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TEDGlobal 2011

Geoffrey West: The surprising math of cities and corporations

Geoffrey West:城市与企业中的奇妙数学

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物理学家Geoffrey West发现,简单的数学定律治理着城市的各种属性--财富,犯罪率,步行速度以及城市的其它方方面面都可由一个数字推算出来:即城市的人口。他通过展示其中的原理,阐述生物与企业拥有的相似定律,让这场TED全球演讲颠覆了人们思想。

- Theorist
Physicist Geoffrey West believes that complex systems from organisms to cities are in many ways governed by simple laws -- laws that can be discovered and analyzed. Full bio

Cities are the crucible of civilization.
城市是文明的熔炉
00:16
They have been expanding,
它们一直在扩张
00:19
urbanization has been expanding,
城市化的扩张速度
00:21
at an exponential rate in the last 200 years
在过去的200年里变得越来越快
00:23
so that by the second part of this century,
到了本世纪下半叶
00:25
the planet will be completely dominated
整个地球都将被城市
00:28
by cities.
所主宰
00:30
Cities are the origins of global warming,
城市是全球变暖的源头
00:33
impact on the environment,
影响着环境
00:36
health, pollution, disease,
卫生 污染 疾病
00:38
finance,
金融
00:41
economies, energy --
经济 能源--
00:43
they're all problems
这些问题
00:46
that are confronted by having cities.
都是由城市引起的
00:48
That's where all these problems come from.
这是所有这些问题的源头
00:50
And the tsunami of problems that we feel we're facing
我们感觉可持续性方面的问题
00:52
in terms of sustainability questions
正如海啸般扑面而来
00:55
are actually a reflection
而这些问题实际上
00:57
of the exponential increase
是与日俱增的
00:59
in urbanization across the planet.
全球城市化进程所产生的效应
01:01
Here's some numbers.
我们来看几个数字
01:04
Two hundred years ago, the United States
200年前 美国
01:06
was less than a few percent urbanized.
城市化程度不到百分之几而已
01:08
It's now more than 82 percent.
而现在则超过了82%
01:10
The planet has crossed the halfway mark a few years ago.
全球的城市化程度在几年前就超过了百分之五十
01:12
China's building 300 new cities
中国在将来的20年内
01:15
in the next 20 years.
建设300座新城市
01:17
Now listen to this:
请注意
01:19
Every week for the foreseeable future,
在将来的每一周
01:21
until 2050,
一直到2050年
01:24
every week more than a million people
每一周 将有100万人
01:26
are being added to our cities.
进入我们的城市
01:28
This is going to affect everything.
这将对一切产生影响
01:30
Everybody in this room, if you stay alive,
在座的各位 如果你一直活着
01:32
is going to be affected
你就必定要受到
01:34
by what's happening in cities
城市化所带来的
01:36
in this extraordinary phenomenon.
翻天覆地的影响
01:38
However, cities,
然而 城市
01:40
despite having this negative aspect to them,
尽管存在负面效应
01:43
are also the solution.
但城市也是问题解决的出路
01:46
Because cities are the vacuum cleaners and the magnets
这是因为城市是除尘器和吸铁石
01:48
that have sucked up creative people,
吸纳了所有创意人才
01:52
creating ideas, innovation,
创造着思想 革新
01:54
wealth and so on.
财富等等
01:56
So we have this kind of dual nature.
我们具有这样的双面性
01:58
And so there's an urgent need
我们迫切需要运用
02:00
for a scientific theory of cities.
城市的科学原理
02:03
Now these are my comrades in arms.
这些是我全副武装的同志们
02:07
This work has been done with an extraordinary group of people,
这群杰出的人士做了这些工作
02:10
and they've done all the work,
都是他们的功劳
02:12
and I'm the great bullshitter
我只会胡吹海侃
02:14
that tries to bring it all together.
做个总体介绍
02:16
(Laughter)
(众人笑)
02:18
So here's the problem: This is what we all want.
