Science, science has allowed us to know so much about the far reaches of the universe, which is at the same time tremendously important and extremely remote, and yet much, much closer, much more directly related to us, there are many things we don't really understand. And one of them is the extraordinary social complexity of the animals around us, and today I want to tell you a few stories of animal complexity.
科学, 科学让我们了解了非常多 关于浩瀚宇宙的深奥知识。 与此同时,宇宙是又是极为的重要 并且极为的遥远, 然而同时,它又比很多我们并不明白的事物 离我们更近, 跟我们更加直接相关。 我们不明白的事情之一 就是我们周围这些动物的社会复合性。 今天我想给你们讲几个 关于动物的复合性的故事。
But first, what do we call complexity? What is complex? Well, complex is not complicated. Something complicated comprises many small parts, all different, and each of them has its own precise role in the machinery. On the opposite, a complex system is made of many, many similar parts, and it is their interaction that produces a globally coherent behavior. Complex systems have many interacting parts which behave according to simple, individual rules, and this results in emergent properties. The behavior of the system as a whole cannot be predicted from the individual rules only. As Aristotle wrote, the whole is greater than the sum of its parts. But from Aristotle, let's move onto a more concrete example of complex systems.
首先,什么被我们称为复合性呢? 复合是什么? 复合并不是复杂。 一件复杂的事物是由很多小部分所组成的, 每一部分都各不相同,而且每一部分都 在这个体系中有其自身的确切作用。 与之相反,(这是)一个复合的系统 由很多很多类似的部件所组成, 而且(就是因为)他们之间的相互影响 才形成了一种宏观上一致的行为。 复合系统含有很多相互动的的元素, 它们根据简单的、 个体的规则行动, 这(样)就导致了(新的)特征的出现。 系统作为一个整体的行为 是无法仅仅根据 个体规则推测出来的。 正如亚里斯多德写道: 整体大于其各部分的总和。 但是让我们从亚里斯多德转到 复杂系统的一个更具体的例子吧。
These are Scottish terriers. In the beginning, the system is disorganized. Then comes a perturbation: milk. Every individual starts pushing in one direction and this is what happens. The pinwheel is an emergent property of the interactions between puppies whose only rule is to try to keep access to the milk and therefore to push in a random direction.
这里有几条苏格兰梗犬。 开始时,该系统是混乱无章的。 现在来点干扰: 牛奶。 每个个体开始向同一个方向推动, 然后就会变成这样。 风车式的运动是一个新出现的特征, 它源于小狗们之间的相互作用(配合) 小狗们唯一的规则就是要尽量够得着牛奶 因此它们就向一个随机的方向推。
So it's all about finding the simple rules from which complexity emerges. I call this simplifying complexity, and it's what we do at the chair of systems design at ETH Zurich. We collect data on animal populations, analyze complex patterns, try to explain them. It requires physicists who work with biologists, with mathematicians and computer scientists, and it is their interaction that produces cross-boundary competence to solve these problems. So again, the whole is greater than the sum of the parts. In a way, collaboration is another example of a complex system.
所以就是要找到 从简单规则中突现出的复合性。 我把这称之为简化复杂性, 这就是我们在苏黎世ETH系统设计院 所做的事。 我们收集关于动物族群的数据, 分析复合模式,尝试解释它们。 这需要物理学家与生物学家, 还有数学家和计算机科学家共同合作, 并且这是他们的相互动所产生出的 交叉(跨学科)式的能力 可以用来解决这些问题。 再一次,这样一来整体是 比各个部分的总和要大。 从某种程度上说,合作 是复合系统的另一个例子。
And you may be asking yourself which side I'm on, biology or physics? In fact, it's a little different, and to explain, I need to tell you a short story about myself. When I was a child, I loved to build stuff, to create complicated machines. So I set out to study electrical engineering and robotics, and my end-of-studies project was about building a robot called ER-1 -- it looked like this— that would collect information from its environment and proceed to follow a white line on the ground. It was very, very complicated, but it worked beautifully in our test room, and on demo day, professors had assembled to grade the project. So we took ER-1 to the evaluation room. It turned out, the light in that room was slightly different. The robot's vision system got confused. At the first bend in the line, it left its course, and crashed into a wall. We had spent weeks building it, and all it took to destroy it was a subtle change in the color of the light in the room. That's when I realized that the more complicated you make a machine, the more likely that it will fail due to something absolutely unexpected. And I decided that, in fact, I didn't really want to create complicated stuff. I wanted to understand complexity, the complexity of the world around us and especially in the animal kingdom.
