The surprising power of collective intelligence

46 | NewScientist | 1 February 2014

Perfect swarm

The collective intelligence of swarming animals could help us design robots, heal wounds and even understand consciousness, finds Michael Brooks

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AIN COUZIN does not have fond memories of field research. Early in his career, he travelled to Mauritania in north-west Africa to follow a swarm of locusts. Devastation caused by the insects meant no one was selling food and the team was forced to live off dried camel entrails. Couzin, a vegetarian at the time, was violently ill. “I was hallucinating – I thought I was going to die.” By the time he recovered, a huge sand storm had blown in. The researchers were trapped in their tents for several days and when, eventually, they emerged, the locusts had gone, blown away by the storm. “I was out there for two months and I got absolutely no usable data,” he says. “It was the worst experience of my life.” Fieldwork can be difficult at the best of times, and it would appear that Couzin, who is at Princeton University, is not the only swarm scientist averse to it. One of the tricky things is how to study the interactions between animals when their numbers are so huge. So researchers have generally stayed indoors with their computer models. However, these are only as good as the information you put

into them, and often they have not proved terribly enlightening. You can recreate swarmlike behaviour without really understanding why it exists. Now, though, researchers are starting to see swarms as living entities with senses, motivations and evolved behaviour. From this new view is coming a much better understanding of how animals act collectively. This does not simply tell us about flocking birds, shoaling fish, swarming locusts, and the like. It has implications for how we understand all sorts of collective action. There is a limit to what a single organism can compute, but the combined informationprocessing power of a swarm is more than the sum of its parts. Applying this concept to other complex systems provides insights in all sorts of areas, from fighting disease to building robot swarms. It might even provide a way of thinking about the human brain. For a long time, the standard approach to studying synchronised movement was to model the animals concerned as “selfpropelled particles” following a few simple rules, such as “keep a body length away from

your nearest neighbours” and “match the speed and orientation of the organism in front”. This physics-led approach, which treats animals as mindless objects, is almost certainly too simplistic – a point that was brought home to Couzin a few years ago. In an attempt to understand how locust swarms march together across an area of land, he and his colleagues had built a model which represented the insects as a collection of particles, rather like the atoms in a gas. To coordinate movement and prevent collisions, each “particle” simply had to adjust its speed and direction in response to the speed, proximity and direction of its neighbours. The team’s findings were published in Science in 2006. Only later did they discover the flaw in their model. Watching real locusts in the lab, they were surprised to find fewer at the end of their experiments than at the start. Far from avoiding collision, they were exterminating one another as they marched. “We discovered by chance that the swarm is driven by cannibalism. Everyone is trying to eat everyone else while avoiding being eaten,” > 1 February 2014 | NewScientist | 47

says Couzin. “That was a real wake-up call.” Since then, Couzin and his collaborators have seen swarming in a different light. “This isn’t just about physics,” he says. “These are biological organisms: they’re responding to sensory information.” Understanding this makes studying swarms more challenging because you need to consider the capabilities and motivations of their members. But with the help of new technology, this is exactly what Couzin and others are doing and, in the process, overturning some preconceived ideas about swarms.

Info in flow

”There is huge potential in thinking of swarms not as bunches of ‘pixels’ but as groups of sensory beings” 48 | NewScientist | 1 February 2014

Take shoaling fish. Olav Handegard, who works in Couzin’s lab and also at the Institute of Marine Research in Bergen, Norway, is using sonar imaging to reveal what is going on in the murky waters of Louisiana’s estuaries when shoals of Gulf menhaden come under attack from spotted sea trout. Like many schooling fish, they split up into smaller pods, which according to received wisdom is a way of evading predators. Not so. Handegard has found that this is what the trout are aiming for: they do their best to break up the menhaden shoal because it is easier to take a fish from a smaller group. For the menhaden, the intact shoal is the best place to be because news of a predator’s presence reaches them more rapidly in a large shoal. Each fish reacts to the movements of its nearest neighbours to create a “wave of turning” that propagates 15 times as fast as a fish can swim, and faster than the predator too. The more eyes there are to spot danger and the more neighbours’ movements there are to follow, the better the information flow. To find out more, Christos Ioannou, who splits his time between the University of Bristol, UK, and Couzin’s lab, created a virtual reality for sunfish. He simulated the shoals these predatory fish pursue by projecting white dots in various patterns onto a screen inside the sunfish’s tank. He found that when all the dots stayed together and moved in the same direction, the sunfish left them alone. The approach reveals how a predator’s behaviour influences the social interactions of its prey, and the benefit of thinking about coordinated collective motion as an evolved process. Couzin and colleagues are finding it fruitful to consider swarms as groups of sensory beings rather than rule-following data points. Other researchers have highlighted another flaw in swarming models. Modellers often assume that each member of a swarm has an equal say in determining the motion of the group – that you can model them as identical particles working together. Research on homing pigeons reveals this is not necessarily the case. A team led by Tamas Vicsek at Eötvös University in Budapest, Hungary, used GPS to

track the interactions between birds in a flock. “To our amazement, it turned out that there is a set of delicate leader-follower relationships,” he says. What’s more, these were not the same hierarchies as existed back in the loft (PNAS, vol 110, p 13049). And pigeons are not the only animals that have complex relationships between group members: herring take up different positions in a school depending on their reproductive state; female zebras with young play a disproportionate role in decisions about herd movements; and cattle have a pecking order of influence. The presence of leaders and followers may be a strength when it comes to making a collective decision (see “We all vote together”, right) but it also makes research more difficult. Vicsek and others use high-tech devices including miniature GPS trackers and real-time video taken from unmanned aerial vehicles. “To find out what animals perceive and how they react, one needs detailed information about their trajectories, orientation and so on,” he says. This is also exactly the sort of information Couzin and his colleagues are collecting. They developed computer models that map the posture of individual fish 200 times per second, with each frame reconstructing the precise field of view of each fish in the shoal. Then they projected different types of habitat onto the bottom of a fish tank to create a virtual dappled stream where a real shoal of freshwater golden shiner fish could swim through areas of light and dark. “For the first time, we have been able to see the world from the organism’s perspective,” he says. What they observed was intriguing. Fish shoals tend to stick to darker waters where they are less visible to predators, and golden shiners are no exception. This suggests that individual fish see where the water becomes darker and follow that “gradient” to safety. “It turns out the animals are doing something much simpler and much more elegant,” says Couzin. Rather than an ability to detect darkness and move towards it, the