这里有个问题 这是我们希望的结果
02:20
The 10 billion people on the planet in 2050
到了2050年,地球上的10亿人
02:22
want to live in places like this,
都想生活在这样的地方
02:25
having things like this,
拥有这些东西
02:27
doing things like this,
进行这样的活动
02:29
with economies that are growing like this,
在这样的经济增长情况下
02:31
not realizing that entropy
而没有意识到
02:34
produces things like this,
人口过剩会造成这样
02:36
this, this
这样 这样
02:38
and this.
和这样的情况
02:42
And the question is:
问题是
02:44
Is that what Edinburgh and London and New York
爱丁堡 伦敦和纽约
02:46
are going to look like in 2050,
到了2050年会变成这样
02:48
or is it going to be this?
还是这样
02:50
That's the question.
这是个问题
02:52
I must say, many of the indicators
我不得不说 许多这样的参数
02:54
look like this is what it's going to look like,
似乎更可能是它们将来的样子
02:56
but let's talk about it.
我们来探讨一下
02:59
So my provocative statement
我敢大胆地说
03:02
is that we desperately need a serious scientific theory of cities.
我们急需一个严谨的城市科学理论
03:05
And scientific theory means quantifiable --
科学理论意味着它是可量化的
03:08
relying on underlying generic principles
依据基本的普遍原理
03:11
that can be made into a predictive framework.
我们能够推导出一个可预见的结构
03:14
That's the quest.
这是我们的目标
03:16
Is that conceivable?
这可能吗
03:18
Are there universal laws?
有这样的普遍定律吗
03:20
So here's two questions
每当我思考这个问题
03:22
that I have in my head when I think about this problem.
两个疑问一直在我脑子里打转
03:24
The first is:
第一
03:26
Are cities part of biology?
城市是生物界的一部分吗
03:28
Is London a great big whale?
伦敦是一只大鲸鱼吗
03:30
Is Edinburgh a horse?
爱丁堡是一匹马吗
03:32
Is Microsoft a great big anthill?
微软是一座巨型蚁山吗
03:34
What do we learn from that?
我们从中能得到什么启发
03:36
We use them metaphorically --
我们可以使用比喻
03:38
the DNA of a company, the metabolism of a city, and so on --
一个公司的DNA 一个城市的新陈代谢 等等
03:40
is that just bullshit, metaphorical bullshit,
这些都是胡扯 乱七八糟的比喻
03:42
or is there serious substance to it?
还是有严谨的依据
03:45
And if that is the case,
如果确有依据
03:48
how come that it's very hard to kill a city?
为什么城市总是生生不息呢
03:50
You could drop an atom bomb on a city,
你可以扔一个原子弹炸毁一个城市
03:52
and 30 years later it's surviving.
而30年之后 它依然存在
03:54
Very few cities fail.
消亡的城市寥寥无几
03:56
All companies die, all companies.
而所有公司都会关门 无一例外
03:59
And if you have a serious theory, you should be able to predict
如果你掌握了缜密的原理 你就应该可以预测
04:02
when Google is going to go bust.
谷歌什么时候关门大吉
04:04
So is that just another version
这是不是
04:07
of this?
这个画面的翻版
04:10
Well we understand this very well.
我们对此非常清楚
04:12
That is, you ask any generic question about this --
如果你随便问一个常识问题
04:14
how many trees of a given size,
某已知体积的大树有多少棵
04:16
how many branches of a given size does a tree have,
一颗体积已知的大树有多少分枝
04:18
how many leaves,
多少树叶
04:20
what is the energy flowing through each branch,
每根树枝中流动的能量是什么
04:22
what is the size of the canopy,
树冠有多大
04:24
what is its growth, what is its mortality?
它长势如何 寿命多长
04:26
We have a mathematical framework
我们有一套数学体系
04:28
based on generic universal principles
建立在普遍原理的基础上
04:30
that can answer those questions.
