For me, this story begins about 15 years ago, when I was a hospice doctor at the University of Chicago. And I was taking care of people who were dying and their families in the South Side of Chicago. And I was observing what happened to people and their families over the course of their terminal illness. And in my lab, I was studying the widower effect, which is a very old idea in the social sciences, going back 150 years, known as "dying of a broken heart." So, when I die, my wife's risk of death can double, for instance, in the first year. And I had gone to take care of one particular patient, a woman who was dying of dementia. And in this case, unlike this couple, she was being cared for by her daughter. And the daughter was exhausted from caring for her mother. And the daughter's husband, he also was sick from his wife's exhaustion. And I was driving home one day, and I get a phone call from the husband's friend, calling me because he was depressed about what was happening to his friend. So here I get this call from this random guy that's having an experience that's being influenced by people at some social distance.
大約15年前 我在芝加哥大學擔任安寧病房醫生 我負責照顧那些瀕臨死亡的人,與他們的家人 就在芝加哥南端 我觀察這些疾病末期的人,與他們家人, 疾病帶給他們的影響 我在實驗室研究守寡效應 這個想法不新穎 150年前就有了 就是大家所知的「心碎而死」 就是,我死了,我妻子的死亡率在第一年 會增加一倍。 當時我照顧一個 失智症的婦人 不像其他病人 她是由她女兒 負責照顧。 為照顧母親,她女兒已心力憔悴 而她女婿 也因為妻子的憔悴 而生病了。 我有天開車回家 接到一通女婿的朋友打來的電話 他說,因為他朋友(女婿)生病 他也心情低落。 這通陌生人的電話 讓我有了這個體驗 原來人與人的影響 不止於親近的人。
And so I suddenly realized two very simple things: First, the widowhood effect was not restricted to husbands and wives. And second, it was not restricted to pairs of people. And I started to see the world in a whole new way, like pairs of people connected to each other. And then I realized that these individuals would be connected into foursomes with other pairs of people nearby. And then, in fact, these people were embedded in other sorts of relationships: marriage and spousal and friendship and other sorts of ties. And that, in fact, these connections were vast and that we were all embedded in this broad set of connections with each other. So I started to see the world in a completely new way and I became obsessed with this. I became obsessed with how it might be that we're embedded in these social networks, and how they affect our lives. So, social networks are these intricate things of beauty, and they're so elaborate and so complex and so ubiquitous, in fact, that one has to ask what purpose they serve. Why are we embedded in social networks? I mean, how do they form? How do they operate? And how do they effect us?
我因此意識到兩件很簡單的事情 第一,守寡效應 並不侷限於夫妻 第二,並不侷限於兩個人而已 我開始以全新的視角 來看這世界 人們一對對連結著 然後又有其他個體 與鄰近的這對連結,變成兩對 然後這些人 又被其他關係包圍著 婚姻、夫妻、 友情等等連結 事實上,這些連結很廣 我們每個人之間 都是被這許多的連結連起來的。 我開始用全新的角度看這世界 並為此著迷 我著迷於圍繞著 每個人的人際網路 與它的影響。 人際關係是種美麗亦複雜的東西 它既精密、複雜 卻又普及,事實上, 我們會問,它的功能是什麼? 我們為什麼會處於人際網路中? 它們如何形成?怎麼運作? 是怎麼影響我們的?
So my first topic with respect to this, was not death, but obesity. It had become trendy to speak about the "obesity epidemic." And, along with my collaborator, James Fowler, we began to wonder whether obesity really was epidemic and could it spread from person to person like the four people I discussed earlier. So this is a slide of some of our initial results. It's 2,200 people in the year 2000. Every dot is a person. We make the dot size proportional to people's body size; so bigger dots are bigger people. In addition, if your body size, if your BMI, your body mass index, is above 30 -- if you're clinically obese -- we also colored the dots yellow. So, if you look at this image, right away you might be able to see that there are clusters of obese and non-obese people in the image. But the visual complexity is still very high. It's not obvious exactly what's going on. In addition, some questions are immediately raised: How much clustering is there? Is there more clustering than would be due to chance alone? How big are the clusters? How far do they reach? And, most importantly, what causes the clusters?
