Nicholas Christakis: How social networks predict epidemics

譯者: Hsin Cheng Lin 審譯者: Adrienne Lin

For the last 10 years, I've been spending my time trying to figure out

過去10年來，我試著了解，

how and why human beings

人們為何形成社交網路，

assemble themselves into social networks.

以及這些網路是如何形成的。

And the kind of social network I'm talking about

我所要談的網路，

is not the recent online variety,

並非現在所謂的網路社群。

but rather, the kind of social networks

而是更原始的社交網路，

that human beings have been assembling for hundreds of thousands of years,

自從人類在非洲大草原出現以來，

ever since we emerged from the African savannah.

已經使用這種連結十幾萬年了。

So, I form friendships and co-worker

我和其他人分享友誼、同事、

and sibling and relative relationships with other people

手足和親戚等等人際關係，

who in turn have similar relationships with other people.

這些人也和其他人有相似的連結。

And this spreads on out endlessly into a distance.

這樣的連結向外擴散，

And you get a network that looks like this.

從而得到的網路看起來會像這樣。

Every dot is a person.

每點代表一個人，

Every line between them is a relationship between two people --

兩點間的線則代表兩個人之間的關係，

different kinds of relationships.

各種不同的關係。

And you can get this kind of vast fabric of humanity,

種種的關係交織成一幅巨大的網路，

in which we're all embedded.

而我們都位於其中。

And my colleague, James Fowler and I have been studying for quite sometime

我的同事James Fowler和我花了滿長時間研究，

what are the mathematical, social,

想找到一個基於數學、社會學、

biological and psychological rules

生物學或是心理學的規則，

that govern how these networks are assembled

能夠主導這些網路的形成。

and what are the similar rules

以及是否有類似的規則

that govern how they operate, how they affect our lives.

主導網路的運作，進而影響我們的生活。

But recently, we've been wondering

直到最近，我們開始思考，

whether it might be possible to take advantage of this insight,

是否有可能利用這些發現，

to actually find ways to improve the world,

來找出增進人類福祉的方法，

to do something better,

改善現況，

to actually fix things, not just understand things.

去導正，而非只是單純理解問題。

So one of the first things we thought we would tackle

我們最先著手研究的議題，

would be how we go about predicting epidemics.

是如何預測流行趨勢。

And the current state of the art in predicting an epidemic --

目前最先進的預測方法—

if you're the CDC or some other national body --

如果你在疾病管制中心(CDC)或類似的政府單位工作—

is to sit in the middle where you are

是待在中央枯等，

and collect data

並收集資料，

from physicians and laboratories in the field

第一線的醫生和實驗室把資料傳進來，

that report the prevalence or the incidence of certain conditions.

報告疾病的流行程度或發生機率。

So, so and so patients have been diagnosed with something,

這邊有某個病患被診斷出來，

or other patients have been diagnosed,

那邊又有別人得病。

and all these data are fed into a central repository, with some delay.

資訊經過一些延遲之後，傳進中央的資料庫裡。

And if everything goes smoothly,

如果一切順利，

one to two weeks from now

一到兩個禮拜之後，

you'll know where the epidemic was today.

我們才會得知當天流行病的狀況。

And actually, about a year or so ago,

事實上一年多以前，

there was this promulgation

有人發表了這樣的概念，

of the idea of Google Flu Trends, with respect to the flu,

使用Google流感趨勢(Flu Trends)來尋找流感。

where by looking at people's searching behavior today,

透過對搜尋行為的分析，

we could know where the flu --

我們能夠得知流感發生的區域，

what the status of the epidemic was today,

得知當天傳染病的狀態，

what's the prevalence of the epidemic today.

以及傳染病的影響程度。

But what I'd like to show you today

不過這次我要介紹的方法，

is a means by which we might get

讓我們不只能夠

not just rapid warning about an epidemic,

得到傳染病的快速預警，

but also actually

更能夠讓我們

early detection of an epidemic.