它能够解答那些问题
04:33
And the idea is can we do the same for this?
问题是 它是否适用于城市
04:35
So the route in is recognizing
首先我们要认识到
04:40
one of the most extraordinary things about life,
生命最奇妙之处 其中之一
04:43
is that it is scalable,
就是它是会长大的
04:45
it works over an extraordinary range.
它能够长到非常之大
04:47
This is just a tiny range actually:
这只是很小的一个尺度
04:49
It's us mammals;
这是我们 哺乳动物
04:51
we're one of these.
我们是其中之一
04:53
The same principles, the same dynamics,
相同的原理 相同的活动
04:55
the same organization is at work
相同的组织 在所有这些动物中
04:57
in all of these, including us,
发挥着作用 我们也包括在内
04:59
and it can scale over a range of 100 million in size.
它能够长大到一亿个单位
05:01
And that is one of the main reasons
生命如此周而复始 欣欣向荣
05:04
life is so resilient and robust --
这就是原因之一
05:07
scalability.
伸展性
05:09
We're going to discuss that in a moment more.
我们一会再讨论这个
05:11
But you know, at a local level,
从我们自身出发
05:14
you scale; everybody in this room is scaled.
你会长大 在座所有人的身体都长大了
05:16
That's called growth.
这就是成长
05:18
Here's how you grew.
你就是这么成长的
05:20
Rat, that's a rat -- could have been you.
这是一只老鼠 也可以是你
05:22
We're all pretty much the same.
我们之间非常相似
05:24
And you see, you're very familiar with this.
你们可以看到 你的情况与之十分相似
05:27
You grow very quickly and then you stop.
你长得很快 接着停止生长
05:29
And that line there
上面的那条线
05:31
is a prediction from the same theory,
是同一理论推导出来的
05:33
based on the same principles,
所依据的原理
05:35
that describes that forest.
与描述森林的原理相同
05:37
And here it is for the growth of a rat,
这显示的是老鼠的生长情况
05:39
and those points on there are data points.
上面的点是数据点
05:41
This is just the weight versus the age.
即体重与年龄的比例
05:43
And you see, it stops growing.
你看 它停止生长了
05:45
Very, very good for biology --
这对生物界非常有益
05:47
also one of the reasons for its great resilience.
这也证明了其强大的伸展性
05:49
Very, very bad
但对我们目前规划中的
05:51
for economies and companies and cities
经济 公司和城市而而言
05:53
in our present paradigm.
这是非常糟糕的
05:55
This is what we believe.
我们就是这么认为的
05:57
This is what our whole economy
这就是我们的经济
05:59
is thrusting upon us,
强加给我们的
06:01
particularly illustrated in that left-hand corner:
左上角的图表凸显了这一点
06:03
hockey sticks.
冰球棍
06:06
This is a bunch of software companies --
它显示的是众多软件公司
06:08
and what it is is their revenue versus their age --
收入与公司建立时间的比例
06:10
all zooming away,
它们都平步青云
06:12
and everybody making millions and billions of dollars.
每家公司都大把大把地捞钱
06:14
Okay, so how do we understand this?
那么 我们如何解读
06:16
So let's first talk about biology.
我们先来讨论一下生物学
06:19
This is explicitly showing you
这让你清清楚楚地看到
06:22
how things scale,
事物的规模是如何增大的
06:24
and this is a truly remarkable graph.
这幅图表意义非凡
06:26
What is plotted here is metabolic rate --
上面显示的是新陈代谢率
06:28
how much energy you need per day to stay alive --
为维持生命你每天需要摄入的能量
06:31
versus your weight, your mass,
比上你的体重
06:34
for all of us bunch of organisms.
这适用于人类以及许多其它生物
06:36
And it's plotted in this funny way by going up by factors of 10,
它的结构很有意思 以10倍递进
06:39
otherwise you couldn't get everything on the graph.