我的第一個要探討的主題, 不是關於死亡,而是肥胖 突然間,大家都討論著 「肥胖流行症」 我和James Fowler合作 共同研究為什麼肥胖會流行 還有它的傳染是否像我剛所提的 那四個人那樣 這是我們最初的結果 2000年研究的2200人 每個點是一個人,我們依據 體型來做點的大小 大點點的人體型較大 還有,體型、 BMI值超過30以上的 在醫學上被診斷為肥胖的 我們標為黃點 各位可以看到這張圖 肥胖的人聚成一團 不胖的人聚成一團 不過視覺上看起來還是很複雜 真正的情況看得不明顯。 另一個馬上想到的問題是 圖中有多少聚集? 聚集的產生是否不單因為巧合? 這些聚集有多大?各自距離有多遠? 最重要的還有 形成聚集原因是什麼?
So we did some mathematics to study the size of these clusters. This here shows, on the Y-axis, the increase in the probability that a person is obese given that a social contact of theirs is obese and, on the X-axis, the degrees of separation between the two people. On the far left, you see the purple line. It says that, if your friends are obese, your risk of obesity is 45 percent higher. And the next bar over, the [red] line, says if your friend's friends are obese, your risk of obesity is 25 percent higher. And then the next line over says if your friend's friend's friend, someone you probably don't even know, is obese, your risk of obesity is 10 percent higher. And it's only when you get to your friend's friend's friend's friends that there's no longer a relationship between that person's body size and your own body size.
所以我們將這些聚集的大小數據化 可以看到,縱軸是 一個人因為週遭朋友 而變胖的可能性 橫軸是兩個人之間,分離的程度 最左邊,紫色長條顯示 如果你的朋友都過胖 你過胖的機率比別人高45% 旁邊的紅色長條 代表如果你朋友的朋友都過胖 你過胖的機率比平均高出25% 下一個長條表示 如果你朋友的朋友的朋友-即使你都不認識-過胖 你過胖的機率比平均高出10% 只有到了你朋友的朋友的朋友的朋友 幾乎沒有關係可言 你們的體型才不會互相影響。
Well, what might be causing this clustering? There are at least three possibilities: One possibility is that, as I gain weight, it causes you to gain weight. A kind of induction, a kind of spread from person to person. Another possibility, very obvious, is homophily, or, birds of a feather flock together; here, I form my tie to you because you and I share a similar body size. And the last possibility is what is known as confounding, because it confounds our ability to figure out what's going on. And here, the idea is not that my weight gain is causing your weight gain, nor that I preferentially form a tie with you because you and I share the same body size, but rather that we share a common exposure to something, like a health club that makes us both lose weight at the same time.
那形成這種聚集的原因是什麼? 至少三種可能 第一,當我體重增加 你體重也增加 這是誘導性,在人與人之間的傳染 第二,很明顯的,同質性 也就是「物以類聚,人以群分」 我和你的聯繫 是因為我們體型相同 最後一個可能性是混雜法 我們搞不清楚狀況是什麼 意思是,你體重增加的原因 不是因為我體重增加 也不是我選擇與你有關聯 而是因為我們有一樣的體型 所以我們會去類似的地方 例如健身房等等 我們一起瘦身的地方
When we studied these data, we found evidence for all of these things, including for induction. And we found that if your friend becomes obese, it increases your risk of obesity by about 57 percent in the same given time period. There can be many mechanisms for this effect: One possibility is that your friends say to you something like -- you know, they adopt a behavior that spreads to you -- like, they say, "Let's go have muffins and beer," which is a terrible combination. (Laughter) But you adopt that combination, and then you start gaining weight like them. Another more subtle possibility is that they start gaining weight, and it changes your ideas of what an acceptable body size is. Here, what's spreading from person to person is not a behavior, but rather a norm: An idea is spreading.
我們研究這些數據,發現以下一些證據 包含誘導性 我們發現,如果你的朋友變胖 同一時期裡,你變胖的機會 立刻增加57% 造成這種效果有很多機制 一種情況是,你朋友的行為傳染給你 他們可能會對你說: 「我們吃馬芬鬆糕配啤酒吧」 這搭配好糟糕 但你習慣這樣吃以後 你就會開始和他們一樣變胖 另一種可能 是他們開始增胖,你開始改變了 對於正常體型的看法 這種人與人傳染情況 不是行為改變,而是標準改變。 有越來越多人接受這種想法。
Now, headline writers had a field day with our studies. I think the headline in The New York Times was, "Are you packing it on? Blame your fat friends." (Laughter) What was interesting to us is that the European headline writers had a different take: They said, "Are your friends gaining weight? Perhaps you are to blame." (Laughter) And we thought this was a very interesting comment on America, and a kind of self-serving, "not my responsibility" kind of phenomenon.
