How the Science of Swarms Can Help Us Fight Cancer and Predict the Future _ WIRED

7/24/2019 How the Science of Swarms Can Help Us Fight Cancer and Predict the Future _ WIRED

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HOW THE SCIENCE OF SWARMS CANHELP US FIGHT CANCER AND PREDICT

THE FUTURE

THE FIRST THING TO HIT IAIN CO UZIN when he walked

into the Oxford lab where he kept his locusts was the

smell, like a stale barn full of old hay. The second, third,

and fourth things to hit him were locusts. The insects

frequently escaped their cages and careened into the

faces of scientists and lab techs. The room was hot and

humid, and the constant commotion of 20,000 bugsproduced a miasma of aerosolized insect exoskeleton.



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eve op ng severe a erg es. It wasn t t e eas est p ace

to do science,” Couzin says.

In the mid-2000s that lab was,however, one of the only

places on earth to do the kind

of science Couzin wanted. He

didn’t care about locusts, per

se—Couzin studies collective

behavior. That’s swarms,

flocks, schools, colonies …

anywhere the actions of

individuals turn into the

behaviors of a group.

Biologists had already teased

apart the anatomy of locusts in detail, describing their

transition from wingless green loners at birth to flying

black-and-yellow adults. But you could dissect one

after another and still never figure out why they

blacken the sky in mile-wide plagues. Few people had

looked at how locusts swarm since the 1960s—it was,frankly, too hard. So no one knew how a small, chaotic

group of stupid insects turned into a cloud of millions,

united in one purpose.

Couzin would put groups of up to 120 juveniles into a

sombrero-shaped arena he called the locustaccelerator, letting them walk in circles around the rim

for eight hours a day while an overhead camera filmed

their movements and software mapped their positions

and orientations. He eventually saw what he was

looking for: At a certain density, the bugs would shift to

cohesive, aligned clusters. And at a second criticalpoint, the clusters would become a single marching

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pre u e to t e r trans ormat on nto ac -an -ye ow

adults.

That’s what happens in nature, but no one had everinduced these shifts in the lab—at least not in animals.

In 1995 a Hungarian physicist named Tamás Vicsek and

his colleagues devised a model to explain group

behavior with a simple—almost rudimentary

—condition: Every individual moving at a constant

velocity matches its direction to that of its neighbors

within a certain radius. As this hypothetical collective

becomes bigger, it flips from a disordered throng to an

organized swarm, just like Couzin’s locusts. It’s a phase

transition, like water turning to ice. The individuals

have no plan. They obey no instructions. But with the

right if-then rules, order emerges.

Couzin wanted to know what if-then rules produced

similar behaviors in living things. “We thought that

maybe by being close to each other, they could transfer

information,” Couzin says. But they weren’t

communicating in a recognizable way. Some other

dynamic had to be at work.

The answer turned out to be quite grisly. Every

morning, Couzin would count the number of locusts he

placed in the accelerator. In the evening, his colleague

Jerome Buhl would count them as he took them out.

But Buhl was finding fewer individuals than Couzin

said he had started with. “I thought I was going mad,”

Couzin says. “My credibility was at stake if I couldn’t

even count the right number of locusts.”

When he replayed the video footage and zoomed in, hesaw that the locusts were biting each other if they got

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evoure . T at was t e ey. ann a sm, not

cooperation, was aligning the swarm. Couzin figured

out an elegant proof for the theory: “You can cut the

nerve in their abdomen that lets them feel bites from

behind, and you completely remove their capacity to

swarm,” he says.

Couzin’s findings are an example of a phenomenon that

has captured the imagination of researchers around

the world. For more than a century people have tried to

understand how individuals become unified groups.

The hints were tantalizing—animals spontaneously

generate the same formations that physicists observe

in statistical models. There had to be underlying

commonalities. The secrets of the swarm hinted at a

whole new way of looking at the world.