否则你无法看到全局
06:42
And what you see if you plot it
在这样一个有意思的图标中
06:44
in this slightly curious way
你可以看到
06:46
is that everybody lies on the same line.
每个人都落在了同一条线上
06:48
Despite the fact that this is the most complex and diverse system
尽管这是宇宙中
06:51
in the universe,
最为纷繁复杂的系统
06:54
there's an extraordinary simplicity
但它显示了一个
06:57
being expressed by this.
极为简单现象
06:59
It's particularly astonishing
这令人震惊
07:01
because each one of these organisms,
这上面的每个物种
07:04
each subsystem, each cell type, each gene,
每个子系统 每个细胞种类 每个基因
07:06
has evolved in its own unique environmental niche
都在其独特的生态位和历史中
07:08
with its own unique history.
得到进化发展
07:12
And yet, despite all of that Darwinian evolution
然而 即使经过了达尔文派支持的进化论
07:15
and natural selection,
和自然选择
07:18
they've been constrained to lie on a line.
它们最终还是集中到了一条线上
07:20
Something else is going on.
还有其它力量在发挥作用
07:22
Before I talk about that,
谈到这之前
07:24
I've written down at the bottom there
我在底下标出了
07:26
the slope of this curve, this straight line.
这条曲线的斜率 即这条直线
07:28
It's three-quarters, roughly,
大约为3比4
07:30
which is less than one -- and we call that sublinear.
小于1 呈“次线性”
07:32
And here's the point of that.
这里有一点值得注意
07:35
It says that, if it were linear,
当最大斜率
07:37
the steepest slope,
呈线性
07:40
then doubling the size
那么当体型翻倍
07:42
you would require double the amount of energy.
所需能量也随之翻倍
07:44
But it's sublinear, and what that translates into
而若呈次线性 情况则是
07:46
is that, if you double the size of the organism,
当生物的体型翻倍
07:49
you actually only need 75 percent more energy.
它实际只需增加75%的能量
07:51
So a wonderful thing about all of biology
生物的奇妙之处就在于
07:54
is that it expresses an extraordinary economy of scale.
它巧妙地展现了经济的伸展能力
07:56
The bigger you are systematically,
根据准确定义的规律
07:59
according to very well-defined rules,
一个系统越大
08:01
less energy per capita.
其所需的平均能力越少
08:03
Now any physiological variable you can think of,
你能够想到的任何变量
08:06
any life history event you can think of,
任何历史事件
08:09
if you plot it this way, looks like this.
只要你照着这样制表 都会得到相似的图形
08:11
There is an extraordinary regularity.
其一致性非常惊人
08:14
So you tell me the size of a mammal,
只要你说出一种哺乳动物的体型
08:16
I can tell you at the 90 percent level everything about it
我就能告诉你关于其生理和生命周期等情况
08:18
in terms of its physiology, life history, etc.
正确率90%
08:21
And the reason for this is because of networks.
原因就在于网络
08:25
All of life is controlled by networks --
所有生命都由网络所控制
08:28
from the intracellular through the multicellular
不论是单细胞还是多细胞生物
08:31
through the ecosystem level.
整个生态系统都是如此
08:33
And you're very familiar with these networks.
你对这些网络并不陌生
08:35
That's a little thing that lives inside an elephant.
这是生长在大象体内的一种小生物
08:39
And here's the summary of what I'm saying.
这是我讲话内容的总结
08:42
If you take those networks,
你有了这些网络
08:45
this idea of networks,
网络的概念
08:47
and you apply universal principles,
再用上普遍原理
08:49
mathematizable, universal principles,
数学化的普遍原理
08:51
all of these scalings
所有规模增长
08:53
and all of these constraints follow,
所有限制因素
08:55
including the description of the forest,
包括森林的情况
08:58
the description of your circulatory system,
你循环系统的情况
09:00
the description within cells.