有些記者 將我們的研究寫成報導 我想紐約時報的頭條是: 「變胖了嗎?」 「怪你朋友吧!」 我們覺得有趣的是,歐洲的記者 寫了不同的頭條: 「你朋友變胖了嗎?是你害的!」 (笑聲) 我們覺得很有趣,這反應出美國人那種 有點自私、 「不干我的事」的態度
Now, I want to be very clear: We do not think our work should or could justify prejudice against people of one or another body size at all. Our next questions was: Could we actually visualize this spread? Was weight gain in one person actually spreading to weight gain in another person? And this was complicated because we needed to take into account the fact that the network structure, the architecture of the ties, was changing across time. In addition, because obesity is not a unicentric epidemic, there's not a Patient Zero of the obesity epidemic -- if we find that guy, there was a spread of obesity out from him -- it's a multicentric epidemic. Lots of people are doing things at the same time. And I'm about to show you a 30 second video animation that took me and James five years of our lives to do. So, again, every dot is a person. Every tie between them is a relationship. We're going to put this into motion now, taking daily cuts through the network for about 30 years.
到此,我要澄清,我們並不認為 這研究能被拿來 當作身材歧視的正當理由 我們下一個問題是: 這種擴散要如何視覺化? 一個人變胖是否會連帶影響 另一個人的體重? 這很複雜 因為我們要考慮到網路的結構、 連結的構造方式,是隨時在改變的 還有,肥胖症不是種只有單一中心的流行病 沒有肥胖流行病的「零號病人」- 疾病的原始帶原者是不存在的 它是有許多中心的 很多人同時做著相同的事 我給大家看個30秒動畫 我和James花五年研究出來的 每個點都是一個人 每條線表示他們的關連 我們現在放給大家看 一睹30年的人際網路變化
The dot sizes are going to grow, you're going to see a sea of yellow take over. You're going to see people be born and die -- dots will appear and disappear -- ties will form and break, marriages and divorces, friendings and defriendings. A lot of complexity, a lot is happening just in this 30-year period that includes the obesity epidemic. And, by the end, you're going to see clusters of obese and non-obese individuals within the network. Now, when looked at this, it changed the way I see things, because this thing, this network that's changing across time, it has a memory, it moves, things flow within it, it has a kind of consistency -- people can die, but it doesn't die; it still persists -- and it has a kind of resilience that allows it to persist across time.
點的大小開始變化 會看到越來越多黃點 也可以看到人們的出生、死亡 點的消失與形成 連結的形成與斷裂,結婚與離婚 友情的產生與破裂 非常複雜,這30年時間 發生了許多事情 包括肥胖的流行 最後,你可以看到 肥胖、不肥胖的個體 在這網路裡 看著這張圖 改變了我看事情的角度 因為這個網路 隨時間變換的網路 它是有記憶的、會移動的 裏面也有很多流動 它擁有著一種持續性 人會死亡,但它不會 永久存在 它有種恢復力 能隨時間存在著
And so, I came to see these kinds of social networks as living things, as living things that we could put under a kind of microscope to study and analyze and understand. And we used a variety of techniques to do this. And we started exploring all kinds of other phenomena. We looked at smoking and drinking behavior, and voting behavior, and divorce -- which can spread -- and altruism. And, eventually, we became interested in emotions. Now, when we have emotions, we show them. Why do we show our emotions? I mean, there would be an advantage to experiencing our emotions inside, you know, anger or happiness. But we don't just experience them, we show them. And not only do we show them, but others can read them. And, not only can they read them, but they copy them. There's emotional contagion that takes place in human populations. And so this function of emotions suggests that, in addition to any other purpose they serve, they're a kind of primitive form of communication. And that, in fact, if we really want to understand human emotions, we need to think about them in this way.