你可能会问自己, 我是在哪一个(学科的分支)上面呢,生物学家还是物理学家呢? 事实上,都不是, 要解释这一点,我得告诉你 关于我自己的一个小故事。 当我还是个孩子的时候, 我喜欢打造东西,创建复杂的机器。 所以我决定去学电气工程 和机器人(技术)。 我的结业项目 是造一个名叫 ER-1的机器人, 看起来就像这样— 它可以从其环境中收集信息, 然后沿着地面上的白线前进。 这个机器人非常、 非常复杂, 但它在我们的测试室特别好使。 到了演示的那天,教授们聚集在一起要给我们的项目打分。 于是我们把ER1带到评估室。 结果,那间房间里的灯光 稍有不同, (这样一来)机器人的视觉系统就被弄糊涂了。 在白线第一个转弯的地方, 它就偏离了路线,然后撞到了墙上。 我们花了好几个星期来打造它, 结果使它毁于一旦的 (竟然只)是房间里灯光颜色的 微妙的变化。 这使我意识到, 你把一台机器造得越复杂, 它就越有可能会由于 某个绝对意想到不的因素而失败。 于是我决定,其实, 我并不想创建复杂的东西。 我想要了解的是复合性, 我们周围的世界的复合性, 尤其是在动物王国里。
Which brings us to bats. Bechstein's bats are a common species of European bats. They are very social animals. Mostly they roost, or sleep, together. And they live in maternity colonies, which means that every spring, the females meet after the winter hibernation, and they stay together for about six months to rear their young, and they all carry a very small chip, which means that every time one of them enters one of these specially equipped bat boxes, we know where she is, and more importantly, we know with whom she is. So I study roosting associations in bats, and this is what it looks like. During the day, the bats roost in a number of sub-groups in different boxes. It could be that on one day, the colony is split between two boxes, but on another day, it could be together in a single box, or split between three or more boxes, and that all seems rather erratic, really. It's called fission-fusion dynamics, the property for an animal group of regularly splitting and merging into different subgroups.
这就得说到蝙蝠。 彼氏鼠耳蝠是一种常见的欧洲蝙蝠。 他们是非常具有社会性的动物。 通常他们栖息或睡在一起, 而且他们生活在母系的群落里。 这就意味着每年春天, 雌蝙蝠在冬眠后聚在一起, 然后一起生活大约六个月 来养育后代。 他们身上都携带了很小的芯片, 这样每次他们其中之一 进入一个这种具有特殊装备的蝙蝠盒, 我们就知道她在哪里 更重要的是, 我们知道她和谁在一起。 所以我研究的是蝙蝠的栖息联系, 看起来就像这样: 白天的时候,蝙蝠 分成很多小组栖息在不同的蝙蝠盒里。 有可能某一天, 整个群落分在两个盒子里, 但是另一天, 它们可能一起聚在一个盒子里, 或者分到三个或或更多个盒子里, 这一切看起来确实相当混乱。 这被称为裂变融合动态, 指一群动物 定期拆分和合并 到不同的子组里的特性。
So what we do is take all these data from all these different days and pool them together to extract a long-term association pattern by applying techniques with network analysis to get a complete picture of the social structure of the colony. Okay? So that's what this picture looks like. In this network, all the circles are nodes, individual bats, and the lines between them are social bonds, associations between individuals. It turns out this is a very interesting picture. This bat colony is organized in two different communities which cannot be predicted from the daily fission-fusion dynamics. We call them cryptic social units. Even more interesting, in fact: Every year, around October, the colony splits up, and all bats hibernate separately, but year after year, when the bats come together again in the spring, the communities stay the same.
