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(单词翻译:双击或拖选)
This is TALK OF THE NATION. I'm Neal Conan.
We've all seen a flock of birds shift direction instantaneously mid-flight, or a school of fish swirl1 in what looked like tightly choreographed2 maneuvers3. That's called collective behavior and it fascinated and baffled scientists. Why do they do it? How? Telepathy? Now technology is revolutionizing the way researchers can track, visualize4 and even create swarms5, and what they're finding will make you go wow.
Ed Yong is a freelance science writer, who, luckily for us, covers the wow beat. His piece "How the Science of Swarms Can Help Us Fight Cancer and Predict the Future" ran in the March issue of Wired magazine. He joins us now by Skype from his home in London. Nice to have you with us today.
ED YONG: Hi. Good to be here.
YONG: So if you take a bunch of locusts and put them in a box, they will face any which - all sorts of different directions. They'll mill about fairly randomly10. But if you continue to add them, what you'll see is small clusters starting to form where the locusts start to line up with each other. And the more locusts you add, you'll hit a point where suddenly all of them just start lining11 up and forming this very cohesive12, aligned13 marching army. And all of this happens very instantaneously, and we know that it's a result of cannibalism14.
CONAN: Cannibalism?
YONG: So - yeah, exactly. So you might look at this and think, well, maybe the locusts are talking to each other, or maybe they have some sort of mental template of a swarm that they're confirming - conforming to. Actually, it's that they're trying to avoid getting eaten by each other, and they're trying to eat the locust8 in front of them. And because they - there are so many of them, this imperative15 to eat and not be eaten drives them to march in an orderly rank and file.
And it's a classic example of what the science of collective behavior teaches us. That these very, very simple interactions can give rise to behaviors that seem at first to be impossibly complex.
CONAN: Well, are starlings afraid of being eaten by other starlings when they form those amazing murmurations?
YONG: No. So the details differ from system to system, but, actually, a lot of scientists discovering a lot of similarities between them. So let's talk about starlings. You can actually simulate the movements of a flock of birds incredibly well by programming a virtual birds or voids with very simple rule. So if, for example, they're attracted to their neighbors but if they maintain a certain distance from them, and if they generally keep a similar heading, so attraction, repulsion and alignment16. Those three rules together can, on a computer screen, produce a very convincing simulation of the movements of a flock. So again, we see that simple rules can produce incredibly complex behaviors. And with the starling flock, you know, you can see thousands of birds all twisting and turning and moving as one. If a falcon17 dives into them, they will dodge18 out of the way as one.
And at least one prominent ornithologist19 used to think that this was because they had telepathic powers. But we now know that you don't need explanations anywhere near that complicated. Again, simple rules can give rules to these mesmerizing20 displays.
CONAN: And in your article, you described a scientist who gathers a large number of what he describes as incredibly dumb fish, shiners, and finds out what causes them to school and react the way they do.
YONG: That's right.
His name is Iain Couzin. He works at Princeton, and he's done a lot of work on collective behavior. And I had the delight to go and visit his lab and see some of these experiments. The shiners he works with are just kind of very boring small fish. You know, if you try and draw a small fish, you'll probably draw something that looks a bit like golden China(ph). And what he's shown is that the fish together as a shoal are very good at following patches of shade.
So if you put them in a tank and you have a sort of shifting light display over them, they will very quickly find the darkest bits. But if you put an individual fish in the tank, they can't do that. They can't track shade very well on their own. It's something that only the shoal can do. And what Couzin has shown is that the individual fish are only measuring how bright it is where they currently are. And if it's darker, they'll slow down and swim more slowly.
Now if you have an entire shoal, what happens is if the shoal hits a dark patch, the fish in that dark patch starts slowing down. And because all the fishes stick together, they swing into the shadow. And then once they're in the shadow, because they're all slowing down, they bunch up together and then stay there.
So while the individual fish aren't tracking the darkness, they're not looking around and going, oh, that's darker over there, I'll swim over there; the shoal just by moving together can unlock this new ability to seek out shade and follow it.