我將這些人際網路視為 活的東西 是我們可以放到顯微鏡下觀察、研究、 並加以了解的東西 我們用了很多方式研究 並開始探索其他現象 我們觀察吸菸、酗酒的人、 有投票習慣的人、 離婚的人-這也會傳染 還有無私。 最後,我們對情緒感興趣 人的情緒一來 馬上展現出來 為什麼展現情緒? 我是說,如果能把生氣、開心等情緒 放在心裡應該是種優點吧 我們不只有情緒,我們會展現出來 我們不只會展現出來,其他人還解讀的出來 他們不只解讀的出來,還會複製那情緒 這就是人類社會的 情緒傳染 這些情緒的功能 還顯示他們有其他用途 他們是一種最基本的溝通方式 如果我們真想了解人類的情緒 我們就需要將之視為如此
Now, we're accustomed to thinking about emotions in this way, in simple, sort of, brief periods of time. So, for example, I was giving this talk recently in New York City, and I said, "You know when you're on the subway and the other person across the subway car smiles at you, and you just instinctively smile back?" And they looked at me and said, "We don't do that in New York City." (Laughter) And I said, "Everywhere else in the world, that's normal human behavior." And so there's a very instinctive way in which we briefly transmit emotions to each other. And, in fact, emotional contagion can be broader still. Like we could have punctuated expressions of anger, as in riots. The question that we wanted to ask was: Could emotion spread, in a more sustained way than riots, across time and involve large numbers of people, not just this pair of individuals smiling at each other in the subway car? Maybe there's a kind of below the surface, quiet riot that animates us all the time. Maybe there are emotional stampedes that ripple through social networks. Maybe, in fact, emotions have a collective existence, not just an individual existence.
我們短時間內,已經習慣 將情緒視為溝通方式 舉個例子, 我最近在紐約也做了演講 我說:「你搭地鐵時,」 「坐你對面的人」 「對你微笑」 「你會直覺的也對他微笑。」 觀眾看著我,說:「在紐約沒人這樣做。」 我說:「紐約除外,世界其他地方」 「這是正常現象。」 這是種直覺性的動作 簡單的將情緒傳給他人 事實上,這種情緒傳染是可以很廣的 就像暴動中,展現憤怒情緒 的間接表達 我們想問的問題是: 情緒傳染是否不止 在地鐵中相互微笑的兩人而已 是否可以有更多人 甚至是更永續、跨時間的方式? 或許有那種表面下的暴動 永久地控制我們。 也許人際網路中也有 情緒潰散的情形 又或許,情緒有種聚集存在 而非只是個體存在。
And this is one of the first images we made to study this phenomenon. Again, a social network, but now we color the people yellow if they're happy and blue if they're sad and green in between. And if you look at this image, you can right away see clusters of happy and unhappy people, again, spreading to three degrees of separation. And you might form the intuition that the unhappy people occupy a different structural location within the network. There's a middle and an edge to this network, and the unhappy people seem to be located at the edges. So to invoke another metaphor, if you imagine social networks as a kind of vast fabric of humanity -- I'm connected to you and you to her, on out endlessly into the distance -- this fabric is actually like an old-fashioned American quilt, and it has patches on it: happy and unhappy patches. And whether you become happy or not depends in part on whether you occupy a happy patch.
這是我們為這個研究所做的圖 同樣的,一個人際網路 黃點是快樂的人 藍點是傷心的,綠點是其他 這張圖可以明顯看出 快樂、不快樂的人聚集 有三種程度的分散 馬上看的出來 不快樂的人 聚集在網路裡的不同地點 網路有中間及邊緣 而不快樂的人似乎都 聚在邊緣 用東西來比喻的話 可以將人際網路想成是 一塊人性的布料 我與你連結、你與她連結,無限的距離 這塊布料就有點像 老舊的美製棉被 上面有補丁,開心的、傷心的補丁 而你快樂與否 取決於你是否在快樂補丁上
(Laughter)
(笑聲)
So, this work with emotions, which are so fundamental, then got us to thinking about: Maybe the fundamental causes of human social networks are somehow encoded in our genes. Because human social networks, whenever they are mapped, always kind of look like this: the picture of the network. But they never look like this. Why do they not look like this? Why don't we form human social networks that look like a regular lattice? Well, the striking patterns of human social networks, their ubiquity and their apparent purpose beg questions about whether we evolved to have human social networks in the first place, and whether we evolved to form networks with a particular structure.
所以,我們對情緒的研究 是非常基本的 後來我們想,也許 影響人際關係的根本原因 也許和基因有關 因為人際關係,不論如何塑造 都是長這樣 這是人際網路圖 但從來不會像這樣 為什麼不像這樣? 為什麼我們的人際網路 不像這樣的點陣圖? 人際網路的獨特圖形 這種普遍性,和明顯的目的 引出了一個問題:我們是否 天生就有這種人際網路, 或者我們網路獨特樣貌的形成 是後天進化的?
And notice first of all -- so, to understand this, though, we need to dissect network structure a little bit first -- and notice that every person in this network has exactly the same structural location as every other person. But that's not the case with real networks. So, for example, here is a real network of college students at an elite northeastern university. And now I'm highlighting a few dots. If you look here at the dots, compare node B in the upper left to node D in the far right; B has four friends coming out from him and D has six friends coming out from him. And so, those two individuals have different numbers of friends. That's very obvious, we all know that. But certain other aspects of social network structure are not so obvious.