提早偵測到流行病的發生。

And, in fact, this idea can be used

事實上，這個概念不止能夠

not just to predict epidemics of germs,

用來預測病菌的流行，

but also to predict epidemics of all sorts of kinds.

也能夠應用來預測各種事物的趨勢。

For example, anything that spreads by a form of social contagion

例如，任何能透過社群的方式傳播的事物，

could be understood in this way,

都可以用這種方式理解。

from abstract ideas on the left

從左邊的抽象概念，

like patriotism, or altruism, or religion

像是愛國主義、利他精神，或是宗教，

to practices

到具體的事物，

like dieting behavior, or book purchasing,

像是飲食行為、購買書籍、

or drinking, or bicycle-helmet [and] other safety practices,

酗酒、使用腳踏車安全帽等安全措施，

or products that people might buy,

或是日常用品，

purchases of electronic goods,

電子產品，

anything in which there's kind of an interpersonal spread.

任何透過人與人之間傳遞的事物。

A kind of a diffusion of innovation

這種創新的擴散，

could be understood and predicted

可以透過接下來我將展示的機制，

by the mechanism I'm going to show you now.

來理解並且預測。

So, as all of you probably know,

你們或許知道，

the classic way of thinking about this

最經典的範例，

is the diffusion-of-innovation,

就是創新的擴散，

or the adoption curve.

或是所謂的「普及曲線」。

So here on the Y-axis, we have the percent of the people affected,

Y軸是受影響人數的百分比，

and on the X-axis, we have time.

X軸表示時間的推移。

And at the very beginning, not too many people are affected,

剛開始沒有太多人受到影響，

and you get this classic sigmoidal,

然後你會看到經典的反曲線，

or S-shaped, curve.

或是S型曲線。

And the reason for this shape is that at the very beginning,

形成這種曲線的原因是，

let's say one or two people

一開始只有一兩個人

are infected, or affected by the thing

被影響，或是被「感染」，

and then they affect, or infect, two people,

然後傳遞給另外兩個人，

who in turn affect four, eight, 16 and so forth,

接著4、8、16，以此類推，

and you get the epidemic growth phase of the curve.

這時進入迅速增長的階段。

And eventually, you saturate the population.

最終擴散到整個群體。

There are fewer and fewer people

於是越來越難找到

who are still available that you might infect,

尚未被影響的人，

and then you get the plateau of the curve,

這時候曲線進入高原期，

and you get this classic sigmoidal curve.

形成整條反曲線。

And this holds for germs, ideas,

這個模式在病菌、創意、

product adoption, behaviors,

新產品的普及、行為，

and the like.

以及類似情況都適用。

But things don't just diffuse in human populations at random.

要注意的是，事物並不是隨機在人群中蔓延，

They actually diffuse through networks.

而是隨著網路分布來擴散。

Because, as I said, we live our lives in networks,

因為我們活在網路的世界，

and these networks have a particular kind of a structure.

而這種網路有特定的結構。

Now if you look at a network like this --

觀察這個網路，

this is 105 people.

裡面有105人。

And the lines represent -- the dots are the people,

每個點代表一個人

and the lines represent friendship relationships.

每條線代表彼此間的友誼關係。

You might see that people occupy

人們在這個網路中

different locations within the network.

佔據不同的位置，

And there are different kinds of relationships between the people.

彼此間有不同類型的關係。

You could have friendship relationships, sibling relationships,

可能是朋友、手足、

spousal relationships, co-worker relationships,

配偶、同事、

neighbor relationships and the like.

鄰居等等。

And different sorts of things

不同的事物會

spread across different sorts of ties.

透過不同的關係來傳播。

For instance, sexually transmitted diseases

例如，性傳染病，

will spread across sexual ties.

會藉由性伴侶的聯繫來散佈。

Or, for instance, people's smoking behavior

或者像人們吸菸，

might be influenced by their friends.

