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.
科學, 科學讓我們了解了非常多 關於浩瀚宇宙的深奧知識。 與此同時,宇宙是又是極為的重要 並且極為的遙遠, 然而同時, 它又比很多我們並不明白的事物 離我們更近, 跟我們更加直接相關。 我們不明白的事情之一 就是我們周圍這些動物的社會複合性。 今天我想給你們講幾個 關於動物的複合性的故事。
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,什麼被我們稱為複合性呢? 複合是什麼? 複合並不是複雜。 複雜的事物是由很多小部分所組成的, 每一部分都各不相同,而且每一部分都 在這個體系中有其自身的確切作用。 與之相反,這是一個複合的系統 由很多很多類似的部件所組成, 而且他們之間的相互影響 才形成了一種宏觀上一致的行為。 複合系統含有很多相互動的的元素, 它們根據簡單的、 個體的規則行動, 這就導致了新特徵的出現。 系統作為一個整體的行為 是無法僅僅根據 個體規則推測出來的。 正如亞里斯多德寫道: 「整體大於其各部分的總和。」 但是讓我們從亞里斯多德轉到 複合系統的一個更具體的例子吧。
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.
這裡有幾條蘇格蘭梗犬。 開始時,該系統是混亂無章的。 現在來點干擾: 牛奶。 每個個體開始向同一個方向移動, 然後就會變成這樣。 風車式的運動是一個新出現的特徵, 它源於小狗們之間的相互作用, 小狗們唯一的規則是盡量搆得著牛奶, 因此牠們就向一個隨機的方向移動。
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, 從簡單規則中展現出的複合性。 我把這稱之為簡化複合性, 這就是我們在蘇黎世聯邦理工學院系統設計院 所做的事。 我們收集關於動物族群的數據, 分析複合模式,並且嘗試去解釋它們。 這需要物理學家、生物學家、 還有數學家和計算機科學家共同合作, 運用跨學科領域的研究能力, 來解決這些問題。 再一次,這樣一來整體是 比各個部分的總和要大。 從某種程度上說 合作是複合系統的另一個例子。
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.
你可能會問自己, 我是在哪一個學科分支上面呢, 生物學家還是物理學家呢? 事實上,都不是。 要解釋這一點,我得告訴你 關於我自己的一個小故事。 當我還是個孩子的時候, 我喜歡打造東西,創建複雜的機器。 所以我決定去學電器工程 和機器人工程。 我的畢業設計 是造一個名叫 ER-1 的機器人, 看起來就像這樣。 它可以從其環境中收集信息, 然後沿著地面上的白線前進。 這個機器人非常非常複雜, 但它在我們的測試室運作的很好。 到了演示的那天,教授們聚集在一起要給我們的項目打分。 於是我們把 ER-1 帶到評估室。 結果,那間房間裡的燈光 稍有不同, 結果機器人的視覺系統就被弄糊塗了。 在白線第一個轉彎的地方, 它就偏離了路線,然後撞到了牆上。 我們花了好幾個星期來打造它, 結果使它毀於一旦的 竟然是房間裡燈光顏色的 微妙的變化。 這使我意識到, 你把一台機器造得越複雜, 它就越有可能會由於 某個絕對意想到不的因素而失敗。 於是我決定,其實, 我並不想創建複雜的東西。 我想要了解的是複合性, 我們周圍的世界的複合性, 尤其在動物王國裡。
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.
這就得說到蝙蝠。 彼氏鼠耳蝠是一種常見的歐洲蝙蝠。 他們是非常具有社會性的動物。 通常他們棲息或睡在一起, 而且他們生活在母系的群落裡。 這就意味著每年春天, 雌蝙蝠在冬眠後聚在一起, 然後一起生活大約六個月 來養育後代。 他們身上都攜帶了很小的芯片, 這樣每次牠們其中之一 進入一個這種具有特殊裝備的蝙蝠盒, 我們就知道她在哪裡 更重要的是, 我們知道她和誰在一起。 所以我研究的是蝙蝠的棲息聯繫, 看起來就像這樣: 白天的時候,蝙蝠 分成很多小組棲息在不同的蝙蝠盒裡。 有可能某一天, 整個群落分在兩個盒子裡, 但是另一天, 牠們可能一起聚在一個盒子裡, 或者分到三個或或更多個盒子裡, 這一切看起來確實相當混亂。 這被稱為裂變融合動態, 指一群動物 定期拆分和合併 到不同的子組裡的特性。
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.
