leadership within animal groups, and perhaps also leadership with our own societies, and also about democracy. So, I'm going to start with a very dramatic example of collective dynamics and collective behavior, and that's mass migration in insects. And so, what am I thinking of here? I'm thinking of organisms like this. This is the desert locust. You've probably seen footage like this. 

And if you haven't, you almost certainly will do this year. This year is looking to be one of the worst years for a long time. And so these gigantic swarms of insects that really influence the livelihood of people in these areas. And, just to give you a sense of scale, I did my field research in west Africa in Mauritania, and I'll show you some pictures of that shortly. This one species of locust, the desert locust can invade up to one-fifth of the Earth's land surface during plague years. I can't think of many problems that are this large that nobody is studying. And the FAO, the Food and Agriculture Organization of the UN, estimates the impact of locusts on humanity to be one in 10 people on the planet are influenced by locusts, through food availability principally. And yet, in the premiere magazine Science, it was admitted that even after 50 years of fighting locusts, our understanding is really more of an art than a science. And that's really embarrassing to scientists because the individual locust, taking an individual into the lab, people have been doing that for a very long time. It's one of the best studied organisms for physiology and neurobiology. 

But the last time someone took locusts into the lab to see how they swarmed was a woman called Peggy Ellis in the early 1950s, and she did groundbreaking research but published it in the  locust bulletin. [LAUGHTER] Which not many people are aware of. In fact, we are very lucky that when Peggy died her family donated her papers to our lab. And so that's how we were aware of her work. So, I want to ask some basic biological questions. Why do insects, such as locusts, perform these mass migrations? And what are the biological processes that underlie it, both in terms of how they do it and in terms of why they do it? So, real key questions that we don't yet understand, or we didn't understand. And so, you saw flying swarms before. Well, locusts actually only get their wings in that adult stage of life. Right at the end of their life they have wings. Prior to that, and you can see some hatching out here in our laboratory, you can see they don't have wings. 

They only get wings for that last dispersal stage. And so this is really the critical stage. It's called hopper bands. These sort of two-dimensional bands. It's the critical stage for locust control. So it's very difficult to study these bands in the field. But, fortunately, these are not the smartest animals out there. So we can put them in this racetrack, this sort of arena. It's like a locust particle accelerator. [LAUGHTER] And we've developed software. 

I developed the software but the experiments were mostly conducted by Jerome Buhl, a postdoc in my group. This gives us the detailed information that we need to infer the types of interactions that they exhibit. And what do we see? Well, we've reduced all of that information about how they moved down to a single value which is called average alignment. An average alignment of one means the locusts are all marching clockwise around the arena. An average alignment of minus one, on the other hand, is all locusts marching counterclockwise around the arena. A value approximating zero is the locusts moving in both directions. And so, if we look at a low density of locusts on the far left, they behave much like particles in a gas. They're kind of wandering around, bumping into each other, but we don't see the fluid-like motion you see with the swarms. And this is for eight hours, which is a typical day in the life of locusts. So it's very nice to have the computer software get these data for us because it's incredibly tedious to watch this happening over an eight-hour period. 

[LAUGHTER] We did this, I think, for 60 days. In the middle there, what you're seeing is an intermediate density of locusts. And here, they go counterclockwise and then suddenly transition to clockwise and then transition back to counterclockwise. This is called intermittency. So, they exhibit a stable behavior, and when you look at it, you think they're never going to change, and then somehow it gets triggered. And the last figure, on the far right there, shows the locusts going clockwise for the entire eight hours, and that occurs at high density. And so, we thought to ourselves, how can we understand this? And we got inspiration from something that might sound quite unlike locusts which was how magnetic particles align with each other within a ferrous magnet. So, why is a magnet magnetic? It's because all of those little microscopic particles are aligning with each other. And if you heat up a magnet, you're adding thermal noise. You're causing them to jitter around. 

And if they're jittering around and they're trying to align with neighbors who are jittering around, you suddenly reach a temperature, so this system is magnetic, you heat it up, it's still magnetic, you heat it up, it's still magnetic, and suddenly you hit a temperature called the Curie point at which the magnet suddenly loses it's magnetism. And that's shown on the left-hand side there. The disordered magnets. So you might think, well, what's that got to do with locusts? Well, can we think of locusts and model locusts as magnetic-like particles that are on the move? And so, our model assumes that individuals, the blue one in the middle, interacts only with local neighbors, individuals in a local neighborhood, which here is indicated by the bright green. And their tendency, like the particles in the magnet, is to tend to align their direction of travel with them. And so, we have a sort of moving swarm of magnetic particles. And what we've found was that this very simple representation inspired by magnetism allowed us to extremely accurately predict and understand how the system undergoes this transition from a gaseous sort of behavior to this sort of driven fluid type of behavior. And it did not rely on the individuals changing their behavior. Just increasing the density of locusts increases the number of interactions which causes them to become aligned. So the individuals don't need to measure their density. 