可能是受到朋友的影響。

Or their altruistic or their charitable giving behavior

人們的善行或捐助，

might be influenced by their coworkers,

可能是出自同事間的影響，

or by their neighbors.

或是他們鄰居的行為。

But not all positions in the network are the same.

但是網路中的位置並非都一樣。

So if you look at this, you might immediately grasp

這張圖或許能讓你了解，

that different people have different numbers of connections.

不同人有不同數量的連結。

Some people have one connection, some have two,

有的人一個，有人兩個，

some have six, some have 10 connections.

有人六個，有的人擁有十個連結。

And this is called the "degree" of a node,

也就是一個節點的「度數」，

or the number of connections that a node has.

或是一個節點所擁有的連結數。

But in addition, there's something else.

除此之外，

So, if you look at nodes A and B,

如果觀察節點A與B，

they both have six connections.

兩者都擁有六個連結。

But if you can see this image [of the network] from a bird's eye view,

但是如果鳥瞰整個圖像，

you can appreciate that there's something very different

你就會發現兩者之間，

about nodes A and B.

A與B的不同之處

So, let me ask you this -- I can cultivate this intuition by asking a question --

問題來了 －請用直覺回答－

who would you rather be

你比較想當誰：

if a deadly germ was spreading through the network, A or B?

如果致命病菌正在網路中散佈，A或是B？

(Audience: B.) Nicholas Christakis: B, it's obvious.

（觀眾：B）很明顯的是B。

B is located on the edge of the network.

B處在網路的邊緣。

Now, who would you rather be

現在，你比較想當誰：

if a juicy piece of gossip were spreading through the network?

如果網路中流傳著一個天大的八卦？

A. And you have an immediate appreciation

A。而且你馬上能夠理解到，

that A is going to be more likely

A會有更高的機率

to get the thing that's spreading and to get it sooner

趕上流行，而且早先一步。

by virtue of their structural location within the network.

這要歸功於他們在網路中的位置。

A, in fact, is more central,

A比較靠近中央，

and this can be formalized mathematically.

這可以用數學形式來描述。

So, if we want to track something

因此，如果我們希望追蹤某些事物

that was spreading through a network,

在網路中散佈的狀態，

what we ideally would like to do is to set up sensors

理想狀況是佈置感測器，

on the central individuals within the network,

對準網路裡的中央個體，

including node A,

包括節點A。

monitor those people that are right there in the middle of the network,

監視這些位於中心位置的人們，

and somehow get an early detection

以早期的預警到

of whatever it is that is spreading through the network.

正在網路上傳播的事物。

So if you saw them contract a germ or a piece of information,

亦即，如果這些人染病或是獲悉某些資訊，

you would know that, soon enough,

你就可以推斷，要不了多久，

everybody was about to contract this germ

所有人都會被波及，不管是染病，

or this piece of information.

或是得到資訊。

And this would be much better

這樣的作法遠勝於

than monitoring six randomly chosen people,

隨機挑選六個人來監控，

without reference to the structure of the population.

因為該做法並未考慮到群體的結構。

And in fact, if you could do that,

若是真的能夠實行，

what you would see is something like this.

我們會得到類似這樣的情況：

On the left-hand panel, again, we have the S-shaped curve of adoption.

左邊的圖表，是S型的普及曲線。

In the dotted red line, we show

我們用紅色虛線標示出，

what the adoption would be in the random people,

一般人的普及情形，

and in the left-hand line, shifted to the left,

左邊的線段，則向左偏移，

we show what the adoption would be

顯示出網路中的核心個體，

in the central individuals within the network.

他們的普及情形。

On the Y-axis is the cumulative instances of contagion,

Y軸是受到傳染「病例」的累積數量，

and on the X-axis is the time.

X軸則是時間。

And on the right-hand side, we show the same data,

右邊的圖表是相同的資料，

but here with daily incidence.