CONAN: And that suggests that, as a collective, there is an intelligence that does not apply to any of the individuals or even the aggregate21 of the individuals.
YONG: That's exactly right. It's the idea that there is this swarm intelligence, this ability to make decisions, to carry out computations that exists only at the level of the group. The individual fish don't have it. They can't - they fundamentally cannot do this thing that the group of them can manage.
CONAN: And there are, of course, other things that can form swarms, including human beings.
YONG: Absolutely. If you - people have done fascinating experiments looking at similarities between lots different swarms. So for humans, for example, you can take a bunch of people and put them in a large arena22 with lots of different targets around them. And you - if you tell them all to stick together and you give one of them information about which target is the right one to head for, and you'll see them all moving about randomly but very gradually heading towards that target. So the vast majority of people in that group have no idea where they're going, but because they're sticking together, they can follow the single informed person to the right destination.
And this is fascinating when you think about things like migrating animals. Think about a herd23 of wildebeests. If you look at, like, millions of these animals migrating across the African plains, you might think, OK, all of them know where they're going. They're sort of sensing something. Maybe it's the sun. Maybe it's some sort of magnetic field. But actually, we now know that all it takes is one or a few leaders in order to steer24 the entire group in the right direction. So, you know, what applies to a group of humans may also apply to a herd of cattle. Maybe it applies to cells in a tumor25 too.
CONAN: There also have been studies of humans who get involve in those horrible crushing incidents. So in addition to leading to the right direction, they could lead to the wrong direction.
YONG: Right. So the study of swarms is fascinating, not just for explaining these beautiful movements in the animal world, but also showing what happens when swarming26 behavior goes catastrophically wrong. And again, scientists have managed to model these types of movements, like people getting crushed when they're trying to escape from a flaming building, using these very simple rules, you know, these simple concepts like attraction and repulsion. They might apply to birds in a flock or to locusts in a swarm, but they can also give a pretty good approximation for what groups of humans will do in a kind of panic or crisis.
CONAN: And it's interesting. The two groups of scientists, very broadly speaking, who have been investigating this with very different ideas to begin with, are biologists - and they're looking at animal behavior - and physicists27.
YONG: Yeah, because these principles apply to all sorts of collectives. It doesn't have to be herds28 of wildebeests or flocks of starlings, it could be different particles in a magnet. Physicists started modeling these things a long time ago using mathematics, and biologists were studying other swarms like, say, ant colonies. But I think it was only through a combination of those two approaches - using modeling and computer simulations to understand what living creatures were doing and using living creatures to show real and vivid examples of the principles that the maths were demonstrating - it was only through fusion29 of those things that the field really starts taking off. And now, you know, it's almost like there's a swarm of swarm researchers. There are so many of them, and they're looking at this fascinating problem in all manner of different ways.
CONAN: And both rely, of course, on computers because biologists couldn't watch all the movements of these starlings or ants or whatever - they couldn't track them all until they got the technology.
YONG: Right. So Couzin, he relies lots on technology that actually comes from the video games industry. He uses the incredibly powerful graphics30 cards that they have in order to create these simulations on his computer, and he uses eye tracking software to track swarms in motion. So he can watch a school of fish with cameras and plot where all the individuals are and where they're all looking. He can do the same for a group of people walking through a crowded place like a railway station.
And this technology is invaluable31. It allows us to track the movements of thousands of individuals in a swarm, but it also allows us to program virtual swarms and show that actually these very simple principles are enough to create the types of behavior that you see on natural history programs or in the wild.
CONAN: We're speaking with science writer Ed Yong. He writes the blog Not Exactly Rocket Science for National Geographic32. His piece on swarm science ran in the March issue of Wired. You can a link to it at our website. You're listening to TALK OF THE NATION from NPR News. And we have a caller on the line with a question for you. This is Drew, and Drew is with us from Philadelphia.
DREW: Hey. How's it going to day?
CONAN: Good. Thanks.
YONG: Hi, Drew.