所以我们做的是收集来自不同的天 所有这些数据 并把它们集中在一起 来提取一个长期的关联模式。 通过应用技术与网络分析, 我们可以获得一份 关于整个群落社会结构的完整图片。 对吧?这张图片看起来是这样子。 在这个网络中,所有的圈 是节点,是蝙蝠个体, 而它们之间的连线 是社会纽带,是个体之间的关联。 事实证明这是张非常有趣的图片。 整个蝙蝠群 被分成两个不同的社区, 这是无法 从日常的裂变未来动态中预测出来的。 我们把他们称为隐性社会单位。 更有趣的是 每年10月前后 种群分散开了 所有蝙蝠都分开冬眠, 但是年复一年, 当蝙蝠在春天再一次聚在一起的时候, 两个群体保持不变。
So these bats remember their friends for a really long time. With a brain the size of a peanut, they maintain individualized, long-term social bonds, We didn't know that was possible. We knew that primates and elephants and dolphins could do that, but compared to bats, they have huge brains. So how could it be that the bats maintain this complex, stable social structure with such limited cognitive abilities?
所以这些蝙蝠可以记住他们的朋友 并且记住很长时间。 (虽然)大脑只有花生粒大小, 他们却保持个性化、 长期的社会纽带, 在此之前我们不知道这是有可能的。 我们知道灵长类动物, 还有大象和海豚能做到这一点, 但与蝙蝠相比,他们有巨大的大脑。 所以(蝙蝠的大脑)怎么可能 让蝙蝠们保持这个复合的、 稳定的社会结构, 而又(只有)在如此有限的认知能力(的情况下)呢?
And this is where complexity brings an answer. To understand this system, we built a computer model of roosting, based on simple, individual rules, and simulated thousands and thousands of days in the virtual bat colony. It's a mathematical model, but it's not complicated. What the model told us is that, in a nutshell, each bat knows a few other colony members as her friends, and is just slightly more likely to roost in a box with them. Simple, individual rules. This is all it takes to explain the social complexity of these bats.
在这里复合性就给予了一个解答 要了解这个系统, 我们建立了一个栖息的计算机模型。 基于简单的、 个体的规则, 然后虚拟的蝙蝠群里模拟 成千上万的日子。 这是一个数学模型, 但它并不复杂。 简而言之,该模型告诉我们的是, 每个蝙蝠认识种群少数的其他几个成员, 把她们当作朋友,而且只是稍微更有可能 在与他们栖息在同一个盒子里。 简单的、 个体的规则。 这些就足以解释 这些蝙蝠的社会复合性。
But it gets better. Between 2010 and 2011, the colony lost more than two thirds of its members, probably due to the very cold winter. The next spring, it didn't form two communities like every year, which may have led the whole colony to die because it had become too small. Instead, it formed a single, cohesive social unit, which allowed the colony to survive that season and thrive again in the next two years. What we know is that the bats are not aware that their colony is doing this. All they do is follow simple association rules, and from this simplicity emerges social complexity which allows the colony to be resilient against dramatic changes in the population structure. And I find this incredible.
还有更有趣的呢。 2010 年和 2011 年之间, 种群失去了三分之二的成员, 可能因为那年寒冷的冬天。 第二年春天,没有像每年那样 形成两个社群, 那样就可能会导致整个种群的死亡, 因为它变得太小了。 取而代之的是,它形成了一个单一、 紧密的社会单位, 这就使种群在那一季度中幸存了下来, 并在随后的两年里再次蓬勃发展。 我们知道那些蝙蝠 并不知道他们的种群在这样做。 所有他们所做的只是去遵循简单的关联规则, 而从这简单性(中), 涌现出了社会复合性 (这种社会复合型又)使得种群重新振作, (同时)去抵抗群体结构的巨大变化。 我觉得这令人难以置信。
Now I want to tell you another story, but for this we have to travel from Europe to the Kalahari Desert in South Africa. This is where meerkats live. I'm sure you know meerkats. They're fascinating creatures. They live in groups with a very strict social hierarchy. There is one dominant pair, and many subordinates, some acting as sentinels, some acting as babysitters, some teaching pups, and so on. What we do is put very small GPS collars on these animals to study how they move together, and what this has to do with their social structure. And there's a very interesting example of collective movement in meerkats. In the middle of the reserve which they live in lies a road. On this road there are cars, so it's dangerous. But the meerkats have to cross it to get from one feeding place to another. So we asked, how exactly do they do this? We found that the dominant female is mostly the one who leads the group to the road, but when it comes to crossing it, crossing the road, she gives way to the subordinates, a manner of saying, "Go ahead, tell me if it's safe." What I didn't know, in fact, was what rules in their behavior the meerkats follow for this change at the edge of the group to happen and if simple rules were sufficient to explain it.