呈現的是每日的「感染」數字。

And what we show here is -- like, here --

我們想要傳達的是，

very few people are affected, more and more and more and up to here,

一開始少數人受到影響，然後越來越多直到這裡，

and here's the peak of the epidemic.

這裡就是傳播的高峰期。

But shifted to the left is what's occurring in the central individuals.

向左偏的則是在核心個體發生的情形，

And this difference in time between the two

這兩條曲線間的時間差，

is the early detection, the early warning we can get,

就是預測時差，我們可以從中得到預警，

about an impending epidemic

人群中是否有

in the human population.

即將爆發的疫情。

The problem, however,

然而問題在於，

is that mapping human social networks

人際間的社交網路，

is not always possible.

並不容易繪測。

It can be expensive, not feasible,

這樣的計畫可能所費不貲、非常困難、

unethical,

具有道德爭議

or, frankly, just not possible to do such a thing.

說實話，就是不可能。

So, how can we figure out

所以，我們要如何找出，

who the central people are in a network

網路中的核心個體在哪，

without actually mapping the network?

而無需繪出整個網路？

What we came up with

我們所想到的，

was an idea to exploit an old fact,

是利用一個既有的事實

or a known fact, about social networks,

關於社交網路，眾所皆知的事實。

which goes like this:

也就是：

Do you know that your friends

你知道你的朋友，

have more friends than you do?

所擁有的友人數目比你還多嗎？

Your friends have more friends than you do,

朋友的友人數目比自己擁有的還多，

and this is known as the friendship paradox.

通常這種情況被稱做「友誼悖論」。

Imagine a very popular person in the social network --

試想社交網路中的人氣王 －

like a party host who has hundreds of friends --

例如派對的主人，身邊有上百個朋友 --

and a misanthrope who has just one friend,

和孤僻成性，只有一個朋友的人。

and you pick someone at random from the population;

若是你隨便從人群中挑出一位，

they were much more likely to know the party host.

他們就非常有可能認識這位派對主人，

And if they nominate the party host as their friend,

而當他們舉出派對主人是自己的朋友，

that party host has a hundred friends,

由於他有上百個朋友，

therefore, has more friends than they do.

因此遠比自己的朋友數目還多。

And this, in essence, is what's known as the friendship paradox.

在本質上，這就是友誼悖論：

The friends of randomly chosen people

隨機挑選的人，他的朋友，

have higher degree, and are more central

會有較高的連結數目，也較為趨近核心，

than the random people themselves.

因而優於那些隨機挑選的人。

And you can get an intuitive appreciation for this

因此，你可以憑直覺想像，

if you imagine just the people at the perimeter of the network.

如果是那些位於網路邊緣的人，

If you pick this person,

這樣的人，

the only friend they have to nominate is this person,

他的朋友只會有這個人，

who, by construction, must have at least two

而結構上來說，這個人至少會有兩位、

and typically more friends.

甚至更多的朋友。

And that happens at every peripheral node.

在每個外圍的節點都是這樣。

And in fact, it happens throughout the network as you move in,

當你越往網路的中心移動時就越常見，

everyone you pick, when they nominate a random --

每個被你挑到的人，當他們隨意提出一個...

when a random person nominates a friend of theirs,

每當提出一個他們的朋友，

you move closer to the center of the network.

你就越靠近網路的中心。

So, we thought we would exploit this idea

於是我們認為可以利用這個概念，

in order to study whether we could predict phenomena within networks.

來研究我們是否能預測網路中所發生的現象。

Because now, with this idea

因為有了這樣的發現，

we can take a random sample of people,

我們可以從人群中隨機挑選樣本，

have them nominate their friends,

請他們指出他們的朋友，

those friends would be more central,

這些朋友會比較靠近中心，

and we could do this without having to map the network.

而我們就無須標出整個網路的圖像。

And we tested this idea with an outbreak of H1N1 flu

在哈佛大學，我們利用H1N1流感的爆發

at Harvard College

來測試這個概念。

in the fall and winter of 2009, just a few months ago.