要解答這問題 我們需要解剖這網路 注意這網路的每個人 與其他人同處相同的地點 但真正的網路並非如此 這裡是一間東北方頂尖大學的 學生人際關係圖 我標出幾個明顯的點 看看這些點 比較左上的節點B 與最右邊的節點D B有四個朋友 而D有六個朋友 這兩個人有不同數量的朋友 很明顯啊,不用解釋 但其他方面 這種人際結構就沒那麼明顯了
Compare node B in the upper left to node A in the lower left. Now, those people both have four friends, but A's friends all know each other, and B's friends do not. So the friend of a friend of A's is, back again, a friend of A's, whereas the friend of a friend of B's is not a friend of B's, but is farther away in the network. This is known as transitivity in networks. And, finally, compare nodes C and D: C and D both have six friends. If you talk to them, and you said, "What is your social life like?" they would say, "I've got six friends. That's my social experience." But now we, with a bird's eye view looking at this network, can see that they occupy very different social worlds. And I can cultivate that intuition in you by just asking you: Who would you rather be if a deadly germ was spreading through the network? Would you rather be C or D? You'd rather be D, on the edge of the network. And now who would you rather be if a juicy piece of gossip -- not about you -- was spreading through the network? (Laughter) Now, you would rather be C.
比較節點B與左下的節點A 這些人各有四個朋友 但A的朋友互相認識 而B的朋友則不是 所以A的朋友的朋友 也是A的朋友 然而,B的朋友的朋友,不是B的朋友 而是在網路的更遠端 這是網路的傳遞性 最後,比較節點C、節點D 兩者都有六個朋友, 如果你問:「你的社交生活如何?」 他們會答:「我有六個朋友,」 「這是我的交友經驗」 現在我們鳥瞰這張圖 可以發現他們的社交圈是完全不同的 現在用直覺回答這問題: 如果有種致命病毒 正在這網路傳播 你要選節點C還是D? 你會選D,在人際網路邊緣 現在,如果是聊八卦 講別人的八卦,不是你的 這種情況你選哪個? 你會選C吧
So different structural locations have different implications for your life. And, in fact, when we did some experiments looking at this, what we found is that 46 percent of the variation in how many friends you have is explained by your genes. And this is not surprising. We know that some people are born shy and some are born gregarious. That's obvious. But we also found some non-obvious things. For instance, 47 percent in the variation in whether your friends know each other is attributable to your genes. Whether your friends know each other has not just to do with their genes, but with yours. And we think the reason for this is that some people like to introduce their friends to each other -- you know who you are -- and others of you keep them apart and don't introduce your friends to each other. And so some people knit together the networks around them, creating a kind of dense web of ties in which they're comfortably embedded. And finally, we even found that 30 percent of the variation in whether or not people are in the middle or on the edge of the network can also be attributed to their genes. So whether you find yourself in the middle or on the edge is also partially heritable.
所以不同的結構點 對於人生有不同的含意 事實上,我們為此做了些實驗 朋友數量多寡的差異 有46%都是可以用基因 來解釋 這並不新奇,我們都知道,有些人天生害羞 有些人天生合群,這是顯而易見的 但我們也發現了些不那麼明顯的東西 例如,你的朋友們是否互相認識 其中47%的差異 是和你的基因有關。 你的朋友是否互相認識 是與你的基因有關,而不是他們的。 我們認為,這原因在於有些人 喜歡把自己的朋友介紹給彼此 而其他人喜歡把朋友們分開,不介紹給彼此 所以有些人將他們的人際網路編織在一起 形成了緊密的網路 並舒服的身處其中 最後,我們還發現 不論你是處在 網路中心或邊緣,30%的差異 也是和基因有關 所以不管你是在中心還是邊緣 有一部分是遺傳的
Now, what is the point of this? How does this help us understand? How does this help us figure out some of the problems that are affecting us these days? Well, the argument I'd like to make is that networks have value. They are a kind of social capital. New properties emerge because of our embeddedness in social networks, and these properties inhere in the structure of the networks, not just in the individuals within them. So think about these two common objects. They're both made of carbon, and yet one of them has carbon atoms in it that are arranged in one particular way -- on the left -- and you get graphite, which is soft and dark. But if you take the same carbon atoms and interconnect them a different way, you get diamond, which is clear and hard. And those properties of softness and hardness and darkness and clearness do not reside in the carbon atoms; they reside in the interconnections between the carbon atoms, or at least arise because of the interconnections between the carbon atoms. So, similarly, the pattern of connections among people confers upon the groups of people different properties. It is the ties between people that makes the whole greater than the sum of its parts. And so it is not just what's happening to these people -- whether they're losing weight or gaining weight, or becoming rich or becoming poor, or becoming happy or not becoming happy -- that affects us; it's also the actual architecture of the ties around us.
