Geoffrey West: The surprising math of cities and corporations

170,507 views ・ 2011-07-26

TED


请双击下面的英文字幕来播放视频。

翻译人员: Lili Liang 校对人员: Peng Zhang
00:16
Cities are the crucible of civilization.
0
16260
3000
城市是文明的熔炉
00:19
They have been expanding,
1
19260
2000
它们一直在扩张
00:21
urbanization has been expanding,
2
21260
2000
城市化的扩张速度
00:23
at an exponential rate in the last 200 years
3
23260
2000
在过去的200年里变得越来越快
00:25
so that by the second part of this century,
4
25260
3000
到了本世纪下半叶
00:28
the planet will be completely dominated
5
28260
2000
整个地球都将被城市
00:30
by cities.
6
30260
3000
所主宰
00:33
Cities are the origins of global warming,
7
33260
3000
城市是全球变暖的源头
00:36
impact on the environment,
8
36260
2000
影响着环境
00:38
health, pollution, disease,
9
38260
3000
卫生 污染 疾病
00:41
finance,
10
41260
2000
金融
00:43
economies, energy --
11
43260
3000
经济 能源--
00:46
they're all problems
12
46260
2000
这些问题
00:48
that are confronted by having cities.
13
48260
2000
都是由城市引起的
00:50
That's where all these problems come from.
14
50260
2000
这是所有这些问题的源头
00:52
And the tsunami of problems that we feel we're facing
15
52260
3000
我们感觉可持续性方面的问题
00:55
in terms of sustainability questions
16
55260
2000
正如海啸般扑面而来
00:57
are actually a reflection
17
57260
2000
而这些问题实际上
00:59
of the exponential increase
18
59260
2000
是与日俱增的
01:01
in urbanization across the planet.
19
61260
3000
全球城市化进程所产生的效应
01:04
Here's some numbers.
20
64260
2000
我们来看几个数字
01:06
Two hundred years ago, the United States
21
66260
2000
200年前 美国
01:08
was less than a few percent urbanized.
22
68260
2000
城市化程度不到百分之几而已
01:10
It's now more than 82 percent.
23
70260
2000
而现在则超过了82%
01:12
The planet has crossed the halfway mark a few years ago.
24
72260
3000
全球的城市化程度在几年前就超过了百分之五十
01:15
China's building 300 new cities
25
75260
2000
中国在将来的20年内
01:17
in the next 20 years.
26
77260
2000
建设300座新城市
01:19
Now listen to this:
27
79260
2000
请注意
01:21
Every week for the foreseeable future,
28
81260
3000
在将来的每一周
01:24
until 2050,
29
84260
2000
一直到2050年
01:26
every week more than a million people
30
86260
2000
每一周 将有100万人
01:28
are being added to our cities.
31
88260
2000
进入我们的城市
01:30
This is going to affect everything.
32
90260
2000
这将对一切产生影响
01:32
Everybody in this room, if you stay alive,
33
92260
2000
在座的各位 如果你一直活着
01:34
is going to be affected
34
94260
2000
你就必定要受到
01:36
by what's happening in cities
35
96260
2000
城市化所带来的
01:38
in this extraordinary phenomenon.
36
98260
2000
翻天覆地的影响
01:40
However, cities,
37
100260
3000
然而 城市
01:43
despite having this negative aspect to them,
38
103260
3000
尽管存在负面效应
01:46
are also the solution.
39
106260
2000
但城市也是问题解决的出路
01:48
Because cities are the vacuum cleaners and the magnets
40
108260
4000
这是因为城市是除尘器和吸铁石
01:52
that have sucked up creative people,
41
112260
2000
吸纳了所有创意人才
01:54
creating ideas, innovation,
42
114260
2000
创造着思想 革新
01:56
wealth and so on.
43
116260
2000
财富等等
01:58
So we have this kind of dual nature.
44
118260
2000
我们具有这样的双面性
02:00
And so there's an urgent need
45
120260
3000
我们迫切需要运用
02:03
for a scientific theory of cities.
46
123260
4000
城市的科学原理
02:07
Now these are my comrades in arms.
47
127260
3000
这些是我全副武装的同志们
02:10
This work has been done with an extraordinary group of people,
48
130260
2000
这群杰出的人士做了这些工作
02:12
and they've done all the work,
49
132260
2000
都是他们的功劳
02:14
and I'm the great bullshitter
50
134260
2000
我只会胡吹海侃
02:16
that tries to bring it all together.
51
136260
2000
做个总体介绍
02:18
(Laughter)
52
138260
2000
(众人笑)
02:20
So here's the problem: This is what we all want.