They don't need to be able to count how many locusts are around them. This will spontaneously happen. Just like the magnet spontaneously loses or gains magnetism as you heat it up, locusts will spontaneously swarm above a critical density. This tells us something important which is that local interactions between these individuals can scale over very long distances. So it explains to us how locusts swarms can be 10 kilometers long even though they can only see over a 10- or 15-centimeter range. But it didn't tell us why the locusts aligned with each other. And this, as is often the case in science, was actually discovered through an accidental observation. And the work was mostly done by my graduate student Sepideh Bazazi. She started this when she was an undergraduate. Because what happened was I was putting the locusts into this arena in the morning, and my postdoc, Jerome, whom I showed you a photo of before, was taking them out eight hours later. 

And Jerome would say to me, Iain, we were going to do 50 locusts today, but you only put in 49. I was like, sorry, Jerome. The next day I had to put in, say, 120, and Jerome said, no, there were only 112, or something. Goodness me, surely I can count. And started being really careful about making sure I was putting the right number of locusts in. And they just were disappearing. And we checked, the apparatus was meticulously made. It wasn't possible for them to be escaping. What happened was, going back to the video, I saw these locusts eating each other. Coming and biting each other and eating each other. 

And this is not what you expect. You think of these as vegetarian insects. And so they have these aggressive interactions. So we then thought, well, perhaps the way we've been thinking about this system is very different to the selection process that has been driving its evolution. And this is the type of damage that you see to locusts that don't get completely eaten. And for a desert insect, even to have a bit of damage, like the one on the left there, can be deadly. And so, we performed experiments where we located the nerve, we cut a tiny little incision in the belly of the locust, and we located the nerve that gives the locust sensation to its abdomen. And we could cut the nerve so it can no longer feel others biting it. And we had what are called control insects, and in controls we'd perform exactly the same procedure. We find the nerve but we don't cut it. 

And then we seal up the little hole and cover it with wax, and these are very robust insects. They survive this pretty well. Our computer tracking shows that their behavior on their own in isolation is identical. However, if we put swarms of nerve-cut individuals together, we could show, they're shown in red here, that they wouldn't swarm. Their behavior was indistinguishable to individuals not interacting. So we removed their capacity to swarm by preventing them from feeling the biting behavior from others. We also performed an experiment on the visual system. And this is one of our most high tech experiments. At the top there you see a normal locust with perfect vision. In B, you see a locust with black at the front half of his eye because we've painted it with black modeling paint, so it can't see those ahead. 

In C, there's a locust that's had the back part of his eye painted, or her eye painted, so she can't see those approaching from behind. And the poor locusts in D are completely blind. And we can then create swarms of these insects to ask, is it information from ahead or is it information from behind that's important? And the key thing to note on this particular figure is the orange on the far right are totally blind swarms, and the green just to the left of that are individuals that can see perfectly well ahead, but they can't see from behind. If you can't see from behind and you can't feel the biting from behind, you don't swarm. There's also a slight pull towards those in front of you. And so I went out to Mauritania to try to test some of these ideas with locusts in the field. And it's actually surprisingly difficult to find locusts in this type of environment, but we did. We managed, thanks to colleagues there. In fact, that's one of the colleagues, and that's a camel. [LAUGHTER] And you can see footprints in the sand in the background. 

The locust footprints in the sand. It's not often you do field research with a camel, and so I got quite attached. [LAUGHTER] Unfortunately, it turns out I'm not very good at planning. This is something my mother's been telling me for a long time. But here, we've ran out of food, and also, given that there was a locust plague, we didn't really realize there would also be a famine. So despite the fact we have plenty of money, there was nothing to buy. The only we food we could buy were these camel entrails, and I'd been a vegetarian for a decade prior to this trip. [LAUGHTER] And, if you look down here, there's a fly laying its eggs. And we dried it in the little tree beside the campsite until it became like a jerky, and then we ate it. And I became horrifically ill and hallucinating, and not in a good way. 

It was awful. [LAUGHTER] And then, after about two weeks, I started feeling a bit better. [LAUGHTER] And you can see how well prepared I was. This tent cost me $50. [LAUGHTER] And the tent pegs didn't go in the sand. My Mauritanian colleagues had to give me these tent pegs. And this is the same tree. We're resourceful biologists. We also dried out clothes in this tree. And this is obviously the Mauritanian tent. 

[LAUGHTER] But what you're seeing in the background there is a sandstorm coming towards us. My tent stayed up. They were all laying on top of theirs in the morning. This is the sandstorm. Not a sandstorm, this is the same sandstorm taken from NASA's website. And it was a disaster. I was there for two months. I got 20 minutes worth of data, and I've been too traumatized to ever look at those videos. [LAUGHTER] And so we came to the good old US of A, where there are things like roads, to work on a very comparable system, which are called Mormon crickets. You may have heard of these, and you can see one near the front there chowing down on one of his buddies before carrying it off because it's dangerous to stop. 