But those secrets were hidden for decades. Science, in

general, is a lot better at breaking complex things into

tiny parts than it is at figuring out how tiny parts turn

into complex things. When it came to figuring out

collectives, nobody had the methods or the math.

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Now, thanks to new observation technologies, powerful

software, and statistical methods, the mechanics of

collectives are being revealed. Indeed, enough

physicists, biologists, and engineers have gotten

involved that the science itself seems to be hitting a

density-dependent shift. Without obvious leaders or an

overarching plan, this collective of the collective-

obsessed is finding that the rules that produce majesticcohesion out of local jostling turn up in everything

from neurons to human beings. Behavior that seems

impossibly complex can have disarmingly simple

foundations. And the rules may explain everything

from how cancer spreads to how the brain works and

how armadas of robot-driven cars might somedaynavigate highways. The way individuals work together

may actually be more important than the way they

work alone.

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ARISTOTLE FIRST POSITED that the whole could be more

than the sum of its parts. Ever since, philosophers,

physicists, chemists, and biologists have periodicallyrediscovered the idea. But it was only in the computer

age—with the ability to iterate simple rule sets

millions of times over—that this hazy concept came

into sharp focus.

HOW SWARMS EMERGE

GOLDEN

SHINERS

ANTS HUMANS

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For most of the 20th century, biologists and physicists

pursued the concept along parallel but separate tracks.

Biologists knew that living things exhibited collective

behavior—it was hard to miss—but how they pulled it

LOCUSTS STARLINGS HONEYBEES

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anyone cou gure out ow swarms orme , someone

had to figure out how to do the observations. In a herd,

all the wildebeests/bacteria/starlings/whatevers look

pretty much alike. Plus, they’re moving fast through

three-dimensional spaces. “It was just incredibly

difficult to get the right data,” says Nigel Franks, a

University of Bristol biologist and Couzin’s thesis

adviser. “You were trying to look at all the parts and

the complete parcel at the same time.”

Physicists, on the other hand, had a different problem.

Typically biologists were working with collectives

ranging in number from a few to a few thousand;

physicists count groups of a few gazillion. The kinds of

collectives that undergo phase transitions, like liquids,

contain individual units counted in double-digit

powers of 10. From a statistical perspective, physics

and math basically pretend those collectives are

infinitely large. So again, you can’t observe the

individuals directly in any meaningful way. But you can

model them.

A great leap forward came in 1970, when a

mathematician named John Conway invented what he

called the Game of Life. Conway imagined an Othello

board, with game pieces flipping between black and

white. The state of the markers—called cells—changeddepending on the status of neighboring cells. A black

cell with one or no black neighbors “died” of loneliness,

turning white. Two black neighbors: no change. Three,

and the cell “resurrected,” flipping from white to black.

Four, and it died of overcrowding—back to white. The

board turned into a constantly shifting mosaic.

Conway could play out these rules with an actual

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t e game g ta y, L e got very comp cate . At g

speed, with larger game boards, they were able to coax

an astonishing array of patterns to evolve across their

screens. Depending on the starting conditions, they got

trains of cells that trailed puffs of smoke, or guns that

shot out small gliders. At a time when most software

needed complex rules to produce even simple

behaviors, the Game of Life did the opposite. Conway

had built a model of emergence—the ability of his littleblack and white critters to self-organize into

something new.

Sixteen years later, a computer animator named Craig

Reynolds set out to find a way to automate the

animated movements of large groups—a more efficient

algorithm would save processing time and money.

Reynolds’ software, Boids, created virtual agents thatmimicked a flock of birds. It included behaviors like

obstacle avoidance and the physics of flight, but at the

heart of Boids were three simple rules: Move toward

the average position of your neighbors, keep some

distance from them, and align with their average

heading (alignment is a measure of how close an

individual’s direction of movement is to that of other

individuals). That’s it.