53
140260
2000
这里有个问题 这是我们希望的结果
02:22
The 10 billion people on the planet in 2050
54
142260
3000
到了2050年,地球上的10亿人
02:25
want to live in places like this,
55
145260
2000
都想生活在这样的地方
02:27
having things like this,
56
147260
2000
拥有这些东西
02:29
doing things like this,
57
149260
2000
进行这样的活动
02:31
with economies that are growing like this,
58
151260
3000
在这样的经济增长情况下
02:34
not realizing that entropy
59
154260
2000
而没有意识到
02:36
produces things like this,
60
156260
2000
人口过剩会造成这样
02:38
this, this
61
158260
4000
这样 这样
02:42
and this.
62
162260
2000
和这样的情况
02:44
And the question is:
63
164260
2000
问题是
02:46
Is that what Edinburgh and London and New York
64
166260
2000
爱丁堡 伦敦和纽约
02:48
are going to look like in 2050,
65
168260
2000
到了2050年会变成这样
02:50
or is it going to be this?
66
170260
2000
还是这样
02:52
That's the question.
67
172260
2000
这是个问题
02:54
I must say, many of the indicators
68
174260
2000
我不得不说 许多这样的参数
02:56
look like this is what it's going to look like,
69
176260
3000
似乎更可能是它们将来的样子
02:59
but let's talk about it.
70
179260
3000
我们来探讨一下
03:02
So my provocative statement
71
182260
3000
我敢大胆地说
03:05
is that we desperately need a serious scientific theory of cities.
72
185260
3000
我们急需一个严谨的城市科学理论
03:08
And scientific theory means quantifiable --
73
188260
3000
科学理论意味着它是可量化的
03:11
relying on underlying generic principles
74
191260
3000
依据基本的普遍原理
03:14
that can be made into a predictive framework.
75
194260
2000
我们能够推导出一个可预见的结构
03:16
That's the quest.
76
196260
2000
这是我们的目标
03:18
Is that conceivable?
77
198260
2000
这可能吗
03:20
Are there universal laws?
78
200260
2000
有这样的普遍定律吗
03:22
So here's two questions
79
202260
2000
每当我思考这个问题
03:24
that I have in my head when I think about this problem.
80
204260
2000
两个疑问一直在我脑子里打转
03:26
The first is:
81
206260
2000
第一
03:28
Are cities part of biology?
82
208260
2000
城市是生物界的一部分吗
03:30
Is London a great big whale?
83
210260
2000
伦敦是一只大鲸鱼吗
03:32
Is Edinburgh a horse?
84
212260
2000
爱丁堡是一匹马吗
03:34
Is Microsoft a great big anthill?
85
214260
2000
微软是一座巨型蚁山吗
03:36
What do we learn from that?
86
216260
2000
我们从中能得到什么启发
03:38
We use them metaphorically --
87
218260
2000
我们可以使用比喻
03:40
the DNA of a company, the metabolism of a city, and so on --
88
220260
2000
一个公司的DNA 一个城市的新陈代谢 等等
03:42
is that just bullshit, metaphorical bullshit,
89
222260
3000
这些都是胡扯 乱七八糟的比喻
03:45
or is there serious substance to it?
90
225260
3000
还是有严谨的依据
03:48
And if that is the case,
91
228260
2000
如果确有依据
03:50
how come that it's very hard to kill a city?
92
230260
2000
为什么城市总是生生不息呢
03:52
You could drop an atom bomb on a city,
93
232260
2000
你可以扔一个原子弹炸毁一个城市
03:54
and 30 years later it's surviving.
94
234260
2000
而30年之后 它依然存在
03:56
Very few cities fail.
95
236260
3000
消亡的城市寥寥无几
03:59
All companies die, all companies.
96
239260
3000
而所有公司都会关门 无一例外
04:02
And if you have a serious theory, you should be able to predict
97
242260
2000
如果你掌握了缜密的原理 你就应该可以预测
04:04
when Google is going to go bust.
98
244260
3000
谷歌什么时候关门大吉
04:07
So is that just another version
99
247260
3000
这是不是
04:10
of this?
100
250260
2000
这个画面的翻版
04:12
Well we understand this very well.
101
252260
2000
我们对此非常清楚
04:14
That is, you ask any generic question about this --
102
254260
2000
如果你随便问一个常识问题
04:16
how many trees of a given size,
103
256260
2000
某已知体积的大树有多少棵
04:18
how many branches of a given size does a tree have,
104
258260
2000
一颗体积已知的大树有多少分枝
04:20
how many leaves,
105
260260
2000
多少树叶
04:22
what is the energy flowing through each branch,
106
262260
2000
每根树枝中流动的能量是什么
04:24
what is the size of the canopy,
107
264260
2000
树冠有多大
04:26
what is its growth, what is its mortality?
108
266260
2000
它长势如何 寿命多长
04:28
We have a mathematical framework
109
268260
2000
我们有一套数学体系
04:30
based on generic universal principles
110
270260
3000
建立在普遍原理的基础上
04:33
that can answer those questions.