So they'll try and find something to climb to then eat the other insect. So, again, these so-called vegetarian insects would eat the ears off this jackrabbit that normally ran the length of the body, and you can see them crawling in through the eyes and in through the mouth. And they'd also eat each other. And so, we created artificial diets. The P stands for protein and the C for carbohydrates. And you would expect them to go for carbohydrates being vegetarian insects. And the zero there, the O, is actually a neutral diet that's got no nutritional value whatsoever. But you can see no difference between their preference for carbohydrates and this neutral diet. And as you increase the amount of protein in the food, these crickets have an increased preference. And I like this. 

This is a sort of living histogram. Of course, we've randomized it in the real experiments. But what we're looking at is crickets feeding on water with no salt in it, all the way up to very strong, two-molar concentration of salt. And they taste it with their feet and fight over it. And you see them fighting over .25-molar salt concentration which is also represented in that graph. Of course, that turns out to be the concentration of cricket blood, the salt concentration. So they're finally tuned to a cannibalistic lifestyle. So we have a new mechanism of collective motion that we call a forced march. Individuals are trying to avoid being attacked from behind and are attacking those ahead. Stop and you risk being cannibalized. 

And I've jumped to a different system to these crickets, but we've also gone back to show this mechanism works in the locusts. I just want to reiterate why this is such an issue. We didn't know why locusts swarmed. If we don't know why they do it, there's no hope for control measures or for trying to understand why we get the sort of situation that we're in at the moment where, across a large swath of Africa, there are swarms emerging. And as I said, this year could be one of the worst for a very long time. But the fact that everyone is eating each other and trying to avoid being eaten doesn't make this a particularly good organism to try to study collective intelligence, which was my original interest. So even though it was fascinating, I also want to study collective cognition. We're very familiar with individual cognition, but is there something about being in a collective that gives us extra capabilities? And when we look at groups like this, we can see why we need to use computational techniques. The human brain is not very good at trying to interpolate what happens when you have many of these interactions occurring at similar times. And as a biologist, I want to try and get inside the heads of these individuals to work out why they group in the way they do. 

And so, what we tend to use to gain insights, to gain some understanding because our brain isn't very good at thinking in this way, is we use computers, really, as an extension of the brain to simulate ways in which these individuals interact with each other. So I'm going to discuss with you how we do this type of simulation. We assume that individuals exist at positions in space and they tend to move. And we can then simulate, for example, if individuals get too close together that they have a tendency to avoid collisions. That's all this equation tells us, that they're trying to avoid bumping into each other if they get too close. Similarly, if they get far away, they might want to be attracted, ought to align their direction of travel, like the locusts and their little magnetic particles, with each other. So we can speculate, and why do we have to speculate? Because when I started this, there was no experimental data. None. Zero. And so we start off with this computational model to try to understand how do these interactions scale to group dynamics. 

And so, in this particular model, the ZOR, in the center there, is that zone in which they want to repeal from others. The Zone of Repulsion. The ZOA, which is the largest zone, is the tendency, if you have individuals in that zone, to be attracted to others. And the ZOO is the tendency to orient, to align your direction of travel with others. And what we can do is we can play around with these zones because animals can change these dynamically to see how they group. For example, if we just have repulsion and attraction, there's no ZOO, there's only repulsion, the ZOR and the ZOA, if we start with that, what we see on our computer screen, this is a still image from the simulation, is a sort of mosquito-like swarm. And what we're now going to do is introduce a tendency for individuals to align direction of travel with near neighbors. And what we see at the group level is suddenly a transition into this bagel-like formation called a torus. There was nothing in the rules that said go in a circle. This is an emergent behavior that results from those interactions. 

And one of my friends took this simulation and produced a really nice graphic of it, and I took this video from YouTube that shows us real animal groups undergoing these types of transitions. Similarly, we also see, for the same behaviors, for the same individual behaviors, with that local zone of orientation, we see sometimes another pattern, which is a tendency for the group to exhibit what we more typically think of as a fish school or a bird flock. And so what we can do now, since I went to Princeton, is to try to get data from real systems to test these types of ideas. And you see some video speeded up here of golden shiners. These are the fish that we tend to use in our lab. And they love schooling in shallow water. This is only around 10 centimeters deep, and it's around seven feet by four feet, the size of the tank. And to then get the quality of the data we need, again we can't resort to observing them. Like we did with the locusts, we've developed software that can track all the individuals simultaneously and even deal with occlusions when they're going over each other. So we can get very comprehensive data of both the positions of individuals, their velocities, and so on. 