细胞内部情况等
09:02
One of the things I did not stress in that introduction
我在介绍中没有提及的一点是
09:04
was that, systematically, the pace of life
生长的节奏会随着你体型的增大
09:07
decreases as you get bigger.
而系统性地减缓
09:10
Heart rates are slower; you live longer;
心率会减缓 你活得更久
09:12
diffusion of oxygen and resources
通过细胞膜的氧气
09:15
across membranes is slower, etc.
和物质的流动减缓
09:17
The question is: Is any of this true
问题是 这是否
09:19
for cities and companies?
也适用于城市和企业
09:21
So is London a scaled up Birmingham,
伦敦是否是长大了的伯明翰
09:24
which is a scaled up Brighton, etc., etc.?
而伯明翰是否是长大了的布莱顿 等等
09:27
Is New York a scaled up San Francisco,
纽约是否是长大了的旧金山
09:30
which is a scaled up Santa Fe?
而旧金山是否是长大了的圣达菲
09:32
Don't know. We will discuss that.
不知道 我们稍候再讨论
09:34
But they are networks,
但它们都是网络
09:36
and the most important network of cities
而城市最重要的网络
09:38
is you.
就是你
09:40
Cities are just a physical manifestation
城市只是
09:42
of your interactions,
你我社会活动
09:45
our interactions,
以及个体相互聚拢集合的
09:47
and the clustering and grouping of individuals.
物质表现
09:49
Here's just a symbolic picture of that.
这只是一张简易图表
09:51
And here's scaling of cities.
这是城市规模的扩大
09:54
This shows that in this very simple example,
这幅图显示出了一个非常简单的例子
09:56
which happens to be a mundane example
这例子很寻常
09:59
of number of petrol stations
加油站的数量
10:01
as a function of size --
作为规模
10:03
plotted in the same way as the biology --
按照同于生物的方法制表
10:05
you see exactly the same kind of thing.
你能够观察到一模一样的结果
10:07
There is a scaling.
上面显示了增长的趋势
10:09
That is that the number of petrol stations in the city
你告诉我城市的规模
10:11
is now given to you
我就能够说出
10:15
when you tell me its size.
这座城市有多少个加油站
10:17
The slope of that is less than linear.
斜率呈次线性
10:19
There is an economy of scale.
这是规模经济
10:22
Less petrol stations per capita the bigger you are -- not surprising.
城市越大 人均加油站数量就越小 并不稀奇
10:24
But here's what's surprising.
稀奇的在这里
10:27
It scales in the same way everywhere.
增长的规律在哪里都适用
10:29
This is just European countries,
这反映的只是欧洲国家的情况
10:31
but you do it in Japan or China or Colombia,
但如果你用同样的方法观察日本 中国或哥伦比亚
10:33
always the same
结果都是一样的
10:36
with the same kind of economy of scale
同样的规模经济
10:38
to the same degree.
同样的水平
10:40
And any infrastructure you look at --
而且 你看到的所有基础设施
10:42
whether it's the length of roads, length of electrical lines --
不论是道路还是电线的长度
10:45
anything you look at
不论是什么
10:48
has the same economy of scale scaling in the same way.
都存在增长模式相同的规模经济
10:50
It's an integrated system
这个综合体系
10:53
that has evolved despite all the planning and so on.
不停演进 无论如何规划都是如此
10:55
But even more surprising
而当你看到
10:58
is if you look at socio-economic quantities,
社会经济数量
11:00
quantities that have no analog in biology,
即八千到一万年前
11:02
that have evolved when we started forming communities
我们开始建立社区时的社会经济数量
11:05
eight to 10,000 years ago.
你们会感到更加意外
11:08
The top one is wages as a function of size
上图以工资作为规模参数
11:10
plotted in the same way.
同理制表
11:12
And the bottom one is you lot --
而下面的是“你”
11:14
super-creatives plotted in the same way.
也就是超级智能人 同理制表
11:16
And what you see
上面显示出
11:19
is a scaling phenomenon.