111
273260
2000
它能够解答那些问题
04:35
And the idea is can we do the same for this?
112
275260
4000
问题是 它是否适用于城市
04:40
So the route in is recognizing
113
280260
3000
首先我们要认识到
04:43
one of the most extraordinary things about life,
114
283260
2000
生命最奇妙之处 其中之一
04:45
is that it is scalable,
115
285260
2000
就是它是会长大的
04:47
it works over an extraordinary range.
116
287260
2000
它能够长到非常之大
04:49
This is just a tiny range actually:
117
289260
2000
这只是很小的一个尺度
04:51
It's us mammals;
118
291260
2000
这是我们 哺乳动物
04:53
we're one of these.
119
293260
2000
我们是其中之一
04:55
The same principles, the same dynamics,
120
295260
2000
相同的原理 相同的活动
04:57
the same organization is at work
121
297260
2000
相同的组织 在所有这些动物中
04:59
in all of these, including us,
122
299260
2000
发挥着作用 我们也包括在内
05:01
and it can scale over a range of 100 million in size.
123
301260
3000
它能够长大到一亿个单位
05:04
And that is one of the main reasons
124
304260
3000
生命如此周而复始 欣欣向荣
05:07
life is so resilient and robust --
125
307260
2000
这就是原因之一
05:09
scalability.
126
309260
2000
伸展性
05:11
We're going to discuss that in a moment more.
127
311260
3000
我们一会再讨论这个
05:14
But you know, at a local level,
128
314260
2000
从我们自身出发
05:16
you scale; everybody in this room is scaled.
129
316260
2000
你会长大 在座所有人的身体都长大了
05:18
That's called growth.
130
318260
2000
这就是成长
05:20
Here's how you grew.
131
320260
2000
你就是这么成长的
05:22
Rat, that's a rat -- could have been you.
132
322260
2000
这是一只老鼠 也可以是你
05:24
We're all pretty much the same.
133
324260
3000
我们之间非常相似
05:27
And you see, you're very familiar with this.
134
327260
2000
你们可以看到 你的情况与之十分相似
05:29
You grow very quickly and then you stop.
135
329260
2000
你长得很快 接着停止生长
05:31
And that line there
136
331260
2000
上面的那条线
05:33
is a prediction from the same theory,
137
333260
2000
是同一理论推导出来的
05:35
based on the same principles,
138
335260
2000
所依据的原理
05:37
that describes that forest.
139
337260
2000
与描述森林的原理相同
05:39
And here it is for the growth of a rat,
140
339260
2000
这显示的是老鼠的生长情况
05:41
and those points on there are data points.
141
341260
2000
上面的点是数据点
05:43
This is just the weight versus the age.
142
343260
2000
即体重与年龄的比例
05:45
And you see, it stops growing.
143
345260
2000
你看 它停止生长了
05:47
Very, very good for biology --
144
347260
2000
这对生物界非常有益
05:49
also one of the reasons for its great resilience.
145
349260
2000
这也证明了其强大的伸展性
05:51
Very, very bad
146
351260
2000
但对我们目前规划中的
05:53
for economies and companies and cities
147
353260
2000
经济 公司和城市而而言
05:55
in our present paradigm.
148
355260
2000
这是非常糟糕的
05:57
This is what we believe.
149
357260
2000
我们就是这么认为的
05:59
This is what our whole economy
150
359260
2000
这就是我们的经济
06:01
is thrusting upon us,
151
361260
2000
强加给我们的
06:03
particularly illustrated in that left-hand corner:
152
363260
3000
左上角的图表凸显了这一点
06:06
hockey sticks.
153
366260
2000
冰球棍
06:08
This is a bunch of software companies --
154
368260
2000
它显示的是众多软件公司
06:10
and what it is is their revenue versus their age --
155
370260
2000
收入与公司建立时间的比例
06:12
all zooming away,
156
372260
2000
它们都平步青云
06:14
and everybody making millions and billions of dollars.
157
374260
2000
每家公司都大把大把地捞钱
06:16
Okay, so how do we understand this?
158
376260
3000
那么 我们如何解读
06:19
So let's first talk about biology.
159
379260
3000
我们先来讨论一下生物学
06:22
This is explicitly showing you
160
382260
2000
这让你清清楚楚地看到
06:24
how things scale,
161
384260
2000
事物的规模是如何增大的
06:26
and this is a truly remarkable graph.
162
386260
2000
这幅图表意义非凡
06:28
What is plotted here is metabolic rate --
163
388260
3000
上面显示的是新陈代谢率
06:31
how much energy you need per day to stay alive --
164
391260
3000
为维持生命你每天需要摄入的能量
06:34
versus your weight, your mass,
165
394260
2000
比上你的体重
06:36
for all of us bunch of organisms.