在2009年秋冬，只有幾個月前，

We took 1,300 randomly selected undergraduates,

我們隨機挑選了1300位大學生，

we had them nominate their friends,

請這些人提供他們的朋友名單，

and we followed both the random students and their friends

我們同時追蹤了這些人和他們的朋友，

daily in time

每天為間隔，

to see whether or not they had the flu epidemic.

確認他們是否染上流感。

And we did this passively by looking at whether or not they'd gone to university health services.

除了被動觀察他們是否去健康中心報到，

And also, we had them [actively] email us a couple of times a week.

同時也要求每個禮拜Email給我們。

Exactly what we predicted happened.

結果一如我們所預期。

So the random group is in the red line.

隨機挑選的群體用紅線標示，

The epidemic in the friends group has shifted to the left, over here.

他們的朋友則向左邊偏移，在這邊。

And the difference in the two is 16 days.

兩者間的差距是16天。

By monitoring the friends group,

觀察朋友的群體，

we could get 16 days advance warning

能夠讓我們提早16天得到警示，

of an impending epidemic in this human population.

警告人群中即將爆發的傳染病。

Now, in addition to that,

除此之外，

if you were an analyst who was trying to study an epidemic

如果你是研究傳染病的分析師，

or to predict the adoption of a product, for example,

或者想要預測產品的普及情形。

what you could do is you could pick a random sample of the population,

你可以從人群中挑選隨機樣本，

also have them nominate their friends and follow the friends

請他們指出自己的朋友，

and follow both the randoms and the friends.

並且同時追蹤這兩群樣本("隨機群"和"朋友群")。

Among the friends, the first evidence you saw of a blip above zero

在朋友群中，當曲線首次開始上升...

in adoption of the innovation, for example,

...例如創新概念的普及，

would be evidence of an impending epidemic.

這個轉折便能標示出即將發生的流行趨勢。

Or you could see the first time the two curves diverged,

另一種情況是當兩條曲線首次出現分歧時，

as shown on the left.

如左圖所示。

When did the randoms -- when did the friends take off

隨機群...他們的朋友群是何時起頭，

and leave the randoms,

離開隨機群的曲線，

and [when did] their curve start shifting?

使得這條曲線開始偏移？

And that, as indicated by the white line,

從白線上可以發現，

occurred 46 days

在整體趨勢達到高峰之前，

before the peak of the epidemic.

提早了46天。

So this would be a technique

這樣的技術，

whereby we could get more than a month-and-a-half warning

可以讓我們提早一個半月得到預警，

about a flu epidemic in a particular population.

得知特定群體中感冒的流行。

I should say that

應該這樣說，

how far advanced a notice one might get about something

我們能夠多早預知事件的發生，

depends on a host of factors.

取決於幾個主要的因素。

It could depend on the nature of the pathogen --

可能由於病原的性質 －

different pathogens,

不同的病原體，

using this technique, you'd get different warning --

利用這個技術，可以得到不同的警示 －

or other phenomena that are spreading,

或是可以說，在人際網路的結構裡

or frankly, on the structure of the human network.

某些正在傳播中的現象。

Now in our case, although it wasn't necessary,

雖然並非必要，不過在這個案例中，

we could also actually map the network of the students.

我們可以將學生的網路完整描繪出來，

So, this is a map of 714 students

所以，這幅圖包含了714個學生，

and their friendship ties.

以及他們的人際關係。

And in a minute now, I'm going to put this map into motion.

接下來我會用動畫呈現這幅圖，

We're going to take daily cuts through the network

逐日推進，

for 120 days.

一共120天。

The red dots are going to be cases of the flu,

紅點代表受到感染的案例，

and the yellow dots are going to be friends of the people with the flu.

黃點則代表受感染學生的朋友，

And the size of the dots is going to be proportional

而點的大小則以比例的方式，

to how many of their friends have the flu.