And what we see is as we predicted in the simulation, there are sort of three fundamental states of matter. And these are exactly the states and the only states that we've ever observed in many, many hours of experimental study. And we can even do better than that. We can track not just the positions, we can also track the positions of their eyes and use computational techniques, such as ray casting, to reconstruct the visual field from the perspective of every individual in the group, 60 times per second. Actually, up to 250 times per second. And here I just show you one individual moving through the group with its visual field reconstructed. And why this is important is we now can look at the network that actually is a network of social interactions that they use to make their decisions, which is based on this visual information. And we can reconstruct a sensory typology, this network where the thickness of a line connecting individuals is proportional to the area subtended on the retina which is the key variable for decision-making in this particular species So we can really see how the information flows through the system in a way that's never been possible before. And if we put a large number of fish in the tank, such as a thousand fish, as shown here, you get a sense that there's this ability for the group to transfer information through these relatively local interactions. You see these amazing waves. 

You see these spirals within the group. It's a really beautiful system that we can study at a range of different scales. Well, why do animals group like this? Well, they're constantly under threat, and one of the things we want to understand is how can they behave in such a beautifully synchronous manner. And so, simulations can be useful in trying to help us understand the types of interactions that work. So this is exactly the same simulation I just showed you, but I've added one more rule which is move away from the predator. And you can see the types of patterns within our model, within our virtual fish school or bird flock, that we also see in the natural world. And I worked with the BBC on this TV series called Predators where they've matched up the model with some lovely footage. And here, you can see a vacuole around the individual. So what about risk is important for the evolution of collective behavior and collective motion? How does risk select for individual social interactions? Well, that's extremely difficult to study. So what might be important? Well, perhaps it's the attraction, the biosocial attraction to others that's the critical factor. 

Perhaps it's the tendency to align the direction of travel with neighbors as the critical factor. Or perhaps it's actually about both social tendencies that's important for protecting individuals from attack. But how do we understand this? How do we test this? We can't go and tell animals, go on, just align with your neighbors. Don't be attracted to them. Let's see what happens. Let's see if you get eaten. Or, you'd be really attractive to neighbors, let's see if you get eaten. You can't do that. And also, a group can be composed of individuals that follow different strategies at the same time, and the group dynamic might be an emergent property of the different behaviors that happen to be within the group. It's incredibly difficult to study, so how can we ask questions about risk and selection on social interactions? Well, we thought of one way of addressing this topic for the first time. 

And that's to allow real predators to hunt and exert evolutionary selection pressure on virtual prey. And this is published just recently. I want to discuss this experiment with you, show you this experiment. And it was covered extensively in the media. Typically what they picked up on is we effectively created a video game for fish. [LAUGHTER] Which is kind of true. The Economist, of course, gets all serious with the benefits of schooling. [LAUGHTER] And I particularly liked the gaming websites that picked up on this, and they created some really cool graphics, such as this. [LAUGHTER] And also, the quality of the journalistic content was awesome. [LAUGHTER] Yep, some truth to that statement, I have to say. 

So, I'm going to show you the experimental design. We used a generalist predator, ferocious generalist predator called the bluegill sunfish, which we caught in the lake in Princeton. And we had an experimental design whereby the individuals were held in a holding chamber, and then we could release them to go out and hunt what they perceived as prey that were projected onto a thin translucent film just inside the tank. And so they're sort of hovering in this space, the prey. And the reason for using the translucent film is we also want to film through that to see the fish coming towards us and then to work out which one it's attacked because we're varying the behaviors, not how the individuals look. So they all look identical to each other. So I'll show you an image. Here's our bluegill. It comes out and then they have this really cool behavior where they hover. They sit there with their little fins and they hover. 

They're like an attack helicopter, targeting. And then they strike. And so we can use that hover time as a proxy for how difficult they find this particular task. And so here's the simulation. You can probably see these little dots which are delightfully delicious looking prey to a bluegill sunfish. And soon you'll see us at the actual experiment. And the fish is, of course, in the distance, and the prey, you can see them there, and the predator is going to come towards us. And when it gets close, there you can see it, and it attacks and it attacks that individual. Then we record which individual was attacked, we go back to our simulation, we know what the parameters were and what the time stamp was, we rerun it, and we work out what was your behavior, and then we punish you because you got attacked. And we do this many, many times. 

This experiment took ages, because we only used each fish once because we don't want to have problems with them learning, for example. And we can then create a risk landscape. Depending on how strong your attraction was with how strong your alignment was, how did you get attacked by the predators. And the circle, the sides of the circle represent the risk. As you can see, individuals with zero attraction and zero orientation, i.e. those individuals that were non-social, that didn't bother regarding what others around them did, got absolutely annihilated. And so, firstly, we saw the evolution of grouping, but it wasn't just random grouping. Having a very strong attraction leads you to group very strongly, but the group doesn't really go anywhere. Having strong orientation allows you to align with others but the group can fragment. And they actually evolved to be somewhere in between, to balance both attraction and alignment. And that was the strategy. 