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researchers found a link between light intensity and speed of movement: the brighter the light hitting a fish’s retina, the faster it moved. This simple response is all that is needed to guide the shoal to safety and encourage it to stay there. What’s more, the bigger the shoal, the more efficient the fish are at finding and staying in darker waters. As well as revealing the true nature of fish perception, this shows they have a collective intelligence, Couzin says (Current Biology, vol 23, R709). Each fish is a rather dumb sensor, but when networked together they can generate intelligent responses to changing environments that outstrip their individual capabilities. The findings that a mass of basic sensors can exhibit complex “emergent” behaviours has implications in other areas. For example, in robotics it could radically simplify the task of programming a network of roaming sensors because each would need only relatively simple sensing abilities but working together they could achieve complex tasks. Now Couzin is working with roboticists at the Georgia Institute of Technology in Atlanta to exploit the benefits of collective cognition to create robotic swarms designed to monitor such things as atmospheric carbon dioxide levels, algal blooms and ocean temperatures. With minimal electronics and programming, the swarms of simple sensors could trace out and highlight areas of maximum concentration, helping researchers identify the sources of pollution and other environmental problems. There are numerous potential applications in medicine too, where systems that look complex might in fact be exhibiting simple swarm-like behaviour, making them easier to understand and manipulate. Take the cells involved in wound healing. If you put a bunch of them in a Petri dish they will start moving around following certain programmed rules. However, as far as we know, individual cells are unable to sense the chemical and electric field gradients necessary to coordinate the repair processes in a body, says Couzin. He suspects that cells involved in wound healing may have

We all vote together We tend to think of swarms as mindless moving masses, not the kind of thoughtful groups that humans form. But humans often behave like a swarm, particularly when it comes to collective decision-making. During election campaigns, people often believe that sufficiently outspoken minority groups have the power to sway the results. That’s unlikely, say Iain Couzin and his team at Princeton University. Their models of voter swarms show that the minority influence, however strong, gets diluted to the point where the group goes with the majority decision – provided the electorate contains enough uninformed and undecided voters who simply copy their neighbours (Science, vol 334, p 1578). For better or worse, ignorance plays a significant role in the way democracies operate.

similar evolutionary programming to shoaling fish – simple rules that allow the group to get a complex job done. If so, we may be able to harness that emergent property and provide optimal healing conditions. Then there is embryo formation. “The process of segregation of cells into structures – an essential part of embryogenesis – is very much influenced and enhanced by flocking behaviours,” says Vicsek. Tumours also contain flocking cells, as do the self-organising cellular troops of the immune system.

Complexity simplified “These are collective decision-making systems,” says Couzin. They have always looked fearsomely complex but maybe they follow rules that are much simpler than we have suspected. By observing the individual behaviour of these swarming cells we may be able to discover those rules, giving us new ways to intervene. Taking the idea even further, Couzin contends that neurons act like swarming animals. The brain is the very definition of complexity: it contains about 86 billion neurons, all interconnected by physical, chemical and electrical channels. Couzin and his colleagues wonder whether each might act as a simple sensor which, when networked, generates complex emergent behaviour. “We’re interested in how they integrate local information from those around them, and how that gets encoded,” he says. This might, he suggests, be a key to understanding how consciousness emerges. Perhaps it is collective information processing, analogous to the way groups of fish detect light gradients that a single fish cannot perceive. Swarm dynamics might also inform our understanding of specific mental processes,

such as memory and recognition. Collections of neurons seem to fire in sync to create a memory or carry out a pattern-recognition task, notes Couzin. This is analogous to what happens when a swarm of ants performs a sudden synchronised activity. He sees each ant as a simple, mobile neural network and the swarm as a parallel information-processing system producing complex behaviour, just as happens in the brain. “There are many important analogues,” he says. Understanding swarms better should also benefit the animals within them. For example, offshore construction projects such as wind farms affect shoaling fish and dolphin schools. “The disturbance changes the way schools split and recombine, and these group sizes have an effect on feeding and reproductive success,” says David Lusseau of the University of Aberdeen, UK, who is advising the Scottish government on the issue. Fish shoal sizes are also predicted to become smaller as global temperatures rise. That’s because warmer seas contain less dissolved oxygen, so fish at the front of a shoal are more likely to deplete the water of oxygen for those behind. “Our activities affect their survival,” says Lusseau. We still have much to learn. But there is huge potential in thinking of swarms as groups of living entities whose collective intelligence outstrips their individual capabilities. That’s why Couzin is keen to get away from the simple models and get everyone thinking about the individuals within swarms as sensory beings rather than mere pixels. “The real world always has surprises, and is much more fascinating than any of the models,” he says. If that means doing more fieldwork, then so be it. Next time, though, he’ll be taking packed lunches. n Michael Brooks is a consultant for New Scientist 1 February 2014 | NewScientist | 49