呈現它周遭朋友受到傳染的數量，

So bigger dots mean more of your friends have the flu.

也就是說，越大的點代表你有越多的朋友感冒。

And if you look at this image -- here we are now in September the 13th --

觀察這張圖 －現在是9月13號－

you're going to see a few cases light up.

你會看到幾個病例亮起來。

You're going to see kind of blooming of the flu in the middle.

中心區域裡，傳染就像開花一樣向外散布。

Here we are on October the 19th.

接下來到了10月19號，

The slope of the epidemic curve is approaching now, in November.

傳染曲線開始上升，到了11月，

Bang, bang, bang, bang, bang -- you're going to see lots of blooming in the middle,

砰，砰，砰，越來越多病例在中央區域發生。

and then you're going to see a sort of leveling off,

接著情勢開始趨緩，

fewer and fewer cases towards the end of December.

越來越少人受到感染，直到十二月底。

And this type of a visualization

這種類型的圖像化資訊，

can show that epidemics like this take root

可以呈現出流行事件開始扎根，

and affect central individuals first,

先影響中心的個體，

before they affect others.

再向外擴散的全貌。

Now, as I've been suggesting,

如我之前所說的，

this method is not restricted to germs,

這套方法並不局限於病菌，

but actually to anything that spreads in populations.

可以是透過人群傳播的任何事物。

Information spreads in populations,

資訊透過人群傳遞，

norms can spread in populations,

規則能透過人群來散佈，

behaviors can spread in populations.

行為也能夠透過人群傳播

And by behaviors, I can mean things like criminal behavior,

談到行為，像是犯罪，

or voting behavior, or health care behavior,

投票，衛生習慣－

like smoking, or vaccination,

像是吸菸或是疫苗接種，

or product adoption, or other kinds of behaviors

新產品的採用，或是其他種類的行為，

that relate to interpersonal influence.

與人們之間的相互影響有關。

If I'm likely to do something that affects others around me,

如果我打算做某些事來影響周圍的人，

this technique can get early warning or early detection

這套技巧就可以提前預警，或是偵測，

about the adoption within the population.

事物在人群中的普及程度。

The key thing is that for it to work,

讓它管用的關鍵在於，

there has to be interpersonal influence.

人們之間要能互相影響，

It cannot be because of some broadcast mechanism

而非因為某種廣播機制，

affecting everyone uniformly.

使得每個人都受到相同的影響。

Now the same insights

同樣的發現，

can also be exploited -- with respect to networks --

透過網路的傳播，也能夠有

can also be exploited in other ways,

各式各樣的應用，

for example, in the use of targeting

例如，用來標示出，

specific people for interventions.

特定的目標以進行干預。

So, for example, most of you are probably familiar

舉例來說，大部分的人可能對

with the notion of herd immunity.

"群體免疫力"感到熟悉。

So, if we have a population of a thousand people,

如果這裡有一千人的群體，

and we want to make the population immune to a pathogen,

我們希望讓群體對某個病原體免疫，

we don't have to immunize every single person.

我們不需要對每個人施打疫苗。

If we immunize 960 of them,

若是讓其中960人免疫，

it's as if we had immunized a hundred [percent] of them.

效果就相當於整個群體都免疫，

Because even if one or two of the non-immune people gets infected,

因為即使一兩個沒有免疫能力的人受到感染，

there's no one for them to infect.

他也沒有人能夠傳染，

They are surrounded by immunized people.

感染者被免疫的人所圍繞。

So 96 percent is as good as 100 percent.

所以百分之96的效果相當於百分之百。

Well, some other scientists have estimated

其他的科學家估計，

what would happen if you took a 30 percent random sample

如果只靠30%的隨機樣本，

of these 1000 people, 300 people and immunized them.

30%在1000人中，也就是讓300個人免疫，

Would you get any population-level immunity?

是否能夠達到群體層次的免疫？

And the answer is no.

