Thanks very much. I am Hannah Fry, the badass. And today I'm asking the question: Is life really that complex? Now, I've only got nine minutes to try and provide you with an answer, so what I've done is split this neatly into two parts: part one: yes; and later on, part two: no. Or, to be more accurate: no?
非常謝謝你們。 我是壞胚子漢娜佛萊。 今天我要問的問題是: 人生真的那麼複雜嗎? 我有九分鐘的時間可以 試著提供各位一個答案, 所以我要把這場演說 乾淨地切成兩部分: 第一部分:是的; 再來,第二部分:不是的。 或者,更精確一點:不是的?
(Laughter)
(笑聲)
So first of all, let me try and define what I mean by "complex." Now, I could give you a host of formal definitions, but in the simplest terms, any problem in complexity is something that Einstein and his peers can't do. So, let's imagine -- if the clicker works ... there we go. Einstein is playing a game of snooker. He's a clever chap, so he knows that when he hits the cue ball, he could write you an equation and tell you exactly where the red ball is going to hit the sides, how fast it's going and where it's going to end up. Now, if you scale these snooker balls up to the size of the solar system, Einstein can still help you. Sure, the physics changes, but if you wanted to know about the path of the Earth around the Sun, Einstein could write you an equation telling you where both objects are at any point in time. Now, with a surprising increase in difficulty, Einstein could include the Moon in his calculations. But as you add more and more planets, Mars and Jupiter, say, the problem gets too tough for Einstein to solve with a pen and paper. Now, strangely, if instead of having a handful of planets, you had millions of objects or even billions, the problem actually becomes much simpler, and Einstein is back in the game. Let me explain what I mean by this, by scaling these objects back down to a molecular level.
所以,首先,讓我先定義 我所謂的「複雜」。 我可以給各位一大堆正式的定義, 但用最簡單的話來說, 複雜的問題就是愛因斯坦 和他的同等級的人做不到的事。 所以,咱們來想像一下—— 如果這搖控器能用的話……好了 愛因斯坦在玩撞球比賽。 他是個聰明的傢伙, 所以他知道他撞擊白球時, 他能寫出一條方程式, 告訴你紅球會撞到桌邊的確切位置、 它速度多快及最後會停在哪裡。 如果把那些球的尺寸 放大到太陽系的尺寸, 愛因斯坦還是能幫你計算。 的確,物理學會改變, 但如果你想要知道 地球繞太陽轉動的路徑, 愛因斯坦能幫你寫出一條方程式, 告訴你在任何時點時 兩個物體的所在。 現在,難度大大增加, 愛因斯坦可以把月球 也納入計算考量中。 但當你加入更多星球, 比如火星和木星, 問題就變得太困難,連愛因斯坦 也無法用紙筆來解決了。 奇怪的是,若不是有許多星球, 而是有數百萬個物體, 甚至數十億個, 問題反而會變得簡單許多, 而愛因斯坦又可以上陣了。 讓我解釋一下我為什麼這麼說, 我們將這些物體的尺寸 縮小為分子的等級來解釋。
If you wanted to trace the erratic path of an individual air molecule, you'd have absolutely no hope. But when you have millions of air molecules all together, they start to act in a way which is quantifiable, predictable and well-behaved. And thank goodness air is well-behaved, because if it wasn't, planes would fall out of the sky. Now, on an even bigger scale, across the whole of the world, the idea is exactly the same with all of these air molecules. It's true that you can't take an individual rain droplet and say where it's come from or where it's going to end up. But you can say with pretty good certainty whether it will be cloudy tomorrow. So that's it. In Einstein's time, this is how far science had got. We could do really small problems with a few objects with simple interactions, or we could do huge problems with millions of objects and simple interactions. But what about everything in the middle?
如果你想要追蹤一個個別 空氣分子的不規律路徑, 你是毫無希望的。 但當你有數百萬個 空氣分子在一起時, 它們的行為會變得開始 可以量化、可以預測, 且行為良好。 謝天謝地,空氣的行為很良好, 因為如果不是的話, 飛機就會從天上掉下來。 若把範圍放更大, 延伸到整個世界, 這個想法對所有的空氣分子而言 都是完全一樣的。 事實是,你無法選取 任何一滴個別的雨滴, 然後就判斷出它從何而來, 或是會落在哪裡。 但你可以蠻肯定地說 明天哪裡會多雲。 所以,就這樣。 在愛因斯坦的時代, 科學就只走到那個地步。 我們可以解非常小的問題, 只要用幾個物體, 和簡單的交互作用; 或我們可以解很大的問題, 用數百萬個物體, 和簡單的交互作用。 但中間的所有其他狀況呢?