Boids and its ilk revolutionized Hollywood in the early

’90s. It animated the penguins and bats of Batman

Returns. Its descendants include software like Massive,

the program that choreographed the titanic battles in

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m racu ous enoug , ut t e oc s create y Bo s a so

suggested that real-world animal swarms might arise

the same way—not from top-down orders, mental

templates of orderly flocks, or telepathic

communication (as some biologists had seriously

proposed). Complexity, as Aristotle suggested, could

come from the bottom up.

The field was starting to take off. Vicsek, the Hungarian

physicist, simulated his flock in 1995, and in the late

1990s a German physicist named Dirk Helbing

programmed sims in which digital people

spontaneously formed lanes on a crowded street and

crushed themselves into fatal jams when fleeing from a

threat like a fire—just as real humans do. Helbing did it

with simple “social forces.” All he had to do was tell his

virtual humans to walk at a preferred speed toward a

destination, keep their distance from walls and one

another, and align with the direction of their

neighbors. Presto: instant mob.

By the early 2000s, the research in biology and physics

was starting to intersect. Cameras and computer-

vision technologies could show the action of

individuals in animal swarms, and simulations were

producing more and more lifelike results. Researchers

were starting to be able to ask the key questions: Wereliving collectives following rules as simple as those in

the Game of Life or Vicsek’s models? And if they were …

how?

TAKING SHAPE

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BEFORE STUDYING COLLECTIVES , Couzin collected

them. Growing up in Scotland, he wanted pets, but his

brothers’ various allergies allowed only the most

unorthodox ones. “I had snails at the back of my bed,

aphids in my cupboard, and stick insects in my school

locker,” he says. And anything that formed swarmsfascinated him. “I remember seeing these fluidlike fish

schools on TV, watching them again and again, and

being mesmerized. I thought fish were boring, but

these patterns—” Couzin pauses, and you can almost

see the whorls of schooling fish looping behind his

eyes; then he’s back. “I’ve always been interested in

patterns,” he says simply.

When Couzin became a graduate student in Franks’ lab

in 1996, he finally got his chance to work on them.

Franks was trying to figure out how ant colonies

organize themselves, and Couzin joined in. He would

dab each bug with paint and watch them on video,

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erent n v ua s. It was very a or ous, e says.

Worse, Couzin doubted it worked. He didn’t believe the

naked eye could follow the multitude of parallel

interactions in a colony. So he turned to artificial ones.

He learned to program a computer to track the

ants—and eventually to simulate entire animal groups.

He was learning to study not the ants but the swarm.

For a biologist, the field was a lonely one. “I thought

there must be whole labs focused on this,” Couzin says.

“I was astonished to find that there weren’t.” What he

found instead was Boids. In 2002 Couzin cracked open

the software and focused on its essential trinity of

attraction, repulsion, and alignment. Then he messed

with it. With attraction and repulsion turned up and

alignment turned off, his virtual swarm stayed loose

and disordered. When Couzin upped the alignment, the

swarm coalesced into a whirling doughnut, like a

school of mackerel. When he increased the range over

which alignment occurred even more, the doughnut

disintegrated and all the elements pointed themselvesin one direction and started moving together, like a

flock of migrating birds. In other words, all these

different shapes come from the same algorithms. “I

began to view the simulations as an extension of my

brain,” Couzin says. “By allowing the computer to help

me think, I could develop my intuition of how these

systems worked.”

By 2003, Couzin had a grant to work with locusts at

Oxford. Labs around the world were quietly putting

other swarms through their paces. Bacterial colonies,

slime molds, fish, birds … a broader literature wasstarting to emerge. Work from Couzin’s group, though,

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ow t e r sc p nes cou use toget er. Stu y ng

animal behavior “used to involve taking a notepad and

writing, ‘The big gorilla hit the little gorilla,’ ” Vicsek

says. “Now there’s a new era where you can collect data

at millions of bits per second and then go to your

computer and analyze it.”