所以,這表示什麼? 它如何讓我們了解這世界? 它如何幫助我們 了解我們現在所面臨的問題? 我的論點是,這些人際網路充滿價值 就如一種社會資產 新特性的出現 是因為包圍我們的人際網路 以及形成網路結構 所固有的這些特性 不單只是其中的個體而已 看看這兩個常見的東西 都用碳做的 但其中一個是碳原子 以獨特的方式組合而成 就是左邊的石墨,柔軟漆黑 一樣的碳原子 以不同的方式組合 就變成鑽石,透徹堅硬 而這些柔軟、堅硬、漆黑、透徹的屬性 並不是碳原子造成的 而是碳原子間的組合方式 或者說,至少是因為 碳原子間的組合方式造成的 同樣的,人與人之間的關聯 也賜與各組群 不同的屬性 正是這種連結 讓整體變的比個體還好很多。 所以,不只是這些人所經歷的事- 像減肥、增肥,變有錢或變窮、 變得快樂、不快樂-在影響著我們 同時影響我們的 還有我們的連結架構。
Our experience of the world depends on the actual structure of the networks in which we're residing and on all the kinds of things that ripple and flow through the network. Now, the reason, I think, that this is the case is that human beings assemble themselves and form a kind of superorganism. Now, a superorganism is a collection of individuals which show or evince behaviors or phenomena that are not reducible to the study of individuals and that must be understood by reference to, and by studying, the collective. Like, for example, a hive of bees that's finding a new nesting site, or a flock of birds that's evading a predator, or a flock of birds that's able to pool its wisdom and navigate and find a tiny speck of an island in the middle of the Pacific, or a pack of wolves that's able to bring down larger prey. Superorganisms have properties that cannot be understood just by studying the individuals. I think understanding social networks and how they form and operate can help us understand not just health and emotions but all kinds of other phenomena -- like crime, and warfare, and economic phenomena like bank runs and market crashes and the adoption of innovation and the spread of product adoption.
我們在世上的經歷 取決於我們所處網路的 實際連結架構 以及在網路中,各種事情 所激盪的漣漪 我想,這是因為 人類形成群落 組合成一種「超級個體」 超級個體是每個獨立個體的集合 表現出的行為或現象 無法藉由研究個體而得知。 而需要了解、研究 整個群體 例如,一窩蜜蜂 正在找新的巢穴地點 還有一群躲避掠食者、 或是利用群體智慧 尋找太平洋裡的 一座小島的鳥兒 或是一群合作 攻擊獵物的狼。 超級個體的屬性 無法藉由研究單一個體來了解 我認為,了解人際關係 了解它的形成與運作 可以幫助我們了解健康和情感 甚至其他現象- 例如犯罪、福利、 或是經濟現象,例如銀行擠兌、 市場崩盤、 對於創新的適應、 產品適應的傳播等。
Now, look at this. I think we form social networks because the benefits of a connected life outweigh the costs. If I was always violent towards you or gave you misinformation or made you sad or infected you with deadly germs, you would cut the ties to me, and the network would disintegrate. So the spread of good and valuable things is required to sustain and nourish social networks. Similarly, social networks are required for the spread of good and valuable things, like love and kindness and happiness and altruism and ideas. I think, in fact, that if we realized how valuable social networks are, we'd spend a lot more time nourishing them and sustaining them, because I think social networks are fundamentally related to goodness. And what I think the world needs now is more connections.
看這結果 我想,我們彼此建立關係 是因為這種連結的生活 利大於弊 如果我總是對你暴力相向 或給你錯誤資訊 或讓你難過、傳染致命病毒給你 你就會和我斷交 這關係就會因此瓦解 美好事物的傳播 需要永續、良好的人際關係 相同的,人際關係也需要 美好事物的傳播 像是愛與善良、 快樂與無私、 新點子 我認為,如果我們可以意識到 人際關係的價值 我們就會花更多時間來培養、維持 因為我認為人際關係 在本質上是與善良相連的 我想現在世界所需的 是更多連結
Thank you.
謝謝
(Applause)
(掌聲)