Well, just seven years before Einstein's death, an American scientist called Warren Weaver made exactly this point. He said that scientific methodology has gone from one extreme to another, leaving out an untouched great middle region. Now, this middle region is where complexity science lies, and this is what I mean by complex. Now, unfortunately, almost every single problem you can think of to do with human behavior lies in this middle region. Einstein's got absolutely no idea how to model the movement of a crowd. There are too many people to look at them all individually and too few to treat them as a gas. Similarly, people are prone to annoying things like decisions and not wanting to walk into each other, which makes the problem all the more complicated. Einstein also couldn't tell you when the next stock market crash is going to be. Einstein couldn't tell you how to improve unemployment. Einstein can't even tell you whether the next iPhone is going to be a hit or a flop. So to conclude part one: we're completely screwed. We've got no tools to deal with this, and life is way too complex.
在愛因斯坦死前七年, 有一位美國科學家叫做瓦倫韋弗, 他就建立了這個論點。 他說科學方法論已經 從一個極端跑到了另一個極端, 留下了中間區域沒有被觸及。 這中間區域就是 複雜科學所在之處, 這就是我所謂的複雜。 不幸的是,幾乎你能 想出與人類行為有關的 每一個問題, 都落在這個中間區域。 愛因斯坦完全不知道要如何 針對一群人的移動來建立模型。 有太多人,無法 一個一個個別來看, 但又沒多到可以當氣體來看待。 同樣的,人常會做煩人的事, 如決策之類的, 且不想要走路時撞到人, 這就讓問題變得更複雜了。 愛因斯坦無法告訴你 股市下次崩盤會是何時。 愛因斯坦無法告訴你 如何改善失業率。 愛因斯坦甚至無法告訴你 下一版的 iPhone 會是 大成功還是大失敗。 所以,第一部分的 結論是,我們死定了。 我們沒有工具來解決這些, 人生太複雜了。
But maybe there's hope, because in the last few years, we've begun to see the beginnings of a new area of science using mathematics to model our social systems. And I'm not just talking here about statistics and computer simulations. I'm talking about writing down equations about our society that will help us understand what's going on in the same way as with the snooker balls or the weather prediction. And this has come about because people have begun to realize that we can use and exploit analogies between our human systems and those of the physical world around us.
但,也許,還有希望, 因為在過去幾年, 我們看到一個新的 科學領域開始出現了, 它是用數學來為我們的 社會系統建立模型。 我並不只是在談統計和電腦模擬。 我在談的是針對我們的 社會,寫下方程式, 來協助我們了解發生了什麼事, 就像是撞球或是氣象預測一樣。 會有這樣的演變, 是因為大家開始了解 我們可以使用和利用人類系統 和我們周圍實體世界的 系統之間的相似處。
Now, to give you an example: the incredibly complex problem of migration across Europe. Actually, as it turns out, when you view all of the people together, collectively, they behave as though they're following the laws of gravity. But instead of planets being attracted to one another, it's people who are attracted to areas with better job opportunities, higher pay, better quality of life and lower unemployment. And in the same way as people are more likely to go for opportunities close to where they live already -- London to Kent, for example, as opposed to London to Melbourne -- the gravitational effect of planets far away is felt much less.
讓我舉個例子: 極複雜的歐洲各地移民問題。 其實,結果發現, 如果你一次一起看所有的人, 他們集體看起來就像是 遵守著萬有引力定律。 但並不是星球彼此牽引, 而是人被吸引到 工作機會更佳、薪水更高、 生活品質更好, 失業率更低的區域。 如同人比較有可能被現居地 鄰近區域的機會所吸引—— 如倫敦到肯特郡, 而不是倫敦到墨爾本—— 星球之間若距離較長, 影響也會少很多。
So, to give you another example: in 2008, a group in UCLA were looking into the patterns of burglary hot spots in the city. Now, one thing about burglaries is this idea of repeat victimization. So if you have a group of burglars who manage to successfully rob an area, they'll tend to return to that area and carry on burgling it. So they learn the layout of the houses, the escape routes and the local security measures that are in place. And this will continue to happen until local residents and police ramp up the security, at which point, the burglars will move off elsewhere. And it's that balance between burglars and security which creates these dynamic hot spots of the city. As it turns out, this is exactly the same process as how a leopard gets its spots, except in the leopard example, it's not burglars and security, it's the chemical process that creates these patterns and something called "morphogenesis." We actually know an awful lot about the morphogenesis of leopard spots. Maybe we can use this to try and spot some of the warning signs with burglaries and perhaps, also to create better crime strategies to prevent crime. There's a group here at UCL who are working with the West Midlands police right now on this very question. I could give you plenty of examples like this, but I wanted to leave you with one from my own research on the London riots.