166
396260
3000
这适用于人类以及许多其它生物
06:39
And it's plotted in this funny way by going up by factors of 10,
167
399260
3000
它的结构很有意思 以10倍递进
06:42
otherwise you couldn't get everything on the graph.
168
402260
2000
否则你无法看到全局
06:44
And what you see if you plot it
169
404260
2000
在这样一个有意思的图标中
06:46
in this slightly curious way
170
406260
2000
你可以看到
06:48
is that everybody lies on the same line.
171
408260
3000
每个人都落在了同一条线上
06:51
Despite the fact that this is the most complex and diverse system
172
411260
3000
尽管这是宇宙中
06:54
in the universe,
173
414260
3000
最为纷繁复杂的系统
06:57
there's an extraordinary simplicity
174
417260
2000
但它显示了一个
06:59
being expressed by this.
175
419260
2000
极为简单现象
07:01
It's particularly astonishing
176
421260
3000
这令人震惊
07:04
because each one of these organisms,
177
424260
2000
这上面的每个物种
07:06
each subsystem, each cell type, each gene,
178
426260
2000
每个子系统 每个细胞种类 每个基因
07:08
has evolved in its own unique environmental niche
179
428260
4000
都在其独特的生态位和历史中
07:12
with its own unique history.
180
432260
3000
得到进化发展
07:15
And yet, despite all of that Darwinian evolution
181
435260
3000
然而 即使经过了达尔文派支持的进化论
07:18
and natural selection,
182
438260
2000
和自然选择
07:20
they've been constrained to lie on a line.
183
440260
2000
它们最终还是集中到了一条线上
07:22
Something else is going on.
184
442260
2000
还有其它力量在发挥作用
07:24
Before I talk about that,
185
444260
2000
谈到这之前
07:26
I've written down at the bottom there
186
446260
2000
我在底下标出了
07:28
the slope of this curve, this straight line.
187
448260
2000
这条曲线的斜率 即这条直线
07:30
It's three-quarters, roughly,
188
450260
2000
大约为3比4
07:32
which is less than one -- and we call that sublinear.
189
452260
3000
小于1 呈“次线性”
07:35
And here's the point of that.
190
455260
2000
这里有一点值得注意
07:37
It says that, if it were linear,
191
457260
3000
当最大斜率
07:40
the steepest slope,
192
460260
2000
呈线性
07:42
then doubling the size
193
462260
2000
那么当体型翻倍
07:44
you would require double the amount of energy.
194
464260
2000
所需能量也随之翻倍
07:46
But it's sublinear, and what that translates into
195
466260
3000
而若呈次线性 情况则是
07:49
is that, if you double the size of the organism,
196
469260
2000
当生物的体型翻倍
07:51
you actually only need 75 percent more energy.
197
471260
3000
它实际只需增加75%的能量
07:54
So a wonderful thing about all of biology
198
474260
2000
生物的奇妙之处就在于
07:56
is that it expresses an extraordinary economy of scale.
199
476260
3000
它巧妙地展现了经济的伸展能力
07:59
The bigger you are systematically,
200
479260
2000
根据准确定义的规律
08:01
according to very well-defined rules,
201
481260
2000
一个系统越大
08:03
less energy per capita.
202
483260
3000
其所需的平均能力越少
08:06
Now any physiological variable you can think of,
203
486260
3000
你能够想到的任何变量
08:09
any life history event you can think of,
204
489260
2000
任何历史事件
08:11
if you plot it this way, looks like this.
205
491260
3000
只要你照着这样制表 都会得到相似的图形
08:14
There is an extraordinary regularity.
206
494260
2000
其一致性非常惊人
08:16
So you tell me the size of a mammal,
207
496260
2000
只要你说出一种哺乳动物的体型
08:18
I can tell you at the 90 percent level everything about it
208
498260
3000
我就能告诉你关于其生理和生命周期等情况
08:21
in terms of its physiology, life history, etc.
209
501260
4000
正确率90%
08:25
And the reason for this is because of networks.
210
505260
3000
原因就在于网络
08:28
All of life is controlled by networks --
211
508260
3000
所有生命都由网络所控制
08:31
from the intracellular through the multicellular
212
511260
2000
不论是单细胞还是多细胞生物
08:33
through the ecosystem level.
213
513260
2000
整个生态系统都是如此
08:35
And you're very familiar with these networks.
214
515260
3000
你对这些网络并不陌生
08:39
That's a little thing that lives inside an elephant.
215
519260
3000
这是生长在大象体内的一种小生物
08:42
And here's the summary of what I'm saying.