Collective motion. Here, collective motion was a strategy that minimized their risk. Why is that? Well, we could use that hovering behavior as a proxy. As group size increases, the fish seem to find it harder and harder to target any individual within that group. And this has been termed the confusion effect. And it's being studied by some chap called David Krakauer. [LAUGHTER] And he used a neural network model that also made this prediction that the accuracy of targeting decreases as a function of increasing number of prey within groups. So we think we're seeing an experimental observation of this phenomenon. But we can go better than this, and we're working on this now. So, this is kind of a two-dimensional display, or a 3D image on a two-dimensional display is what we typically think of. 

But if we track the fish and we calculate where their eyes are, as you saw us doing earlier, we can then render the display from their perspective. Therefore, objects in the environment will be hovering in the water with the fish. And so here we see a zebrafish, and it's going to target an object that isn't really there. It appears to be in the volume of water with it, but it's an optical illusion that we've created in the laboratory. And we can also create virtual fish to swim with our real fish. We're not allowed to use real predators in the lab, so we've also created an artificial predator. This is a robotic predator. So there's a robot under the tank, and this is linked together through magnetism to pull this robot through the tank. So we can create these controlled perturbations. Because as I mentioned, risk is thought to be very important. 

So we've also gone to Louisiana to study fish in their natural environment, and those bigger dots there you see, this is real data captured using a new type of sonar that's got two-centimeter resolution. Very high resolution sonar. You can see real predators absolutely hammering these Gulf menhaden, these prey fish. And so, our computational techniques allow us to track the motion of both the predators, shown as blue crosses, and the prey, shown as these green arrows. So we can ask again for the first time, where is it most risky to be within a group? And what we find is that if we represent the world from the prey's perspective, they tend to get attacked from predators approaching them from the back. And if we look at it from the predator's perspective, so if a predator is in the middle and we ask, how did the prey respond to you as you moved through? You see what the prey do is they part. They let the predator through, and then they reform behind it. It turns out the predators have a terribly difficult time hunting because the collective information transfer within these groups is so fast, as you saw within the starlings before. What did the predators do to deal with this? Well, the predators, too, used collective behavior. If you looked to see where your neighboring predators are, they're directly in front of you and directly behind you in groups of up to six individuals in a line. 

And what this allows the predators to do by forming this line, it stops the fish schools from reforming behind the attack because as soon as they start to reform, there's another predator there and they have to go apart again, then there's another predator there and they have to go apart again. They slice through the group. They use collective behavior to slice the group up into smaller and smaller groups. And why they do this is because, really, they have very little chance of eating anything unless the groups are very small. The lower ranked two and a half meters in this particular species. And so the collective behavior is absolutely vital for the survival and the evolution of the prey, but it's absolutely vital for the behavior of the predators. But it's not all about risk. There are other aspects to collective behavior that we still don't fully understand. One is that individuals also move within complex environments. So here we're simulating something like phytoplankton bloom in the ocean or some sort of odor that's attractive to these individuals. 

We want to understand is there something about being in a group that allows you to better track these types of complex gradients. And complex environments is the norm. So far I've had my fish in this kind of empty tank or have maybe had a predator in there or an artificial predator, but the real environment is very heterogeneous and we've got no idea how this impacts behavior, but we can get a clue from this chap here. This chap is Sir Francis Galton. If you've not heard of Sir Francis, he was an extremely interesting and extraordinary chap. He was the cousin of Charles Darwin, and he was many, many things. He was an anthropologist. He was a tropical explorer, a geographer, an inventor, a meteorologist, a proto-geneticist/eugenecist, and unfortunately, a statistician. But the one thing Sir Francis Galton knew absolutely nothing about, thankfully at least there's one thing or he'd be a real pain, was livestock. Didn't have a clue about livestock. 

And the reason this is important is he went to a fair one day, in fact it was in 1906 that he went to a livestock fair, and there was a competition. Guess the weight of the ox. And if you won, there was a nice little prize. And he thought, goodness, I know nothing about oxes. What am I going to do? I want to win this prize. And so there was this competition to guess the weight of the ox, and almost 800 villagers, farmers and so forth, had entered this competition. They did know something about oxes, he thought to himself. And so he had this idea that the middle most estimate might express the voice of the population. So if everybody is not quite sure, somehow he can take all of this information and make a good guess. And he calculated that the mean estimate was 1,197 pounds. 

The actual weight of the bull, he was so far off, was 1,198. And so, this is the basis of what people believe to be collective intelligence in most systems. And so, if I translate Galton's understanding into a problem with animal behavior, we often have cases where individuals, such as birds migrating and the Earth's magnetic field, are each only able to make a rough estimate. They're not absolutely sure of what way to go. But if by grouping together, they can pool this information, it's not Galton where he could read it and do a math calculation, but by pooling this information, by averaging your estimates with those of others, perhaps you can also exhibit collective intelligence. And so this is the standard mechanism of collective intelligence in nature, the grouping or pooling of estimates. And it's also known as the many wrongs principle because if you combine many wrongs, you can still be right, like Galton was. It's never been tested experimentally. And so what we did was create an experimental arena, and at the top here, what we're going to do is we're projecting a light gradient that's kind of a noisy light gradient. It's like dappled light coming into a stream, if you're a fish schooling in that environment. 