所以,再舉一個例子: 2008 年,洛杉磯 加州大學的一個團隊在研究 城市中的竊盜案高危險區。 竊盜有一個特點, 就是重覆選擇同一個受害區域。 所以,如果有一群竊賊 有辦法在一個區域成功搶劫, 他們會傾向再次返回該區域, 進行下一次搶劫。 他們會了解房屋的分佈、 逃脫的路線, 以及已經啟用的保全措施。 竊盜會不斷發生, 直到當地居民和警方加強保安, 到了這個時點, 竊賊就會改去其他地方。 竊賊和保全之間的平衡 創造出了城市中 不斷改變的高危險區域。 結果發現,這個過程就和美洲豹 得到身上斑點的過程相同, 差別在於,在美洲豹的例子中, 並不是竊賊和保全, 是一個化學過程 創造出了這些圖案, 還有所謂的「形態發生」。 我們其實對於美洲豹斑點的 形態發生所知甚多。 也許我們可以利用這一點, 試著找出將會發生竊盜的警訊, 也許,也能創造出 更好的犯罪策略來預防犯罪。 在倫敦大學學院有個團隊 現在正在和西密德蘭郡警方合作, 研究這個問題。 我可以提出很多像這樣的例子, 但我想要舉最後一個例子, 是我自己的倫敦暴動研究。
Now, you probably don't need me to tell you about the events of last summer, where London and the UK saw the worst sustained period of violent looting and arson for over twenty years. It's understandable that, as a society, we want to try and understand exactly what caused these riots, but also, perhaps, to equip our police with better strategies to lead to a swifter resolution in the future. Now, I don't want to upset the sociologists here, so I absolutely cannot talk about the individual motivations for a rioter, but when you look at the rioters all together, mathematically, you can separate it into a three-stage process and draw analogies accordingly.
應該不需要由我來告訴各位 去年夏天發生的事件, 倫敦和英國經歷了一段暴力搶劫 和縱火的時期, 是二十年來最長最糟的。 可以理解我們這個社會 會想要試著搞懂 到底暴動是如何造成的, 也許,也還想要協助警方 想出更好的策略, 在未來能夠有更快速的解決方案。 我並不想澆社會學家冷水, 所以我絕對不談暴動者的個別動機, 但當你把所有暴動者 當成整體看待時, 在數學上,你就能將它 分成一個三階段的過程, 並做出類推。
So, step one: let's say you've got a group of friends. None of them are involved in the riots, but one of them walks past a Foot Locker which is being raided, and goes in and bags himself a new pair of trainers. He texts one of his friends and says, "Come on down to the riots." So his friend joins him, and then the two of them text more of their friends, who join them, and text more of their friends and more and more, and so it continues. This process is identical to the way that a virus spreads through a population. If you think about the bird flu epidemic of a couple of years ago, the more people that were infected, the more people that got infected, and the faster the virus spread before the authorities managed to get a handle on events. And it's exactly the same process here.
步驟一:假設你有一群朋友。 他們都沒有涉入暴動, 但其中一人走路經過 被襲擊的 Foot Locker 商店, 他走進商店,為自己 拿了一雙新的運動鞋。 他傳訊息給一位朋友,說: 「一起來暴動吧。」 於是,他的朋友加入他, 接著,他們又傳訊息給更多朋友, 這些朋友也加入了, 再傳訊息給更多朋友, 越來越多,一直持續。 這個過程就像是病毒 在人口中散播的方式。 如果想想看幾年前的 禽流感大流行, 越多人受到感染, 造成更多人受到感染, 病毒的散播也更快速, 當權機關來不及處理這些事件。 這裡的過程也完全一樣。
So let's say you've got a rioter, he's decided he's going to riot. The next thing he has to do is pick a riot site. Now, what you should know about rioters is that, um ... Oops, clicker's gone. There we go. What you should know about rioters is, they're not prepared to travel that far from where they live, unless it's a really juicy riot site.