DREW: Hi. I wanted to ask, how could swarms be applied33 to decentralized electronic grids34, especially when they're centralized. If you have one failure, the entire grid35 will fail. You know, people will lose air conditioning in the summer. Can you comment on that? I would definitely like to hear about that.
CONAN: Is that one of the applications to which swarm science might help?
YONG: I - it's a little out of my ballpark. I actually just had an email from a nice gentleman who works on exactly this, so I can't answer the question specifically. But I know that there are lots of different technological36 applications to decipher science. Certainly, solving problems where the failure of one specific node in a network leads to catastrophic failure is a really important application. Getting things to move is one, is another application. You could think about flocks of drones that move together and, perhaps, even trying to get, say, driverless cars to move as a flock, that sort of thing. There are lots of people who are trying to apply these things to robots, to energy grids, to all the types of swarms - the artificial swarms that we have created ourselves.
CONAN: Thanks very much for the call, Drew.
DREW: Thank you. Have a great day.
CONAN: You too. Another application, though, is the understanding by some that cancer cells seem to act as swarms.
YONG: Yes. So I gave you the example about leadership in wildebeest and humans and how specific individuals can emerge as leaders very spontaneously and drive the motion of entire group. Now, cancers can moves as well. Cancers consists of a lot of different cells. And when tumors invade other tissues and move to different tissues, that's, you know, that's a massive problem for us. And it turns out that some tumors have, like, trailblazing leader cells that sit at the front edge of these invasion waves. And, perhaps, they are doing the same thing.
Perhaps, they are somehow steering37 the movements of the rest of the group, which are just trying to stay together. And maybe we can use this idea to steer the movements of an invading tumor away from important tissues. This all just a bit speculative38 at the moment, but these are types of ideas that swarm science might towards. It's just a new way of thinking of the world when you have any situation where a lot of individual units are working or behaving together.
CONAN: And you've talked about different mechanisms39 for the locusts and for the starlings and for the shiner fish. But are we approaching - you talked about a series, though, of very simple principles - are we approaching the idea? Are we on the cost of unified40 swarm theory?
YONG: I think that - I don't think it's any way near or simple as that. And, you know, Couzin and the others I've spoken to - who work in this field - they're very cautious about making really bold claims to this. You know, this week, we know that a lot of these principles apply across physics and biology and lots of different fields. But I think it's - it would be overblown to say that this is, you know, the next relativity or the next, you know, theory of natural selection. It's not that grandiose41.
But what it shows is it gives us another way of understanding the world that we can use what we know about humans to understand how wildebeest migrate or how cancers migrate. If we can look at a beehive and get lessons for how brains work, that tells us a lot about some - about the origins of complex behavior in the world. It shows that, you know, complexity42 doesn't have to arise from complexity. Sometimes, it can arise from simplicity43, but coordinated44, collective simplicity.
CONAN: And it is also interesting to see the descriptions you have in your story, about the laboratories that are studying this at various places, like Princeton - these interdisciplinary groups, its evolutionary45 biologists and then physicists working together with computer technicians.
YONG: That's right. Couzin's, you know, Couzin's laboratory, he's got - when I was there, he had the fish, he had a newly collected tub of ants. He had slime molds, these amoeba-like things that are capable of surprising feats46 of computation. But, you know, his lab is full of physicists. It's full of computer scientists. Aside from the animals that are there, there's not actually much wet stuff going on. There's a lot of supercomputers and, you know, programming. And it just shows that - it just highlights again the fact that these principles transcend47 a lot of the traditional boundaries of science that people are used to.
CONAN: Well, Ed Yong, thank you very much for your time today. We appreciate it.
YONG: Thank you.
CONAN: Ed Yong, again, a science writer, who, as he describes it, covers the Wow Beat. He does that for, among others, um on his National Geographic and his most recent article on the science of swarms in Wired magazine. Tomorrow, TALK OF THE NATION: SCIENCE FRIDAY with a look at how scientists are mapping consciousness. We'll be back with you again on Monday. It's the TALK OF THE NATION from NPR News. I'm Neal Conan in Washington.