TODAY COUZIN, 39, H EADS A LAB at Princeton

University. He has a broad face and cropped hair, and

the gaze coming from behind his black-rimmed glasses

is intense. The 19-person team he leads is ostensiblypart of the Department of Ecology and Evolutionary

Biology but includes physicists and mathematicians.

They share an office with eight high-end

workstations—all named Hyron, the Cretan word for

beehive, and powered by videogame graphics cards.

Locusts are verboten in US research because of fears

they’ll escape and destroy crops. So when Couzin came

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He a one some wor w t s , so e ea e to a

nearby lake with nets, waders, and a willing team. After

hours of slapstick failure, and very few fish, he

approached some fishermen on a nearby bridge. “I

thought they’d know where the shoals would be, but

then I went over and saw tiny minnow-sized fish in

their buckets, schooling like crazy.” They were golden

shiners—unremarkable 2- to 3-inch-long creatures that

are “dumber than I could possibly have imagined,”Couzin says. They are also extremely cheap. To get

started he bought 1,000 of them for 70 bucks.

When Couzin enters the room where the shiners are

kept, they press up against the front of their tanks in

their expectation of food, losing any semblance of a

collective. But as soon as he nets them out and drops

them into a wide nearby pool, they school together,

racing around like cars on a track. His team has injected

colored liquid and a jelling agent into their tiny backs;

the two materials congeal into a piece of gaudy plastic,

making them highly visible from above. As theynavigate courses in the pool, lights illuminate the

plastic and cameras film their movements. Couzin is

using these stupid fish to move beyond just looking at

how collectives form and begin to study what they can

accomplish. What abilities do they gain?

For example, when Couzin flashes light over the

shiners, they move, as one, to shadier patches,

presumably because darkness equals relative safety for

a fish whose main defensive weapon is “run away.”

Behavior like this is typically explained with the “many

wrongs principle,” first proposed in 1964. Each shiner,the theory goes, makes an imperfect estimate about

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toget er, averages t ese many s g t y wrong

estimations to get the best direction. You might

recognize this concept by the term journalist James

Surowiecki popularized: “the wisdom of crowds.”

But in the case of shiners, Couzin’s observations in the

lab have shown that the theory is wrong. The school

could not be pooling imperfect estimates, because the

individuals don’t make estimates of where things are

darker at all. Instead they obey a simple rule: Swim

slower in shade. When a disorganized group of shiners

hits a dark patch, fish on the edge decelerate and the

entire group swivels into darkness. Once out of the

light, all of them slow down and cluster together, like

cars jamming on a highway. “That’s purely an emergent

property,” Couzin says. “The sensing ability really

happens only at the level of the collective.” In other

words, none of the shiners are purposefully swimming

toward anything. The crowd has no wisdom to cobble

together.

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o swarm nte gence, nc u ng some t at appen n

actual swarms. Every spring, honeybees leave their old

colonies to build new nests. Scouts return to the hive to

convey the locations of prime real estate by waggling

their bottoms and dancing in figure eights. The

intricate steps of the dances encode distance and

direction, but more important, these dances excite

other scouts.

Thomas Seeley, a behavioral biologist at Cornell, used

colored paint to mark bees that visited different sites

and found that those advocating one location ram their

heads against colony-mates that waggle for another. If

a dancer gets rammed often enough, it stops dancing.

The head-butt is the bee version of a downvote. Once

one party builds past a certain threshold of support,

the entire colony flies off as one.

House-hunting bees turn out to be a literal hive mind,

composed of bodies. This is no cheap metaphor. In the

1980s cognitive scientists began to posit that human

cognition itself is an emergent process. In your brain,

this thinking goes, different sets of neurons fire in

favor of different options, exciting some neighbors into

firing like the waggling bees, and inhibiting others into

silence, like the head-butting ones. The competition

builds until a decision emerges. The brain as a wholesays, “Go right” or “Eat that cookie.”