现在我想告诉你另一个故事, 但(为了这个故事)我们必须从欧洲 到南非的卡拉哈里沙漠去。 猫鼬就住在那里。 我确信你们知道猫鼬。 他们是很有趣的生物。 他们生活在具有非常严格的社会等级制度的群体里。 群体里有一对地位最高, 还有很多下属, 有的充当哨兵, 有的充当保姆, 还有的教习幼仔,等等。 我们所做的就是把很小的 GPS 项圈 放在这些动物身上 来研究他们是如何一起行动的, 同时(研究)这与它们的社会结构有什么关系。 这里有一个非常有趣的例子 关于猫鼬的集体活动行为 在它们居住的保护区的中央 有一条马路。 这条路上有车,所以是很危险的。 但猫鼬们必须穿过去, 才能从一个猎食地到达下一个。 所以我们就问,他们到底是怎么做到的呢? 我们发现,地位最高的雌猫鼬, 多数时候(都)是她领着整个群体来到马路边, 但是到要横穿马路的时候, 她就让路给她的下属, (好像)用这种方式在说, “ 去吧,告诉我是不是安全。” 事实上我不知道的是, 这些猫鼬遵循了什么样的行为规则, 可以使群体的边缘发生这样的变化, 而且,是否用简单的规则就足以解释它。
So I built a model, a model of simulated meerkats crossing a simulated road. It's a simplistic model. Moving meerkats are like random particles whose unique rule is one of alignment. They simply move together. When these particles get to the road, they sense some kind of obstacle, and they bounce against it. The only difference between the dominant female, here in red, and the other individuals, is that for her, the height of the obstacle, which is in fact the risk perceived from the road, is just slightly higher, and this tiny difference in the individual's rule of movement is sufficient to explain what we observe, that the dominant female leads her group to the road and then gives way to the others for them to cross first. George Box, who was an English statistician, once wrote, "All models are false, but some models are useful." And in fact, this model is obviously false, because in reality, meerkats are anything but random particles. But it's also useful, because it tells us that extreme simplicity in movement rules at the individual level can result in a great deal of complexity at the level of the group. So again, that's simplifying complexity.
因此我建了一个模型,一个模拟猫鼬 横穿一条模拟马路的模型。 这是一个非常简化的模型。 正在移动的猫鼬就像随机质点一样 其唯一的规则就是排成一列。 他们只是一起行动。 当这些粒子到达马路时, 他们感觉到某种障碍, 然后他们撞了上去。 那只地位最高的雌猫鼬, 这里红色的这只, 和其他个体的唯一差别, 就是对她来说障碍物的高度, 实际上就是被察觉的马路上的风险, 稍稍高了一点儿。 这个个体运动规则中的 小小的区别 足以解释我们所观察到的现象, 也就是地位最高的雌猫鼬, 领着她的群体到马路边, 然后让路给其他猫鼬, 让它们先过马路。 乔治·鲍克斯,他是一个英国的统计学家, 曾经写道,“所有的模型是虚构的, 但有些模型是有用的。” 事实上,这个模型显然是假的, 因为在现实中,猫鼬决不是什么随机粒子。 但是这个模型也挺有用的, 因为它告诉我们这种极端简单, 在个体层面的运动规则, 到了群体的层面上, 就可以导致很大的复合性。 再说一次,这就是简化复合性。
I would like to conclude on what this means for the whole species. When the dominant female gives way to a subordinate, it's not out of courtesy. In fact, the dominant female is extremely important for the cohesion of the group. If she dies on the road, the whole group is at risk. So this behavior of risk avoidance is a very old evolutionary response. These meerkats are replicating an evolved tactic that is thousands of generations old, and they're adapting it to a modern risk, in this case a road built by humans. They adapt very simple rules, and the resulting complex behavior allows them to resist human encroachment into their natural habitat.