And they want to be in the dark regions because they don't want to be in silhouettes to the background. However, when they're in the dark regions, we can't see the little blighters. And so what we also do is we film exactly the same experiment shown here but in infrared light. So this is exactly the same scene, exactly the same experiment, but here we can track the individuals. And because we're controlling this light gradient, it's not a real light gradient, it's using a video projector much like the one we have here, we can then reconstruct what that gradient is from the perspective of all of the individuals within the group. These are real fish in these experiments. And so we can track their motion and try to understand, firstly, is there a collective benefit in terms of gradient tracking? And that's never been shown before. And indeed there is. If we look at the gradient tracking capabilities, it gets much greater when they're in large groups. Well, why is that? Is it because of this Galton-like phenomena? And so, we could use the acceleration of each fish to ask the question, are you accelerating in response to the environmental information, or the social information, or some balance of the two? And so we thought perhaps they're detecting the local gradient, which is noisy, and then pooling it with the estimates of others. 

But what we found was that the environmental information, shown in red here, had next to no influence. And the social information was extremely influential. In fact, we found no evidence that the individual fish could detect their local gradient at all. And yet, the group can. So what's going on here? If the fish are not using their local light gradient, then how can the group respond to it? It doesn't seem to make sense. And what we found was, of course I love working with real systems because they're doing something simpler and much, in some ways, clever is not the right word, but something so simple and beautiful and it works extremely well. All they do is look at the light level where they are. So they don't need to detect a gradient. They just need to know how light is at where I am. They don't need to know anything else. 

And they just slow down if it's an area they like, and they speed up if it's an area they don't like. As simple as that. And they're also attracted to others. And so, the grouping really facilitates and awareness of the environment that's not present at the level of individuals. Individuals entering the dark region travel more slowly, as I mentioned. This, in conjunction with individual interaction, if you imagine my arm, I slow down one side of it and the other side still moves, will start to turn towards the area that the individuals are moving more slowly which is the area of the light. And it also reduces inter-individual distance. It's like cars on a highway. If they're moving slowly, they tend to be close together which means there's a density in the regions that are good which causes an attraction there. So it's these two factors that we could show that cause the group to be aware and to sense this environment. 

The individuals themselves cannot do it. And we also created a computational model, which is the red line here, to show that this is logically consistent. And so, it doesn't place really cognitive demands on the individuals. Minimalist model. But that's actually the one they're using. And when strategies that relate to migration or navigation, or any ecological important function exists primarily at the collective level, this could be really important for our management of migratory species if we find this in those other species. And it's also very robust and very easy to implement, either through evolution or in terms of us building this into a technological design system, such as autonomous robots. We've already done this. We've shown that this now works with micro-robots that only need to sense the light level. They need a single sensor to determine the light level. 

They don't need to do anything clever. We're also testing this as a new model for how cells can actually climb gradients, where the individual entities are receptors in a single cell. So, I also want to talk about leadership. How do grouping animals make informed, unanimous decisions? And so individuals within these groups, or some individuals can have information. Some individuals can be better informed about their environment. They're perhaps older, more experienced. Or perhaps they've just by chance learned about this environment. So how does an individual with information inform others? Does it need to signal? I know what's going on. I know where to go. Follow me. 

Do others need to recognize who has information and who does not have information for information to transfer through the group? And is that even biologically reasonable? So, to get a first grasp at this, again we perform a computational model. A very simple model. Very much like I showed you before. Where individuals have a social tendency, a schooling tendency so to speak. Those individuals with information have to reconcile that social tendency with goal-oriented behavior. And so, importantly, I've colored this individual with information bright red. That's so we can see who has information and who doesn't. But, in the model, there's no way of inferring that. The individuals start at random positions and random orientations, and they don't know the states of other individuals. So the color are purely for our own benefit. 

And these individuals have to balance this with a term that we call omega. You don't have to worry about what this means. What this means is that omega, when it's very low, the goal-oriented behavior has only a slight influence on your behavior. As omega gets higher and higher, then your goal-oriented behavior becomes increasingly important to you in terms of how you move. So we can see how intransigent are individuals to changing their direction of travel. And so the first thing we wanted to ask is, can information be transmitted without explicit signaling of transfer? And so we simulated one, five, and 10 informed individuals within a group of 100. And I'm just showing you the group motion here. The individual is colored white, but you probably can't see that. But you can see that for a group of one, it's not doing very well. One informed individual. 