216
522260
3000
这是我讲话内容的总结
08:45
If you take those networks,
217
525260
2000
你有了这些网络
08:47
this idea of networks,
218
527260
2000
网络的概念
08:49
and you apply universal principles,
219
529260
2000
再用上普遍原理
08:51
mathematizable, universal principles,
220
531260
2000
数学化的普遍原理
08:53
all of these scalings
221
533260
2000
所有规模增长
08:55
and all of these constraints follow,
222
535260
3000
所有限制因素
08:58
including the description of the forest,
223
538260
2000
包括森林的情况
09:00
the description of your circulatory system,
224
540260
2000
你循环系统的情况
09:02
the description within cells.
225
542260
2000
细胞内部情况等
09:04
One of the things I did not stress in that introduction
226
544260
3000
我在介绍中没有提及的一点是
09:07
was that, systematically, the pace of life
227
547260
3000
生长的节奏会随着你体型的增大
09:10
decreases as you get bigger.
228
550260
2000
而系统性地减缓
09:12
Heart rates are slower; you live longer;
229
552260
3000
心率会减缓 你活得更久
09:15
diffusion of oxygen and resources
230
555260
2000
通过细胞膜的氧气
09:17
across membranes is slower, etc.
231
557260
2000
和物质的流动减缓
09:19
The question is: Is any of this true
232
559260
2000
问题是 这是否
09:21
for cities and companies?
233
561260
3000
也适用于城市和企业
09:24
So is London a scaled up Birmingham,
234
564260
3000
伦敦是否是长大了的伯明翰
09:27
which is a scaled up Brighton, etc., etc.?
235
567260
3000
而伯明翰是否是长大了的布莱顿 等等
09:30
Is New York a scaled up San Francisco,
236
570260
2000
纽约是否是长大了的旧金山
09:32
which is a scaled up Santa Fe?
237
572260
2000
而旧金山是否是长大了的圣达菲
09:34
Don't know. We will discuss that.
238
574260
2000
不知道 我们稍候再讨论
09:36
But they are networks,
239
576260
2000
但它们都是网络
09:38
and the most important network of cities
240
578260
2000
而城市最重要的网络
09:40
is you.
241
580260
2000
就是你
09:42
Cities are just a physical manifestation
242
582260
3000
城市只是
09:45
of your interactions,
243
585260
2000
你我社会活动
09:47
our interactions,
244
587260
2000
以及个体相互聚拢集合的
09:49
and the clustering and grouping of individuals.
245
589260
2000
物质表现
09:51
Here's just a symbolic picture of that.
246
591260
3000
这只是一张简易图表
09:54
And here's scaling of cities.
247
594260
2000
这是城市规模的扩大
09:56
This shows that in this very simple example,
248
596260
3000
这幅图显示出了一个非常简单的例子
09:59
which happens to be a mundane example
249
599260
2000
这例子很寻常
10:01
of number of petrol stations
250
601260
2000
加油站的数量
10:03
as a function of size --
251
603260
2000
作为规模
10:05
plotted in the same way as the biology --
252
605260
2000
按照同于生物的方法制表
10:07
you see exactly the same kind of thing.
253
607260
2000
你能够观察到一模一样的结果
10:09
There is a scaling.
254
609260
2000
上面显示了增长的趋势
10:11
That is that the number of petrol stations in the city
255
611260
4000
你告诉我城市的规模
10:15
is now given to you
256
615260
2000
我就能够说出
10:17
when you tell me its size.
257
617260
2000
这座城市有多少个加油站
10:19
The slope of that is less than linear.
258
619260
3000
斜率呈次线性
10:22
There is an economy of scale.
259
622260
2000
这是规模经济
10:24
Less petrol stations per capita the bigger you are -- not surprising.
260
624260
3000
城市越大 人均加油站数量就越小 并不稀奇
10:27
But here's what's surprising.
261
627260
2000
稀奇的在这里
10:29
It scales in the same way everywhere.
262
629260
2000
增长的规律在哪里都适用
10:31
This is just European countries,
263
631260
2000
这反映的只是欧洲国家的情况
10:33
but you do it in Japan or China or Colombia,
264
633260
3000
但如果你用同样的方法观察日本 中国或哥伦比亚
10:36
always the same
265
636260
2000
结果都是一样的
10:38
with the same kind of economy of scale
266
638260
2000
同样的规模经济
10:40
to the same degree.
267
640260
2000
同样的水平
10:42
And any infrastructure you look at --
268
642260
3000
而且 你看到的所有基础设施
10:45
whether it's the length of roads, length of electrical lines --
269
645260
3000
不论是道路还是电线的长度
10:48
anything you look at
270
648260
2000
不论是什么
10:50
has the same economy of scale scaling in the same way.
271
650260
3000
都存在增长模式相同的规模经济
10:53
It's an integrated system
272
653260
2000
这个综合体系
10:55
that has evolved despite all the planning and so on.
273
655260
3000
不停演进 无论如何规划都是如此
10:58
But even more surprising
274
658260
2000
而当你看到
11:00
is if you look at socio-economic quantities,
275
660260
2000
社会经济数量
11:02
quantities that have no analog in biology,
276
662260
3000
即八千到一万年前
11:05
that have evolved when we started forming communities
277
665260
3000
我们开始建立社区时的社会经济数量
11:08
eight to 10,000 years ago.