最后我要说的是 这对整个物种意味着什么。 当地位最高的雌猫鼬 给下属让路, 它不是出于礼貌。 事实上,地位最高的雌猫鼬 对这个群体的凝聚力来说非常重要。 如果她死在马路上,整个集团将处于危险之中。 所以这种风险规避的行为 是一个非常古老的进化反应。 这些猫鼬只是在重复一个已经进化好的策略。 这个策略已经历经成千上万代了, 现在他们在把它向(抵抗)现代风险做改进, 在这里(现代风险)是人类所建的一条路。 他们改进非常简单的规则, 而由此产生的复合行为 使它们可以抵抗人类 对于它们的自然栖息的入侵。
In the end, it may be bats which change their social structure in response to a population crash, or it may be meerkats who show a novel adaptation to a human road, or it may be another species. My message here -- and it's not a complicated one, but a simple one of wonder and hope -- my message here is that animals show extraordinary social complexity, and this allows them to adapt and respond to changes in their environment. In three words, in the animal kingdom, simplicity leads to complexity which leads to resilience.
最后, 这也许是蝙蝠通过改变它们的社会结构 去应对群体的灾难; 也许是猫鼬 对人造的马路所表现出新型的适应方式的 或者也可能是另一个物种。 我这里(想要传达的)信息不是一个复杂的问题, 而是一个简单惊叹与希望 我(要传达)的信息时是,动物 显示出非同寻常的社会复合性, 这允许他们能够适应 并对他们的环境的变化作出响应。 用三个字来说就是,在动物王国中, 简单性导致复合性, 复合性又导致适应力。
Thank you.
谢谢。
(Applause) Dania Gerhardt: Thank you very much, Nicolas, for this great start. Little bit nervous? Nicolas Perony: I'm okay, thanks. DG: Okay, great. I'm sure a lot of people in the audience somehow tried to make associations between the animals you were talking about -- the bats, meerkats -- and humans. You brought some examples: The females are the social ones, the females are the dominant ones, I'm not sure who thinks how. But is it okay to do these associations? Are there stereotypes you can confirm in this regard that can be valid across all species? NP: Well, I would say there are also counter-examples to these stereotypes. For examples, in sea horses or in koalas, in fact, it is the males who take care of the young always. And the lesson is that it's often difficult, and sometimes even a bit dangerous, to draw parallels between humans and animals. So that's it. DG: Okay. Thank you very much for this great start. Thank you, Nicolas Perony.
(掌声) 达尼亚格哈特:尼古拉斯,非常感谢你 带来这么好的一个开场。 有点紧张么? 尼古拉斯普莱尼:我还好,谢谢。 达尼亚格哈特:好的,很好。我确信观众席中的许多人 以某种方式试图去在你谈论的动物比如蝙蝠和猫鼬 与人之间 建立关联 你带来了几个例子 雌性们是上流社会中的个体 雌性们(还是)有统治地位的个体 我不确定谁会去思考为什么(会这样)。 但是,(在人与动物之间)做这样的关联妥当么? 有没有(一些)在这方面你可以确定的模式化见解 是否这样的一个关联在所有的物种之间都是可行的呢? 尼古拉斯普莱尼: 我想说,确实也有 对于这些模式化见解的反例。 举个例子,在海马或者考拉之中,其实, 总是由雄性来照顾他们的幼仔的。 (我们所学到的)经验教训是,有时这是有些困难 并且时常有一点危险 在人类与动物之间进行对比。 这也就是我的回答了。 达尼亚格哈特:好的。感谢你如此精彩的开场。 尼古拉斯普莱尼,谢谢你。