In five informed individuals case, after an initial period of reorganization, they kind of go in the preferred direction. So the information is being transferred somewhat. But with 10 informed individuals, it's sudden and spontaneous. And we can then measure this accuracy of group navigation from zero being completely random to one being perfect. And the point to know from this graph is that all these lines come up sharply and then plateau which means as we increase the proportion of informed individuals, the accuracy increases and then flattens out. But let's take a look at the smallest group, and we'll choose an accuracy of 85%. And this is a group of 10. For it to move with 85% accuracy, we need to have half of the individuals in the group to have information. But if we go to a group, in this case of 200 shown in red, for the same level of accuracy, we now only need less than 5% of the individuals to have information. So as groups get larger, the proportion of informed individuals we need to guide them gets much, much smaller. 

And we've been able to test these ideas in the laboratory by training fish. The trained ones are shown in red, the naive ones are shown in blue, and we can train them to exhibit leadership events. And then we can see, just as predicted by the theory in fact, the theory and the experiments were almost overlapping, we can see the increase in accuracy as we increase the number of informed individuals. So they don't need to be able to talk to each other or explicitly signal. What about if there's differences of opinion within the group? If some want to go one way but others want to go in the other direction? So far, all of the informed individuals have been in agreement. Can they make a collective consensus decision? Effectively, can they vote about where to go? And what we show is they're extremely good at doing this. So here there's 10 that want to get to the white target and 10 that want to get to the red target. And so the choice is random. They'll equally choose the white or the red. But all I need to do is add one more white individual. 

They start at random positions. Again, they can't count, they can't do anything like that. They solve this through the kinematics of their motion. Yet, over 99% of the time, they will correctly go to the majority target. If individuals are really unwilling to give up on their opinions, if the omega values are very large and they're very strongly opinionated, then they cannot come to consensus and they'll tend to split. So, I'll come to some experiments with fish to test these ideas, but it turned out to be really hard to work with fish. It was much easier to work with students. [LAUGHTER] And so we could put high school students in this gymnasium, and undergraduates, and we start them at random orientations, randomly positioned on the circle, and they have little cards that say stay with the others, and some of them have stay with the others, but the safe exit is exit number four, or something like that. And we don't allow them to talk to each other. Yet, we can see that with only one informed individual in the group, they correctly reached the exit. 

We could also show if there's difference of opinions within the group, whether it's two versus two, or four versus two, or three versus one, without speaking to each other, the majority dictates the direction, as we predicted with our theory. And German television got very interested in this and wanted to pay for a large number of Germans to come into an even bigger gymnasium. [LAUGHTER] It was actually underneath the football stadium. And to try and work out what proportion of Germans do you need to lead a population. And it actually turned out to be pretty much spot on to what we predicted. But it was just kind of fun to perform the experiment at this type of spatial scale. Very fun. We also think about this kind of decision-making all the time, but we often think about things like committees. And, of course, within human groups, conflicting interests are very important. I'm sure we're all familiar with this. 

Whether we're making decisions within our family or on committees, there can be different opinions. And yet, failing to achieve a consensus can be very costly. Not voting for anyone to go into government or not deciding on any candidate in a job committee. And individuals may also be susceptible to manipulation by those who are strongly opinionated or have extremist views. I'm sure, again, we've all experienced that sort of process where someone in a committee is extremely strongly opinionated. And within the social sciences is generally thought, or assumed, to be honest, because there's no data, that individuals who are naive or who have weak preferences can be manipulated by those who have strong preferences. So we want to test this idea within groups. And, of course, within humans, time and again, we have adopted voting strategies because voting promotes fair representation. It doesn't matter how strongly I believe in something, I still have the same say as you do, as you do, as you do. But in animal groups, does that even make sense to think of this fair representation? They have limited means of conveying a vote. 

They've got no global overview of votes. They've got no ability to count, and they only have relatively local interactions. I won't go through the details of this, but recall what I just showed you. If I add one more white individual to the group, they'll almost always go to the majority target. So they don't need to have these capabilities to very effectively vote. So I can then ask, well, what if there's a majority versus a minority? Well, we saw that. They vote. The majority will tend to win. Well, then you can ask, if I'm in the minority and I want to get my own way, because remember, these are selfish individuals, they may want to manipulate the interests of the group. Then they can just become more strongly opinionated. 

Does that work as a strategy? And so, we have six versus five, and the strength of preference of the majority I'm going to fix at a value of .3, and it really doesn't matter what these exact numbers are, as I'll show you. So the majority has a certain preference. If the minority has the same preference, the majority will win nearly 100% of the times, a probability of one. But as I increase the strength of the minority preference, the strength of their opinion, you can see that they begin to influence and then finally completely dictate the behavior of the group. So it seems that being strongly opinionated is a good thing for you. It might not be good for the group, but it could be good for you in terms of getting your own way. But remember, everyone has information here. Everyone has a preference. What about if some individuals in the group only have weak preferences or they don't exhibit a bias? They're quite happy to go one way or the other. Or perhaps they're uninformed completely. 