答案是"不能"。

But if you took this 30 percent, these 300 people

但是，如果對這30%，要300個人

and had them nominate their friends

舉出他們的朋友，

and took the same number of vaccine doses

然後用同樣數量的疫苗藥劑，

and vaccinated the friends of the 300 --

為這群300人的朋友接種，

the 300 friends --

300位朋友，

you can get the same level of herd immunity

就能夠得到相同於，讓96%的人免疫

as if you had vaccinated 96 percent of the population

所達到的群體免疫程度。

at a much greater efficiency, with a strict budget constraint.

更有效率，也節省預算。

And similar ideas can be used, for instance,

類似的概念也能用於

to target distribution of things like bed nets

物資的分配標的，例如在發展中國家

in the developing world.

蚊帳的分發方式。

If we could understand the structure of networks in villages,

若是能夠了解村落中的網路架構，

we could target to whom to give the interventions

我們就能影響關鍵的節點，

to foster these kinds of spreads.

以增進這種形式的散佈。

Or, frankly, for advertising with all kinds of products.

或是老實說，用來宣傳各式各樣的產品。

If we could understand how to target,

如果能夠了解

it could affect the efficiency

如何鎖定焦點，

of what we're trying to achieve.

就可以提高成功的效率。

And in fact, we can use data

事實上現在有數不清的來源。

from all kinds of sources nowadays [to do this].

能夠提供我們所需的資料。

This is a map of eight million phone users

這是一份歐洲國家中，

in a European country.

八百萬電話用戶的分布圖。

Every dot is a person, and every line represents

每個點代表一個用戶，每條線

a volume of calls between the people.

代表人們之間的通話量。

And we can use such data, that's being passively obtained,

我們可以利用這份被動獲得的資料，

to map these whole countries

描繪出整個國家的全貌，

and understand who is located where within the network.

並且定位每個人在網路中的位置，

Without actually having to query them at all,

而無須一個個去問，

we can get this kind of a structural insight.

從而得到對整體架構的瞭解。

And other sources of information, as you're no doubt aware

你一定也知道，其他來源的資訊

are available about such features, from email interactions,

也能提供類似的特徵，從email互動，

online interactions,

線上互動，

online social networks and so forth.

線上社群網站等等。

And in fact, we are in the era of what I would call

而事實上我們正在這樣的一個世界，

"massive-passive" data collection efforts.

「巨量-被動」的資料被收集起來。

They're all kinds of ways we can use massively collected data

我們有一大堆方法可以使用這些廣泛收集的資料，

to create sensor networks

用來建立偵測網路，

to follow the population,

用來追蹤人群，

understand what's happening in the population,

找出群體中正在發生的事件，

and intervene in the population for the better.

並且適時介入以改善情況。

Because these new technologies tell us

因為這些新的科技讓我們理解，

not just who is talking to whom,

不只是誰正和誰溝通，

but where everyone is,

還有每個人的位置所在。

and what they're thinking based on what they're uploading on the Internet,

人們在想什麼，是看他們上傳了什麼到網路上，

and what they're buying based on their purchases.

現在的購買決策受到過去購物的影響。

And all this administrative data can be pulled together

所有這樣的資料可以組織起來，

and processed to understand human behavior

經過處理以了解人類的行為，

in a way we never could before.

以一種前所未見的方式。

So, for example, we could use truckers' purchases of fuel.

舉例來說，我們可以觀察卡車司機加油，

So the truckers are just going about their business,

司機們正準備開工，

and they're buying fuel.

他們買入了汽油，

And we see a blip up in the truckers' purchases of fuel,

我們觀察到卡車司機加油的曲線開始上升，

and we know that a recession is about to end.

而能夠推估景氣即將好轉了。

Or we can monitor the velocity

或是可以透過手機，

with which people are moving with their phones on a highway,

監視高速公路上人們的移動速度，

and the phone company can see,

電信公司便能夠得知，

as the velocity is slowing down,

當移動速度下降的時候，

that there's a traffic jam.

