
Chinese: 
翻译人员: Kai Cui
校对人员: Tingting Zhong
去年12月，
一位记者问我和
我的同事Nobel Laureates，
如果只让我们给
世界上一堂课，
那会是什么？
出乎我意料的是
两位经济学家、两位生物学家
一位化学家和三位物理学家，
都给出了相同的回答----
就是不确定性。
所以我今天会
和你们谈谈不确定性。
对于任何问题，
我们都要了解它的不确定性。
不确定性是宇宙整体结构的核心。
让我用一束激光来阐述这个问题。
激光可以投射出一个很小，
但并不是无限小的光点。

English: 
Transcriber: Tijana Mihajlović
Reviewer: Denise RQ
Last December,
me and my fellow Nobel Laureates
were asked by a journalist
if there was one thing
that we could teach the world,
what would it be?
And to my surprise,
two economists, two biologists,
a chemist, and three physicists
gave the same answer.
And that answer was about uncertainty.
So I'm going to talk to you today
about uncertainty.
To understand anything,
you must understand its uncertainty.
Uncertainty is at the heart
of the fabric of the Universe.
I'm going to illustrate this with a laser.
A laser puts out a small,
but not infinitesimally small
point of light.

English: 
You might think that if I go through
and I try to make
that point of light smaller
by, for example,
bringing two jars of a slit together,
that I could make that point
as small as I want.
I just want to make
those slits closer and closer.
So let's see what happens
when I do this for real.
My friends at Mount Stromlo gave a call
and made up a nice little invent,
a little here.
By essentially adjusting
the laser, the slit -
we're going to go through
and we are going to see what happens
when I close the jaws of the slit.
The more I close it,
instead of getting smaller,
the laser gets spread out.
So it works exactly the opposite
of what I was expecting.
And that's due to something known
as Heisenberg's Uncertainty Principle.
Heisenberg's Uncertainty Principle states

Chinese: 
你或许认为，如果我试图-
把这个光点变得更小，
比如，让两片合页逐渐靠拢-
就可以让光点变得尽可能地小。
我只需要让合页间的缝隙尽量靠拢。
那么让我们看看现实中的情况。
通过斯特朗洛山的朋友帮忙，
制作了一个小装置，
用来调整激光和缝隙。
我们可以看到实际的情况。
当我把缝隙逐渐合拢，
缝隙越小，
光点并没有变小，
而是呈发散状态。
所以实际情况
与我们的期望正好相反。
这个现象来源于
海森堡的不确定性原理。
海森堡的不确定性原理是说，

Chinese: 
你不可能同时知道-
一个事物的位置和动量。
光的动量就是它的方向。
所以，当我把缝隙变得越来越小，
我实际上是在限制光的位置。
但是量子理论认为你不可以这么做。
光的方向具有不确定性。
所以并没有出现一个更小的光点
而是发生了随机的散射，
这就是我们所看到的。
生活中的很多事情
我们都可以认为是一系列小的决定。
例如，从一个点开始
我可以向左，或者向右。
我可以说，我有50％的几率向左或者向右。
下面我有一个树状的决策图。

English: 
that you can't know exactly
where something is
and know its momentum at the same time.
Light's momentum is really its direction.
So, as I bring those slits
closer and closer together,
I actually constrain where the light is.
But the quantum world says
you can't do that.
The light then has an uncertain direction.
So instead of being a smaller point,
the light has a randomness put out to it,
which is that pattern that we saw.
Many things in life you can think of
as a series of little decisions.
For example, if I start at a point,
and I can go left or right,
well, I do it, let's say, 50% of the time
I can go left or right.
Let's say I have another decision tree
down below that.