How does that influence the group? Well, it changes everything. What we now find is if the minority has a stronger preference, .38 versus the majority of .3, it's not so strong. In fact, the majority will still win around 60% of the time, .6, if there are no uninformed individuals. But look what happens if I start adding, say, around 10 stupid individuals to the group. It promotes the democratic view. It returns control to the majority, and then it saturates out due to the locality of interactions. I can make the minority more and more strongly opinionated, and spontaneously, individuals that don't care which way to go, but are participating in the dynamics, push the system to the majority. And so this is really cool because this can maybe explain to us why we don't see the strategy of just becoming incredibly opinionated within these types of groups. Having diversity within the group reinforces a democratic outcome. So, these uninformed individuals could play an important role. 

But we want to understand whether this is a general principle. Does this just relate to animal groups? There are many other systems in nature where individuals influence and are influenced by others. There are technological systems, there are neuronal systems, and of course there are social systems. So can we take the core mathematical principles and ask, does it happen in these types of dynamics also? And so, so far we've done modeling of two different systems, actually we've done three, but I've shown here two, and these are deliberately simplified what are called voter models where individuals are influenced and influence those around them. And today I'll just talk a little bit about what's called and adaptive network model. And so, what's important to think about is that we get away from all the complexity. We're not really thinking about fish schools or whatever. We're now representing individuals as nodes on a network with edges between then, lines between them representing interactions or influence. And there are key features that this model has. The key features are that individuals adopt the opinion they perceive to be in the majority. 

And that individual preference manifests as intransigence, an unwillingness to give up on your opinion, or if you do get flipped to the other opinion, you're more likely to flip back to your original opinion. So very simple assumptions. And when we do this within a population here of 10,000 individuals, we see that if the majority and minority have the same preference strength, the majority will tend to win. As the minority increases the strength of their preference, then, like in the other model, eventually they can completely dictate the dynamics of the whole system. And as we increase the proportion of uninformed individuals, initially absolutely nothing happens. The minority still wins. But then we reach a tipping point where just a tiny proportion, a few more nodes in that network of 10,000 of uninformed individuals changes the dynamic of the entire system, and it flips control strongly to the majority. The nice thing about this model is we can actually prove the result mathematically that this has to happen. Fortunately, that was not done by me. That was done by my colleagues. 

[LAUGHTER] So, don't worry, we're not going to go into that. But it's a very rigorous approach. And the reason that that mathematical approach is rigorous is what it does is it shows us that there is only one stable state to the system dynamics for lower critical proportion of uninformed individuals. There's nothing else the system can do, regardless of how you parameterize it. There's nothing else that can happen. And then above that is an appearance of, you don't have to worry about that saddle-node bifurcation, just because of the shape, and the system flips to the majority state. And the nice thing about this sort of provable model is you might ask, well, maybe you got that result because of the exact way that you programmed your simulation. Well, here we can now ask how does the relative strength of the minority preference play out against the numerical advantage of the majority? Those are the two forces at play here when push comes to shove. And you can see here, with everybody, if everybody's got opinions, then the minority can control the dynamics by being opinionated. Now, I'm going to show you the region that flips from minority control to majority control when I add uninformed individuals to the population. 

So now I'm going to add these uninformed individuals, and you can see the area that flips the majority control. So, being strongly opinionated doesn't work unless you're in this little thin sliver of parameter space. There's nothing they can do about that. That's the dynamics of the system. And so we're not making any judgment about whether this is a good thing or a bad thing. We're reporting the dynamics. You can see this in both lights. We conjecture, however, that this can be widespread because we see this in lots of different systems. And it also, by the way, works for more than two opinions. So it's not just restricted to that. 

And it works for neural systems and magnetic systems and all sorts of other things that we've looked at. But, as a biologist, I'm excited because it makes a testable prediction, and I want to go out and test these ideas. The uninformed individuals should inhibit the influence of a strongly opinionated minority and return control to the majority. And also, it makes the prediction that it should occur for relatively small changes in the uninformed population. So perhaps something we will actually see in our experiments. And so we preformed experiments with our schooling fish, and it was performed by Christos Ioannou, a postdoc in my group. And we could train them. Fish have got wonderful memories. Don't believe that eight-second memory span. They've got wonderful memories. 

Train them six times and they'll remember it for weeks, to have a preference for a blue target or a yellow target. And the ones trained to the yellow target have a stronger preference. So we can then combine groups with different preferences or take untrained individuals which are our naive individuals. And if you recall, what we predict is that the minority will win if they're strongly opinionated, and that's exactly what we saw in our experiments. But as we added five and then 10 uninformed individuals, as predicted by the dynamics of these systems, we flipped control back to the numerical majority. So, the message here is that uninformed individuals inhibit extremism and promote democratic consensus decision-making. So I hope to have shown you this evening about how we could learn a lot about nature by thinking about collective dynamics, whether we're thinking about cannibal crickets, whether we're thinking about schooling fish, or even if we're thinking about our own human societies. And I'd like to thank you very much for coming tonight, and I'll take any questions. [APPLAUSE]