一个规模增长的现象
11:21
But most important in this,
但图上最重要的是
11:23
the exponent, the analog to that three-quarters
新陈代谢率的幂
11:25
for the metabolic rate,
近似于三分之四
11:27
is bigger than one -- it's about 1.15 to 1.2.
大于1 大约在1.15和1.2之间
11:29
Here it is,
意思是
11:31
which says that the bigger you are
规模越大
11:33
the more you have per capita, unlike biology --
人均数就越多 与生物学的情况相反
11:36
higher wages, more super-creative people per capita as you get bigger,
工资越高 就有越多的超级智能人出现
11:39
more patents per capita, more crime per capita.
人均专利和犯罪率越高
11:43
And we've looked at everything:
我们研究了所有事物
11:46
more AIDS cases, flu, etc.
艾滋病病例 流感等等
11:48
And here, they're all plotted together.
把这些都放在一起制成表
11:51
Just to show you what we plotted,
让你们看到
11:53
here is income, GDP --
我们把收入 GDP
11:55
GDP of the city --
城市的GDP
11:58
crime and patents all on one graph.
犯罪和专利都放在一张图上
12:00
And you can see, they all follow the same line.
你们可以看到
12:02
And here's the statement.
下面是图的表述
12:04
If you double the size of a city from 100,000 to 200,000,
如果一个城市的规模从10万增长至20万
12:06
from a million to two million, 10 to 20 million,
从一百万到两百万 从一千万到两千万
12:09
it doesn't matter,
都一样
12:11
then systematically
在这个城市中
12:13
you get a 15 percent increase
工资 财富 艾滋病病例
12:15
in wages, wealth, number of AIDS cases,
警察人数
12:17
number of police,
任何你能想到的事物
12:19
anything you can think of.
都会系统地增加15%
12:21
It goes up by 15 percent,
对于所有事物都是如此
12:23
and you have a 15 percent savings
你还能节省
12:25
on the infrastructure.
15%的基础设施经费
12:28
This, no doubt, is the reason
这无疑就是
12:31
why a million people a week are gathering in cities.
城市每周新增一百万人口的原因
12:34
Because they think that all those wonderful things --
他们觉得那些美好的事物
12:37
like creative people, wealth, income --
包括创新人才 财富 收入
12:40
is what attracts them,
对他们有吸引力
12:42
forgetting about the ugly and the bad.
而忘记了城市丑恶的一面
12:44
What is the reason for this?
原因何在
12:46
Well I don't have time to tell you about all the mathematics,
我没有时间跟大家解释其中的数学
12:48
but underlying this is the social networks,
社会网络是其基础
12:51
because this is a universal phenomenon.
因为这是个普遍现象
12:54
This 15 percent rule
这个15%的规律
12:57
is true
是真的
13:00
no matter where you are on the planet --
无论你在地球上哪个角落
13:02
Japan, Chile,
日本 智利
13:04
Portugal, Scotland, doesn't matter.
葡萄牙 苏格兰 都一样
13:06
Always, all the data shows it's the same,
尽管城市的发展是各自独立的
13:09
despite the fact that these cities have evolved independently.
然而所有数据显示的结果都是一样的
13:12
Something universal is going on.
这里蕴藏着一个普遍的规律
13:15
The universality, to repeat, is us --
普遍性在于我们
13:17
that we are the city.
我们就是城市
13:20
And it is our interactions and the clustering of those interactions.
城市是我们相互活动以及这些活动的汇集
13:22
So there it is, I've said it again.
我刚才说过了
13:25
So if it is those networks and their mathematical structure,
那些网络和它们的数学结构
13:27
unlike biology, which had sublinear scaling,
与呈次线性的生物界不同
13:30
economies of scale,
生物是规模经济
13:33
you had the slowing of the pace of life
会随着规模的增大
13:35
as you get bigger.