所以我們做的是收集來自不同日子的 所有這些數據, 並把它們集中在一起 來提取一個長期的關聯模式。 通過應用技術與網絡分析, 我們可以獲得一份 關於整個群落社會結構的完整圖片。 對吧?這張圖片看起來是這樣子。 在這個網絡中,所有的圈 都是每一個節點,是一個蝙蝠個體, 而它們之間的連線 是社會紐帶,是個體之間的關聯。 事實證明這是張非常有趣的圖片。 整個蝙蝠群 被分成兩個不同的社區, 這是從日常的裂變未來動態中預測來的。 我們把它稱為隱性社會單位。 更有趣的是, 每年 10 月前後, 種群分散開了, 所有蝙蝠都分開冬眠。 但是年復一年, 當蝙蝠在春天再一次聚在一起的時候, 兩個群體保持不變。 所以這些蝙蝠可以記住他們的朋友
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?
並且記住很長時間。 雖然,蝙蝠的大腦只有花生粒大小, 他們卻保持個性化、 長期的社會紐帶, 在此之前,我們認為這是不可能的。 我們知道靈長類動物, 還有大象和海豚能做到這一點, 但與蝙蝠相比,牠們的大腦大多了。 所以,蝙蝠的大腦怎麼可能 會讓蝙蝠保持這個複合的、 穩定的社會結構, 而又在如此有限的認知能力的情況下呢? 在這裡複合性就給予了一個解答。
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.
要了解這個系統, 我們建立了一個棲息的計算機模型。 基於簡單的、 個體的規則, 然後虛擬的蝙蝠群裡模擬 成千上萬的日子。 這是一個數學模型, 但它並不複雜。 簡而言之,該模型告訴我們的是, 每個蝙蝠認識種群少數的其他幾個成員, 把牠們當作朋友,而且只是稍微更有可能 在與牠們棲息在同一個盒子裡。 簡單的、個體的規則。 這些就足以解釋 這些蝙蝠的社會複合性。 還有更有趣的呢。
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.
2010 年和 2011 年之間, 種群失去了三分之二的成員, 可能因為那年寒冷的冬天。 第二年春天,沒有像每年那樣 形成兩個社群, 那樣就可能會導致整個種群的死亡, 因為它變得太小了。 取而代之的是,它形成了一個單一、 緊密的社會單位, 這就使種群在那一季度中倖存了下來, 並在隨後的兩年裡再次蓬勃發展。 我們知道那些蝙蝠 並不知道牠們的種群在這樣做。 牠們所做的只是遵循簡單的關聯規則, 而從這簡單性中, 體現出了社會複合性 而這使得種群重新振作, 同時,抵禦群體結構的巨大變化。 我覺得這令人難以置信。 現在我想告訴你另一個故事,
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.
但我們必須從歐洲 到南非的卡拉哈里沙漠去。 貓鼬就住在那裡。 我確信你們知道貓鼬。 牠們是很有趣的生物。 牠們生活在具有非常嚴格的社會等級制度的群體裡。 群體裡有一對地位最高, 還有很多下屬, 有的充當哨兵, 有的充當保姆, 還有的教習幼兒,等等。 我們所做的就是把很小的 GPS 項圈 放在這些動物身上 來研究他們是如何一起行動的, 同時研究這與牠們社會結構的關係。 這裡有一個關於貓鼬的集體活動行為 非常有趣的例子。 在牠們居住的保護區的中央 有一條馬路。 這條路上有車,所以是很危險的。 但貓鼬們必須穿過去, 才能從一個獵食地到達下一個。 我們想,牠們到底是怎麼做到的呢? 我們發現,地位最高的雌貓鼬, 多數時候領著整個群體來到馬路邊, 但是到要橫穿馬路的時候, 她就讓路給她的下屬, 似乎用這種方式在說: 「去吧,告訴我是不是安全。」 事實上,我不知道 這些貓鼬遵循了什麼樣的行為規則 可以使群體的邊緣發生這樣的變化。 是不是用簡單的規則就足以解釋它。 因此,我建了一個模型。
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.