278
668260
2000
你们会感到更加意外
11:10
The top one is wages as a function of size
279
670260
2000
上图以工资作为规模参数
11:12
plotted in the same way.
280
672260
2000
同理制表
11:14
And the bottom one is you lot --
281
674260
2000
而下面的是“你”
11:16
super-creatives plotted in the same way.
282
676260
3000
也就是超级智能人 同理制表
11:19
And what you see
283
679260
2000
上面显示出
11:21
is a scaling phenomenon.
284
681260
2000
一个规模增长的现象
11:23
But most important in this,
285
683260
2000
但图上最重要的是
11:25
the exponent, the analog to that three-quarters
286
685260
2000
新陈代谢率的幂
11:27
for the metabolic rate,
287
687260
2000
近似于三分之四
11:29
is bigger than one -- it's about 1.15 to 1.2.
288
689260
2000
大于1 大约在1.15和1.2之间
11:31
Here it is,
289
691260
2000
意思是
11:33
which says that the bigger you are
290
693260
3000
规模越大
11:36
the more you have per capita, unlike biology --
291
696260
3000
人均数就越多 与生物学的情况相反
11:39
higher wages, more super-creative people per capita as you get bigger,
292
699260
4000
工资越高 就有越多的超级智能人出现
11:43
more patents per capita, more crime per capita.
293
703260
3000
人均专利和犯罪率越高
11:46
And we've looked at everything:
294
706260
2000
我们研究了所有事物
11:48
more AIDS cases, flu, etc.
295
708260
3000
艾滋病病例 流感等等
11:51
And here, they're all plotted together.
296
711260
2000
把这些都放在一起制成表
11:53
Just to show you what we plotted,
297
713260
2000
让你们看到
11:55
here is income, GDP --
298
715260
3000
我们把收入 GDP
11:58
GDP of the city --
299
718260
2000
城市的GDP
12:00
crime and patents all on one graph.
300
720260
2000
犯罪和专利都放在一张图上
12:02
And you can see, they all follow the same line.
301
722260
2000
你们可以看到
12:04
And here's the statement.
302
724260
2000
下面是图的表述
12:06
If you double the size of a city from 100,000 to 200,000,
303
726260
3000
如果一个城市的规模从10万增长至20万
12:09
from a million to two million, 10 to 20 million,
304
729260
2000
从一百万到两百万 从一千万到两千万
12:11
it doesn't matter,
305
731260
2000
都一样
12:13
then systematically
306
733260
2000
在这个城市中
12:15
you get a 15 percent increase
307
735260
2000
工资 财富 艾滋病病例
12:17
in wages, wealth, number of AIDS cases,
308
737260
2000
警察人数
12:19
number of police,
309
739260
2000
任何你能想到的事物
12:21
anything you can think of.
310
741260
2000
都会系统地增加15%
12:23
It goes up by 15 percent,
311
743260
2000
对于所有事物都是如此
12:25
and you have a 15 percent savings
312
745260
3000
你还能节省
12:28
on the infrastructure.
313
748260
3000
15%的基础设施经费
12:31
This, no doubt, is the reason
314
751260
3000
这无疑就是
12:34
why a million people a week are gathering in cities.
315
754260
3000
城市每周新增一百万人口的原因
12:37
Because they think that all those wonderful things --
316
757260
3000
他们觉得那些美好的事物
12:40
like creative people, wealth, income --
317
760260
2000
包括创新人才 财富 收入
12:42
is what attracts them,
318
762260
2000
对他们有吸引力
12:44
forgetting about the ugly and the bad.
319
764260
2000
而忘记了城市丑恶的一面
12:46
What is the reason for this?
320
766260
2000
原因何在
12:48
Well I don't have time to tell you about all the mathematics,
321
768260
3000
我没有时间跟大家解释其中的数学
12:51
but underlying this is the social networks,
322
771260
3000
社会网络是其基础
12:54
because this is a universal phenomenon.
323
774260
3000
因为这是个普遍现象
12:57
This 15 percent rule
324
777260
3000
这个15%的规律
13:00
is true
325
780260
2000
是真的
13:02
no matter where you are on the planet --
326
782260
2000
无论你在地球上哪个角落
13:04
Japan, Chile,
327
784260
2000
日本 智利
13:06
Portugal, Scotland, doesn't matter.
328
786260
3000
葡萄牙 苏格兰 都一样
13:09
Always, all the data shows it's the same,
329
789260
3000
尽管城市的发展是各自独立的
13:12
despite the fact that these cities have evolved independently.
330
792260
3000
然而所有数据显示的结果都是一样的
13:15
Something universal is going on.