代表可能有交通堵塞。

And they can feed that information back to their subscribers,

這些資訊便回傳給電信公司的用戶，

but only to their subscribers on the same highway

並且針對那些在同一條高速公路上，

located behind the traffic jam!

位於車陣後方的用戶!

Or we can monitor doctors prescribing behaviors, passively,

我們也可以被動監測醫生開藥的行為，

and see how the diffusion of innovation with pharmaceuticals

以了解對藥品的接受度，

occurs within [networks of] doctors.

是如何在醫生之間擴散的。

Or again, we can monitor purchasing behavior in people

我們也可以監測人們的購買行為，

and watch how these types of phenomena

觀察購買現象是如何

can diffuse within human populations.

在人群中散播的。

And there are three ways, I think,

我認為這些巨量-被動收集所得的資料，

that these massive-passive data can be used.

有三種方式可以利用。

One is fully passive,

一種是完全的被動，

like I just described --

像我剛剛所描述的 －

as in, for instance, the trucker example,

例如卡車司機的例子，

where we don't actually intervene in the population in any way.

我們並不對群體做任何形式的干預。

One is quasi-active,

一種是半主動，

like the flu example I gave,

像是之前流感的例子，

where we get some people to nominate their friends

我們讓某些人舉出他們的朋友，

and then passively monitor their friends --

然後被動的觀察他們的朋友 －

do they have the flu, or not? -- and then get warning.

他們感冒了沒？－ 並據此取得預警。

Or another example would be,

另外一個例子是，

if you're a phone company, you figure out who's central in the network

電信公司可以想辦法找出網路的中心群，

and you ask those people, "Look, will you just text us your fever every day?

問他們，"你能不能每天用簡訊，讓我們知道你發燒了沒？

Just text us your temperature."

只要傳送體溫即可"

And collect vast amounts of information about people's temperature,

然後從中心群體裡，

but from centrally located individuals.

大量收集體溫資料，

And be able, on a large scale,

便能夠用少量的資料輸入，

to monitor an impending epidemic

來進行大規模的監控，

with very minimal input from people.

以預測流感的爆發。

Or, finally, it can be more fully active --

最後是完全主動的方式 －

as I know subsequent speakers will also talk about today --

就我所知下位演講者也會談到－

where people might globally participate in wikis,

現在全世界的人都參與維基百科的編寫、

or photographing, or monitoring elections,

拍攝照片、或是監視選舉，

and upload information in a way that allows us to pool

人們將資訊上傳，使得我們能夠匯集

information in order to understand social processes

資訊以了解社會進程，

and social phenomena.

以及社會現象的產生。

In fact, the availability of these data, I think,

我認為這些資料的垂手可得，

heralds a kind of new era

揭示了一個新時代的來臨，

of what I and others would like to call

我們將之稱作

"computational social science."

"計算社會科學"。

It's sort of like when Galileo invented -- or, didn't invent --

有點類似伽利略發明 －或許沒有發明－

came to use a telescope

望遠鏡的誕生，

and could see the heavens in a new way,

而可以從全新的角度來觀看天空。

or Leeuwenhoek became aware of the microscope --

或是雷文霍克發現顯微鏡 －

or actually invented --

或許是他發明的－

and could see biology in a new way.

而能夠用新的方式看待生物學。

But now we have access to these kinds of data

現在我們能夠取得的資料，

that allow us to understand social processes

能夠讓我們用過去未見的嶄新角度

and social phenomena

了解社會的進程，

in an entirely new way that was never before possible.

以及其中發生的現象。

And with this science, we can

有了這樣的科學，

understand how exactly

我們就能夠了解

the whole comes to be greater

群體的綜效，是如何優於

than the sum of its parts.

單純個體的加總。

And actually, we can use these insights

我們也能運用這些理解，

to improve society and improve human well-being.

來增進社會以及人類的福祉。

Thank you.

謝謝。