一個模擬貓鼬橫穿模擬馬路的模型。 這是一個非常簡化的模型。 正在移動的貓鼬就像隨機質點一樣, 其唯一的規則就是排成一列。 牠們只是一起行動。 當這些粒子到達馬路時, 牠們感覺到某種障礙, 然後牠們撞了上去。 那隻地位最高的雌貓鼬, 這里紅色的這只, 和其他個體的唯一差別, 就是對牠來說障礙物的高度, 也就是察覺到馬路上的風險, 稍稍大了一點兒。 這個個體運動規則中的 小小的區別 足以解釋我們所觀察到的現象, 也就是地位最高的雌貓鼬, 領著她的群體到馬路邊, 然後讓路給其他貓鼬, 讓牠們先過馬路。 喬治·鮑克斯是一個英國的統計學家, 曾經寫道:「所有的模型是虛構的, 但有些模型是有用的。」 事實上,這個模型顯然是假的, 因為在現實中貓鼬不是什麼隨機粒子。 但是這個模型也挺有用的, 因為它告訴我們這種極端簡單、 處於個體層面的運動規則, 到了群體的層面上 就可以導致很大的複合性。 再說一次,這就是簡化複合性。 最後我要說的是
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.
這對整個物種意味著什麼。 當地位最高的雌貓鼬 給下屬讓路, 牠不是出於禮貌。 事實上,地位最高的雌貓鼬 對這個群體的凝聚力來說非常重要。 如果她死在馬路上,整個集團將處於危險之中。 所以這種風險規避的行為 是一個非常古老的進化反應。 這些貓鼬只是重複進化而來的策略。 這個策略已經歷經成千上萬代了, 牠們只是用它來抵禦新風險, 在這裡新風險就是人類所建的一條路。 它們改進了非常簡單的規則, 而由此產生的複合行為 使牠們可以抵禦人類 對於牠們自然棲息地的入侵。 最後,
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.
這也許是蝙蝠通過改變牠們的社會結構 去應對群體的災難; 也許是貓鼬 對人造馬路表現出的新適應方式; 或者也可能是另一個物種。 我想傳達的不是一個複雜的問題, 而是一個簡單驚嘆與希望。 我想要表達的是 動物所顯示出非同尋常的社會複合性, 讓牠們能夠適應 並對牠們的環境變化作出回應。 用三個詞來說就是,在動物王國中, 簡單性導致複合性, 複合性又導致適應力。 謝謝。
Thank you.
(掌聲)
(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.
達尼亞格·哈特:尼古拉斯,非常感謝你 帶來這麼好的一個開場。有點緊張麼? 尼古拉斯·普萊尼:我還好,謝謝。 達尼亞·格哈特:好的,很好。我確信觀眾席中的許多人 試圖以某種方式給動物行為建立聯繫。 正如你所談到的蝙蝠和貓鼬,甚至是人類。 你帶來了幾個例子: 雌性是上流社會中的個體, 雌性還是有統治地位的個體, 我不確定誰會去思考為什麼會這樣。 但是,在人與動物之間做這樣的關聯妥當麼? 你有沒有一些在這方面可以確定的理論? 是否這樣的關聯在所有的物種之間都是可行的呢? 尼古拉斯·普萊尼: 我想說,確實也有 這些理論的反例。 舉個例子,在海馬或者無尾熊之中,其實, 總是由雄性來照顧他們的幼仔的。 我得到的經驗是,有時這是有些困難、 並且時常有一點危險 把人類與動物進行類比。 這也就是我的回答了。 達尼亞·格哈特:好的。感謝你如此精彩的開場。 尼古拉斯·普萊尼,謝謝你。