331
795260
2000
这里蕴藏着一个普遍的规律
13:17
The universality, to repeat, is us --
332
797260
3000
普遍性在于我们
13:20
that we are the city.
333
800260
2000
我们就是城市
13:22
And it is our interactions and the clustering of those interactions.
334
802260
3000
城市是我们相互活动以及这些活动的汇集
13:25
So there it is, I've said it again.
335
805260
2000
我刚才说过了
13:27
So if it is those networks and their mathematical structure,
336
807260
3000
那些网络和它们的数学结构
13:30
unlike biology, which had sublinear scaling,
337
810260
3000
与呈次线性的生物界不同
13:33
economies of scale,
338
813260
2000
生物是规模经济
13:35
you had the slowing of the pace of life
339
815260
2000
会随着规模的增大
13:37
as you get bigger.
340
817260
2000
而减缓生长的速度
13:39
If it's social networks with super-linear scaling --
341
819260
2000
如果城市的社会网络呈现超线性
13:41
more per capita --
342
821260
2000
人均数值越高
13:43
then the theory says
343
823260
2000
那么依照原理
13:45
that you increase the pace of life.
344
825260
2000
生长速度便会增加
13:47
The bigger you are, life gets faster.
345
827260
2000
你长得越大 生长速度就越快
13:49
On the left is the heart rate showing biology.
346
829260
2000
左边是心率
13:51
On the right is the speed of walking
347
831260
2000
右边是行走的速度
13:53
in a bunch of European cities,
348
833260
2000
在许多欧洲城市
13:55
showing that increase.
349
835260
2000
显示这样的增长情况
13:57
Lastly, I want to talk about growth.
350
837260
3000
最后 我想谈谈增长
14:00
This is what we had in biology, just to repeat.
351
840260
3000
在重复一下 这是生物学的情况
14:03
Economies of scale
352
843260
3000
规模经济
14:06
gave rise to this sigmoidal behavior.
353
846260
3000
使之呈现反曲现象
14:09
You grow fast and then stop --
354
849260
3000
你快速生长接着停止生长
14:12
part of our resilience.
355
852260
2000
这是我们回复力的表现
14:14
That would be bad for economies and cities.
356
854260
3000
这对经济和城市都不利
14:17
And indeed, one of the wonderful things about the theory
357
857260
2000
说实在的 这个原理奇妙之处之一在于
14:19
is that if you have super-linear scaling
358
859260
3000
如果财富创造和创新的
14:22
from wealth creation and innovation,
359
862260
2000
规模增长呈超线性
14:24
then indeed you get, from the same theory,
360
864260
3000
那么根据同一理论 你必定会得到
14:27
a beautiful rising exponential curve -- lovely.
361
867260
2000
一条美妙的正态曲线 漂亮极了
14:29
And in fact, if you compare it to data,
362
869260
2000
实际上 如果你把它与数据进行对比
14:31
it fits very well
363
871260
2000
它非常符合
14:33
with the development of cities and economies.
364
873260
2000
城市与经济的发展情况
14:35
But it has a terrible catch,
365
875260
2000
然而 它存在着一个致命局限
14:37
and the catch
366
877260
2000
这个局限就是
14:39
is that this system is destined to collapse.
367
879260
3000
这个系统注定会崩溃
14:42
And it's destined to collapse for many reasons --
368
882260
2000
它之所以注定会崩溃 原因有很多
14:44
kind of Malthusian reasons -- that you run out of resources.
369
884260
3000
多少出于此消彼长的原因 资源枯竭了
14:47
And how do you avoid that? Well we've done it before.
370
887260
3000
如何避免这种情况呢 我们曾尝试过
14:50
What we do is,
371
890260
2000
我们所做的是
14:52
as we grow and we approach the collapse,
372
892260
3000
当我们发展到接近崩溃的阶段
14:55
a major innovation takes place
373
895260
3000
一项重大的创新出现了
14:58
and we start over again,
374
898260
2000
我们又从新开始
15:00
and we start over again as we approach the next one, and so on.
375
900260
3000
向下一个目标靠近 以此类推
15:03
So there's this continuous cycle of innovation
376
903260
2000
所以这个周而复始的创新周期
15:05
that is necessary
377
905260
2000
对于维系发展
15:07
in order to sustain growth and avoid collapse.
378
907260
3000
避免崩溃 是十分必要的
15:10
The catch, however, to this
379
910260
2000
然而 这一局限
15:12
is that you have to innovate
380
912260
2000
要求你必须
15:14
faster and faster and faster.
381
914260
3000
不断加速创新
15:17
So the image
382
917260
2000
所以 情况就是
15:19
is that we're not only on a treadmill that's going faster,
383
919260
3000
我们不仅坐在一架高速运转的机器上
15:22
but we have to change the treadmill faster and faster.
384
922260
3000
我们还必须加速对机器的更新
15:25
We have to accelerate on a continuous basis.