而减缓生长的速度
13:37
If it's social networks with super-linear scaling --
如果城市的社会网络呈现超线性
13:39
more per capita --
人均数值越高
13:41
then the theory says
那么依照原理
13:43
that you increase the pace of life.
生长速度便会增加
13:45
The bigger you are, life gets faster.
你长得越大 生长速度就越快
13:47
On the left is the heart rate showing biology.
左边是心率
13:49
On the right is the speed of walking
右边是行走的速度
13:51
in a bunch of European cities,
在许多欧洲城市
13:53
showing that increase.
显示这样的增长情况
13:55
Lastly, I want to talk about growth.
最后 我想谈谈增长
13:57
This is what we had in biology, just to repeat.
在重复一下 这是生物学的情况
14:00
Economies of scale
规模经济
14:03
gave rise to this sigmoidal behavior.
使之呈现反曲现象
14:06
You grow fast and then stop --
你快速生长接着停止生长
14:09
part of our resilience.
这是我们回复力的表现
14:12
That would be bad for economies and cities.
这对经济和城市都不利
14:14
And indeed, one of the wonderful things about the theory
说实在的 这个原理奇妙之处之一在于
14:17
is that if you have super-linear scaling
如果财富创造和创新的
14:19
from wealth creation and innovation,
规模增长呈超线性
14:22
then indeed you get, from the same theory,
那么根据同一理论 你必定会得到
14:24
a beautiful rising exponential curve -- lovely.
一条美妙的正态曲线 漂亮极了
14:27
And in fact, if you compare it to data,
实际上 如果你把它与数据进行对比
14:29
it fits very well
它非常符合
14:31
with the development of cities and economies.
城市与经济的发展情况
14:33
But it has a terrible catch,
然而 它存在着一个致命局限
14:35
and the catch
这个局限就是
14:37
is that this system is destined to collapse.
这个系统注定会崩溃
14:39
And it's destined to collapse for many reasons --
它之所以注定会崩溃 原因有很多
14:42
kind of Malthusian reasons -- that you run out of resources.
多少出于此消彼长的原因 资源枯竭了
14:44
And how do you avoid that? Well we've done it before.
如何避免这种情况呢 我们曾尝试过
14:47
What we do is,
我们所做的是
14:50
as we grow and we approach the collapse,
当我们发展到接近崩溃的阶段
14:52
a major innovation takes place
一项重大的创新出现了
14:55
and we start over again,
我们又从新开始
14:58
and we start over again as we approach the next one, and so on.
向下一个目标靠近 以此类推
15:00
So there's this continuous cycle of innovation
所以这个周而复始的创新周期
15:03
that is necessary
对于维系发展
15:05
in order to sustain growth and avoid collapse.
避免崩溃 是十分必要的
15:07
The catch, however, to this
然而 这一局限
15:10
is that you have to innovate
要求你必须
15:12
faster and faster and faster.
不断加速创新
15:14
So the image
所以 情况就是
15:17
is that we're not only on a treadmill that's going faster,
我们不仅坐在一架高速运转的机器上
15:19
but we have to change the treadmill faster and faster.
我们还必须加速对机器的更新
15:22
We have to accelerate on a continuous basis.
我们必须不停地加速
15:25
And the question is: Can we, as socio-economic beings,
问题是 作为社会经济的存在
15:28
avoid a heart attack?
我们能够避免心脏病发作吗
15:31
So lastly, I'm going to finish up in this last minute or two
最后 我会花一两分钟
15:34
asking about companies.
看看公司的情况
15:37
See companies, they scale.
公司的规模不断增大
15:39
The top one, in fact, is Walmart on the right.
上面右边的是沃尔玛
15:41
It's the same plot.
同样的图表
15:43
This happens to be income and assets
这张图显示的是收入和资产
15:45
versus the size of the company as denoted by its number of employees.
比上公司规模 即员工人数
15:47
We could use sales, anything you like.