假設有一位暴動者決定要暴動。 他接下來要做的事, 就是選一個暴動地點。 關於暴動者,各位應該要 知道一件事,呃…… 喔,搖控器不管用。好了。 各位應該要知道,暴動者沒準備要 到離家很遠的地方, 除非是個非常值得的暴動地點。
(Laughter)
(笑聲)
So you can see that here from this graph, with an awful lot of rioters having traveled less than a kilometer to the site that they went to. Now, this pattern is seen in consumer models of retail spending, i.e., where we choose to go shopping. So, of course, people like to go to local shops, but you'd be prepared to go a little bit further if it was a really good retail site. And this analogy, actually, was already picked up by some of the papers, with some tabloid press calling the events "Shopping with violence," which probably sums it up in terms of our research. Oh! -- we're going backwards.
各位可以從這張圖中看到, 有非常多的暴動者 前往暴動地點的距離 還不到一公里。 在零售業花費上的消費者模型中 就可以看到這個模式, 即我們選擇要在哪裡購物的模式。 所以,當然,大家 都喜歡去當地的店家, 但如果有個比較遠的 零售店很不錯, 你會有打算要走遠一點。 這個類推其實已經 被一些報紙採用, 有些小報把這些事件稱為 「用暴力來購物」, 這個名稱可說是 為我們研究做了個總結。 喔!放成前一張了。
OK, step three. Finally, the rioter is at his site, and he wants to avoid getting caught by the police. The rioters will avoid the police at all times, but there is some safety in numbers. And on the flip side, the police, with their limited resources, are trying to protect as much of the city as possible, arrest rioters wherever possible and to create a deterrent effect. And actually, as it turns out, this mechanism between the two species, so to speak, of rioters and police, is identical to predators and prey in the wild. So if you can imagine rabbits and foxes, rabbits are trying to avoid foxes at all costs, while foxes are patrolling the space, trying to look for rabbits. We actually know an awful lot about the dynamics of predators and prey. We also know a lot about consumer spending flows. And we know a lot about how viruses spread through a population.
好了,步驟三。 最後,暴動者到了他的地點, 他想要避免被警方抓到。 暴動者會隨時避開警方, 但數字顯示有些安全點。 一方面,警方的資源有限, 他們要盡可能保護城市中 越多的區域越好, 只要有可能,就要逮捕暴動者, 並製造出威懾效果。 結果發現, 在暴動者和警方 這兩種「物種」之間的機制 和野外的捕食者與獵物相同。 你們可以想像兔子和狐狸, 兔子會不計代價避開狐狸, 而狐狸會在各處巡邏, 試著找到兔子。 其實我們對於捕食者 和獵物的動態了解甚多。 我們很了解消費者花費的金流。 我們也很了解 病毒如何在人群中散播。
So if you take these three analogies together and exploit them, you can come up with a mathematical model of what actually happened, that's capable of replicating the general patterns of the riots themselves. Now, once we've got this, we can almost use this as a petri dish and start having conversations about which areas of the city were more susceptible than others and what police tactics could be used if this were ever to happen again in the future. Even twenty years ago, modeling of this sort was completely unheard of. But I think that these analogies are an incredibly important tool in tackling problems with our society, and perhaps, ultimately improving our society overall.
所以如果把這三種類推 結合起來並利用它們, 你就可以針對真正發生的狀況, 找出一個數學模型, 這個模型能夠複製出暴動本身的 一般性模式。 一旦做到這點了,我們就能 把它當作培養皿來用, 並可以開始談論 城市中的哪些區域 比其他區域更容易受影響, 以及如果未來同樣的狀況再發生時, 警方該用什麼戰術。 即使是在二十年前, 也沒有人聽過這種建模方式。 但我認為這些類推 是非常重要的工具, 能夠追蹤我們社會的問題, 也許,最終還能 改善我們的整個社會。
So, to conclude: life is complex, but perhaps understanding it need not necessarily be that complicated.
所以,結論是:人生很複雜, 但也許,了解人生未必那麼複雜。
Thank you.
謝謝。
(Applause)
(掌聲)