The same dynamics can be seen in starlings: On clear

winter evenings, murmurations of the tiny blackish

birds gather in Rome’s sunset skies, wheeling about

like rustling cloth. If a falcon attacks, all the starlings

dodge almost instantaneously, even those on the far

side of the flock that haven’t seen the threat. How can

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t e r secret y m ng t ousan s o star ngs rom a

chilly museum rooftop with three cameras and using a

computer to reconstruct the birds’ movements in three

dimensions. In most systems where information gets

transferred from individual to individual, the quality of

that information degrades, gets corrupted—like in a

game of telephone. But Cavagna found that the

starlings’ movements are united in a “scale-free” way.

If one turns, they all turn. If one speeds up, they allspeed up. The rules are simple—do what your

half-dozen closest neighbors do without hitting them,

essentially. But because the quality of the information

the birds perceive about one another decays far more

slowly than expected, the perceptions of any individual

starling extend to the edges of the murmuration andthe entire flock moves.

ALL THESE SIMILARITIES seem to point to a grand

unified theory of the swarm—a fundamental ultra-calculus that unites the various strands of group

behavior. In one paper, Vicsek and a colleague

wondered whether there might be “some simple

underlying laws of nature (such as, e.g., the principles

of thermodynamics) that produce the whole variety of

the observed phenomena.”

Couzin has considered the same thing. “Why are we

seeing this again and again?” he says. “There’s got to

be something deeper and more fundamental.”

Biologists are used to convergent evolution, like the

streamlining of dolphins and sharks or echolocation inbats and whales—animals from separate lineages have

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a gor t ms E t er a t ese co ect ves came up w t

different behaviors that produce the same outcomes

—head-butting bees, neighbor-watching starlings,

light-dodging golden shiners—or some basic rules

underlie everything and the behaviors are the bridge

from the rules to the collective.

Stephen Wolfram would probably say it’s the

underlying rules. The British mathematician and

inventor of the indispensable software Mathematica

published a backbreaking 1,200-page book in 2002, A

New Kind of Science, positing that emergent

properties embodied by collectives came from simple

programs that drove the complexity of snowflakes,

shells, the brain, even the universe itself. Wolfram

promised that his book would lead the way to

uncovering those algorithms, but he never quite got

there.

Couzin, on the other hand, is wary of claims that his

field has hit upon the secret to life, the universe, and

everything. “I’m very cautious about suggesting that

there’ll be an underlying theory that’ll explain thestock market and neural systems and fish schools,” he

says. “That’s relatively naive. There’s a danger in

thinking that one equation fits all.” Physics predicts

the interactions of his locusts, but the mechanism

manifests through cannibalism. Math didn’t produce

the biology; biology generated the math.

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w t energy— net c, t erma , w atever—pro uces

patterns. Metal rods organize into vortices when

bounced around on a vibrating platform. In a petri dish,

muscle proteins migrate unidirectionally when pushed

by molecular motors. Tumors spawn populations of

rogue, mobile cells that align with and migrate into

surrounding tissues, following a subset of trailblazing

leader cells. That looks like a migrating swarm; figure

out its algorithms and maybe you could divert it fromvital organs or stop its progress.

The same kind of rules apply when you step up the

complexity. The retina, that sheet of light-sensing

tissue at the back of the eye, connects to the optic

nerve and brain. Michael Berry, a Princeton

neuroscientist, mounts patches of retinas on

electrodes and shows them videos, watching their

electrophysiological responses. In this context, the

videos are like the moving spotlights Couzin uses with

his shiners—and just as with the fish, Berry finds

emergent behaviors with the addition of more neurons.“Whether the variable is direction, heading, or how you

vote, you can map the mathematics from system to

system,” Couzin says.