385
925260
3000
我们必须不停地加速
15:28
And the question is: Can we, as socio-economic beings,
386
928260
3000
问题是 作为社会经济的存在
15:31
avoid a heart attack?
387
931260
3000
我们能够避免心脏病发作吗
15:34
So lastly, I'm going to finish up in this last minute or two
388
934260
3000
最后 我会花一两分钟
15:37
asking about companies.
389
937260
2000
看看公司的情况
15:39
See companies, they scale.
390
939260
2000
公司的规模不断增大
15:41
The top one, in fact, is Walmart on the right.
391
941260
2000
上面右边的是沃尔玛
15:43
It's the same plot.
392
943260
2000
同样的图表
15:45
This happens to be income and assets
393
945260
2000
这张图显示的是收入和资产
15:47
versus the size of the company as denoted by its number of employees.
394
947260
2000
比上公司规模 即员工人数
15:49
We could use sales, anything you like.
395
949260
3000
我们还可以用销售量 什么都行
15:52
There it is: after some little fluctuations at the beginning,
396
952260
3000
看 当公司进行革新
15:55
when companies are innovating,
397
955260
2000
一开始出现轻微浮动
15:57
they scale beautifully.
398
957260
2000
它们长势良好
15:59
And we've looked at 23,000 companies
399
959260
3000
我们观察了23000家
16:02
in the United States, may I say.
400
962260
2000
美国境内的企业
16:04
And I'm only showing you a little bit of this.
401
964260
3000
我今天展示给大家的只是冰山一角
16:07
What is astonishing about companies
402
967260
2000
企业令人意想不到的地方是
16:09
is that they scale sublinearly
403
969260
3000
是它们的规模增长呈次线性
16:12
like biology,
404
972260
2000
就像生物学的情况一样
16:14
indicating that they're dominated,
405
974260
2000
这表明主导它们的
16:16
not by super-linear
406
976260
2000
并不是超线性的
16:18
innovation and ideas;
407
978260
3000
创新活动和思想
16:21
they become dominated
408
981260
2000
主导它们的
16:23
by economies of scale.
409
983260
2000
是规模经济
16:25
In that interpretation,
410
985260
2000
具体说来
16:27
by bureaucracy and administration,
411
987260
2000
就是官僚主义和行政部门
16:29
and they do it beautifully, may I say.
412
989260
2000
可以说 它们干得很棒
16:31
So if you tell me the size of some company, some small company,
413
991260
3000
所以 如果你告诉我某个小企业的规模
16:34
I could have predicted the size of Walmart.
414
994260
3000
我就可以估摸出沃尔玛的规模
16:37
If it has this sublinear scaling,
415
997260
2000
如果其规模的增长呈次线性
16:39
the theory says
416
999260
2000
依照原理
16:41
we should have sigmoidal growth.
417
1001260
3000
我们应该会得到一个S型的增长
16:44
There's Walmart. Doesn't look very sigmoidal.
418
1004260
2000
这是沃尔玛 看起来并不十分像个S
16:46
That's what we like, hockey sticks.
419
1006260
3000
我们喜欢这个形状 冰球棍
16:49
But you notice, I've cheated,
420
1009260
2000
但如果你仔细看 我其实做了手脚
16:51
because I've only gone up to '94.
421
1011260
2000
因为我展示的部分只到94年
16:53
Let's go up to 2008.
422
1013260
2000
我们看看到了2008年情况如何
16:55
That red line is from the theory.
423
1015260
3000
红线表示的是理论上的预测
16:58
So if I'd have done this in 1994,
424
1018260
2000
如果我1994年开始制表
17:00
I could have predicted what Walmart would be now.
425
1020260
3000
我就能够预测到沃尔玛现在的情况
17:03
And then this is repeated
426
1023260
2000
这个情况
17:05
across the entire spectrum of companies.
427
1025260
2000
在所有公司的生命周期中不断重复
17:07
There they are. That's 23,000 companies.
428
1027260
3000
这些就是所有23000家公司
17:10
They all start looking like hockey sticks,
429
1030260
2000
它们一开始都呈现冰球棍的形状
17:12
they all bend over,
430
1032260
2000
接着都弯下来了
17:14
and they all die like you and me.
431
1034260
2000
最后它们就像你我一样难逃一死
17:16
Thank you.
432
1036260
2000
谢谢大家
17:18
(Applause)
433
1038260
9000
(众人鼓掌)
关于本网站

这个网站将向你介绍对学习英语有用的YouTube视频。你将看到来自世界各地的一流教师教授的英语课程。双击每个视频页面上显示的英文字幕,即可从那里播放视频。字幕会随着视频的播放而同步滚动。如果你有任何意见或要求,请使用此联系表与我们联系。

https://forms.gle/WvT1wiN1qDtmnspy7