我们还可以用销售量 什么都行
15:49
There it is: after some little fluctuations at the beginning,
看 当公司进行革新
15:52
when companies are innovating,
一开始出现轻微浮动
15:55
they scale beautifully.
它们长势良好
15:57
And we've looked at 23,000 companies
我们观察了23000家
15:59
in the United States, may I say.
美国境内的企业
16:02
And I'm only showing you a little bit of this.
我今天展示给大家的只是冰山一角
16:04
What is astonishing about companies
企业令人意想不到的地方是
16:07
is that they scale sublinearly
是它们的规模增长呈次线性
16:09
like biology,
就像生物学的情况一样
16:12
indicating that they're dominated,
这表明主导它们的
16:14
not by super-linear
并不是超线性的
16:16
innovation and ideas;
创新活动和思想
16:18
they become dominated
主导它们的
16:21
by economies of scale.
是规模经济
16:23
In that interpretation,
具体说来
16:25
by bureaucracy and administration,
就是官僚主义和行政部门
16:27
and they do it beautifully, may I say.
可以说 它们干得很棒
16:29
So if you tell me the size of some company, some small company,
所以 如果你告诉我某个小企业的规模
16:31
I could have predicted the size of Walmart.
我就可以估摸出沃尔玛的规模
16:34
If it has this sublinear scaling,
如果其规模的增长呈次线性
16:37
the theory says
依照原理
16:39
we should have sigmoidal growth.
我们应该会得到一个S型的增长
16:41
There's Walmart. Doesn't look very sigmoidal.
这是沃尔玛 看起来并不十分像个S
16:44
That's what we like, hockey sticks.
我们喜欢这个形状 冰球棍
16:46
But you notice, I've cheated,
但如果你仔细看 我其实做了手脚
16:49
because I've only gone up to '94.
因为我展示的部分只到94年
16:51
Let's go up to 2008.
我们看看到了2008年情况如何
16:53
That red line is from the theory.
红线表示的是理论上的预测
16:55
So if I'd have done this in 1994,
如果我1994年开始制表
16:58
I could have predicted what Walmart would be now.
我就能够预测到沃尔玛现在的情况
17:00
And then this is repeated
这个情况
17:03
across the entire spectrum of companies.
在所有公司的生命周期中不断重复
17:05
There they are. That's 23,000 companies.
这些就是所有23000家公司
17:07
They all start looking like hockey sticks,
它们一开始都呈现冰球棍的形状
17:10
they all bend over,
接着都弯下来了
17:12
and they all die like you and me.
最后它们就像你我一样难逃一死
17:14
Thank you.
谢谢大家
17:16
(Applause)
(众人鼓掌)
17:18
Translated by Lili Liang
Reviewed by Peng Zhang

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About the speaker:

Geoffrey West - Theorist
Physicist Geoffrey West believes that complex systems from organisms to cities are in many ways governed by simple laws -- laws that can be discovered and analyzed.

Why you should listen

Trained as a theoretical physicist, Geoffrey West has turned his analytical mind toward the inner workings of more concrete things, like ... animals. In a paper for Science in 1997, he and his team uncovered what he sees as a surprisingly universal law of biology — the way in which heart rate, size and energy consumption are related, consistently, across most living animals. (Though not all animals: “There are always going to be people who say, ‘What about the crayfish?’ " he says. “Well, what about it? Every fundamental law has exceptions. But you still need the law or else all you have is observations that don’t make sense.")

A past president of the multidisciplinary Santa Fe Institute (after decades working  in high-energy physics at Los Alamos and Stanford), West now studies the behavior and development of cities. In his newest work, he proposes that one simple number, population, can predict a stunning array of details about any city, from crime rate to economic activity. It's all about the plumbing, he says, the infrastructure that powers growth or dysfunction. His next target for study: corporations.

He says: "Focusing on the differences [between cities] misses the point. Sure, there are differences, but different from what? We’ve found the what."

More profile about the speaker
Geoffrey West | Speaker | TED.com