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IN A LAB THAT LOOKS LIKE AN AIRCRAFT HAN GAR,

several miles from Princeton’s main campus, an

assortment of submersibles are suspended from the

ceiling. The cool air has a tang of chlorine, thanks to a20,000-gallon water tank, 20 feet across and 8 feet

deep, home to four sleek, cat-sized robots with dorsal

and rear propellers that let them swim in three

dimensions.

The robots are called Belugas, and they’re designed to

test models of collective behavior. “We’re learning

about mechanisms in nature that I wouldn’t have

dreamed of designing,” says engineer Naomi Leonard.

She plans to release pods of underwater robots to

collect data on temperature, currents, pollution, and

more. Her robots can also track moving gradients,avoid each other, and keep far enough apart to avoid

collecting redundant data—just enough programming

to unlock more complex abilities. Theoretically.

Today it’s not working. Three Belugas are out of the

tank so Leonard’s team can tinker. The one in the wateris on manual, driven by a thick gaming joystick. The

controls are responsive, if leisurely, and daredevil

maneuvers are out of the question.

Leonard has a video of the robots working together,

though, and it’s much more convincing. The bots carry

out missions with a feedback-controlled algorithm

programmed into them, like finding the highest

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targets separate y an t en reun t ng.

Building a successful robot swarm would show that the

researchers have figured out something basic. Robotgroups already exist, but most have sophisticated

artificial intelligence or rely on orders from human

operators or central computers. To Tamás Vicsek—the

physicist who created those early flock simulations

—that’s cheating. He’s trying to build quadcopters that

flock like real birds, relying only on knowledge of their

neighbors’ position, direction, and speed. Vicsek wants

his quadcopters to chase down another drone, but so

far he’s had little success. “If we just apply the simple

rules developed by us and Iain, it doesn’t work,” Vicsek

says. “They tend to overshoot their mark, because they

do not slow down enough.”

Another group of researchers is trying to pilot a flock

of unmanned aerial vehicles using fancy network

theories—the same kind of rules that govern

relationships on Facebook—to communicate, while

governing the flocking behavior of the drones with a

modified version of Boids, the computer animationsoftware that helped spark the field in the first place.

Yet another team is working on applying flocking

behaviors to autonomous cars—one of the fundamental

emergent properties of a flock is collision avoidance,

and one of the most important things self-driving cars

will have to be able to do is not run into people or one

another.

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eng neer ng. T e ro ots responses to comman s are

delayed. Small asymmetries in their hulls change the

way each one moves. Ultimately, dealing with that

messiness might be the key to taking the study of

collectives to the next level. Ever since the days of

Boids, scientists have made big assumptions about how

animals interact. But animals are more than models.

They sense the world. They communicate. They make

decisions. These are the abilities that Couzin wants tochannel. “I started off with these simple units

interacting to form complex patterns, and that’s fine,

but real animals aren’t that simple,” Couzin says. He

picks up a plastic model of a crow from his bookshelf.

“Here we have a pretty complex creature. It’s getting to

the point where we’ll be able to analyze the behavior of these animals in natural, three-dimensional

environments.” Step one might be to put a cheap

Microsoft Kinect game system into an aviary, bathing

the room in infrared and mapping the space.

Step two would be to take the same measurements inthe real world. Every crow in a murder would carry

miniature sensors that record its movements, along

with the chemicals in its body, the activity in its brain,

and the images on its retina. Couzin could marry the

behavior of the cells and neurons inside each bird with

the movements of the flock. It’s a souped-up version of

the locust accelerator—combine real-world models

with tech to get an unprecedented look at creatures

that have been studied intensively as individuals but

ignored as groups. “We could then really understand

how these animals gain information from each other,

communicate, and make decisions,” Couzin says. He

doesn’t know what he’ll find, but that’s the beauty of

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you re go ng, you st get t ere.

Ed Yong ([email protected]) writes the blog Not

Exactly Rocket Science for National Geographic.

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How the Science of Swarms Can Help Us Fight Cancer and Predict the Future _ WIRED

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