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1
swirl
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| v.(使)打漩，(使)涡卷；n.漩涡，螺旋形 | |
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choreographed
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| v.设计舞蹈动作( choreograph的过去式和过去分词 ) | |
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maneuvers
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| n.策略，谋略，花招( maneuver的名词复数 ) | |
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visualize
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| vt.使看得见，使具体化，想象，设想 | |
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swarms
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| 蜂群，一大群( swarm的名词复数 ) | |
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swarm
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| n.(昆虫)等一大群；vi.成群飞舞；蜂拥而入 | |
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bugs
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| adj.疯狂的，发疯的n.窃听器( bug的名词复数 )；病菌；虫子；[计算机]（制作软件程序所产生的意料不到的）错误 | |
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locust
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| n.蝗虫；洋槐，刺槐 | |
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locusts
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| n.蝗虫( locust的名词复数 )；贪吃的人；破坏者；槐树 | |
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randomly
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| adv.随便地，未加计划地 | |
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lining
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| n.衬里，衬料 | |
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cohesive
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| adj.有粘着力的；有结合力的；凝聚性的 | |
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aligned
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| adj.对齐的，均衡的 | |
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cannibalism
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| n.同类相食；吃人肉 | |
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imperative
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| n.命令，需要；规则；祈使语气；adj.强制的；紧急的 | |
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alignment
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| n.队列；结盟，联合 | |
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falcon
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| n.隼，猎鹰 | |
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dodge
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| v.闪开，躲开，避开；n.妙计，诡计 | |
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ornithologist
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| n.鸟类学家 | |
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mesmerizing
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| adj.有吸引力的，有魅力的v.使入迷( mesmerize的现在分词 ) | |
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aggregate
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| adj.总计的，集合的；n.总数；v.合计；集合 | |
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arena
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| n.竞技场，运动场所；竞争场所，舞台 | |
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herd
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| n.兽群，牧群；vt.使集中，把…赶在一起 | |
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steer
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| vt.驾驶，为…操舵；引导；vi.驾驶 | |
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tumor
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| n.（肿）瘤，肿块(英)tumour | |
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swarming
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| 密集( swarm的现在分词 )； 云集； 成群地移动； 蜜蜂或其他飞行昆虫成群地飞来飞去 | |
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physicists
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| 物理学家( physicist的名词复数 ) | |
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herds
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| 兽群( herd的名词复数 )； 牧群； 人群； 群众 | |
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fusion
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| n.溶化；熔解；熔化状态，熔和；熔接 | |
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graphics
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| n.制图法，制图学；图形显示 | |
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invaluable
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| adj.无价的，非常宝贵的，极为贵重的 | |
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geographic
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| adj.地理学的，地理的 | |
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applied
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| adj.应用的；v.应用，适用 | |
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grids
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| n.格子( grid的名词复数 )；地图上的坐标方格；（输电线路、天然气管道等的）系统网络；（汽车比赛）赛车起跑线 | |
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grid
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| n.高压输电线路网；地图坐标方格；格栅 | |
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technological
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| adj.技术的；工艺的 | |
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steering
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| n.操舵装置 | |
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speculative
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| adj.思索性的，暝想性的，推理的 | |
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mechanisms
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| n.机械( mechanism的名词复数 )；机械装置；[生物学] 机制；机械作用 | |
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unified
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| (unify 的过去式和过去分词)； 统一的； 统一标准的； 一元化的 | |
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41
grandiose
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| adj.宏伟的，宏大的，堂皇的，铺张的 | |
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42
complexity
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| n.复杂(性)，复杂的事物 | |
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43
simplicity
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| n.简单，简易；朴素；直率，单纯 | |
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44
coordinated
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| adj.协调的 | |
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45
evolutionary
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| adj.进化的；演化的，演变的；[生]进化论的 | |
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46
feats
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| 功绩，伟业，技艺( feat的名词复数 ) | |
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47
transcend
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| vt.超出，超越(理性等)的范围 | |
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