English: 
I can go left, I can go right,
or I can go to the middle.
Because I've had two chances
to go to the middle from above,
I would do that 50% of the time.
I only go one quarter to the left
and one quarter all the way to the right.
And you can build up such a decision tree,
and Pascal did this.
It's called Pascal's triangle.
You get a probability
of where you are going to end up.
I brought something like this
with me today.
It's this machine right here.
This is a machine you can put balls into
and you can randomly see what happens.
So, for example, if I put a ball in here,
it'll bounce down
and it'll end up somewhere.
It's essentially an enactment
of Pascal's triangle.
I need two people
from the audience to help me,
and I think I am going to have
Sly and Jon right there
come up and help me if that's okay.
You know who you are.
(Laughter)
What they are going to do
is they are going to,
as fast as they can -

Chinese: 
我可以向左、向右，或者选择中间。
因为从最上面开始
我有两个机会可以选择中间，
选择的几率是50％。
只有四分之一的几率选择左或者右。
你可以建立一个这样的决策树状图
帕斯卡就这样做了。
这就叫做帕斯卡三角形。
你会得到每一个最终结果的选择几率。
我今天带来了这样一个东西。
就是这个机器。
你可以从这里把一个球放进去，
然后它随机掉落。
比如，我把一个球放在这里，
它会随机掉落在下面的一个格子里。
可以说这是一个
现实版的帕斯卡三角形。
我需要两位观众帮忙，
就找这边的Sly和Jon好了。
如果方便请上来帮我个忙。
就是你们了。
（笑声）
他们要做的-
就是尽量快速地-

Chinese: 
比现在的速度要快，
因为我只有18分钟，
（笑声）
把球放在这个机器里，
我们看看结果如何。
机器会记录落在每个格子里的球的数量。
所以你们两位的动作要快。
两人合作。
在我的演讲结束之前
你们要做好这件事。
投入的球越多越好，行吗？
现在开始吧，我会继续演讲。
（笑声）
好的。
情况是这样，
如果你的生活中
充满了一系列随机的事件，
你得到的结果是被称为
“钟形曲线”的东西，
我们也把它叫做“正态分布”
或者“高斯分布”。
所以，如果你只有少量的随机事件，
你不会得到这样的结果。
但是如果你的随机事件越来越多，
它们累积起来，
就会形成这个特点极为鲜明的状态。
高斯曾经用数学的方式将其表达出来。
在大部分情况下，

English: 
faster than they are going right now,
because I only have 18 minutes -
(Laughter)
put balls through this machine,
and we're going to see what happens.
This machine counts things where they end.
So you guys have to go through
as fast as you can.
Work together,
and during the rest of my talk,
you are going to build up this.
And the more you do,
the better it is, okay?
So go for it, and I'll keep on going.
(Laughter)
Alright.
It turns out that if you have
a series of random events in life,
you end up with something
called a Bell Shaped Curve,
which we also call a Normal Distribution
or Gaussian Distribution.
So, for example, if you have
just a few random events,
you don't get something
that really looks like that.
But if you do more and more,
they add up to give you
this very characteristic pattern
which Gauss famously
wrote down mathematically.
It turns out that in most cases

English: 
a series of random events
gives you this bell-shaped curve.
It doesn't really matter what it is.
For example, if I were going to go out
and have a million scales across Australia
measure my weight.
Well, there's some randomness to that,
and you'll get a bell-shaped curve
of what my weight actually is.
If I were instead to go through
and ask a million Australian males
what their weight is,
and actually measure it,
I would also get a bell-shaped curve,
because that is also made up
of a series of random events
which determine people's weight.
So the way a bell-shaped curve
is characterized
is by its mean -
that's the most likely value -
and its width, which we call
a standard deviation.
This is a very important concept
because the width
and how close you are to the mean
you can characterize,
so the likelihood of things is occurring.

Chinese: 
一系列随机事件都会形成钟形曲线。
无论是什么样的事件。
例如，如果我到-
遍布于澳大利亚各地的100万个磅秤上，
去测量我的体重。
这的确有一些随机因素，
得到的体重数据就是一个钟形曲线。
如果我用另一个方式，
询问100万名澳大利亚男性他们的体重数字，
或者实际去测量他们的体重，
我也会得到一个钟形曲线。
因为是一系列随机事件-
决定了人们的体重。
所以钟形曲线重要的特点-
就是它的中间值----
也就是最可能的值。
曲线的宽度，
我们称之为“标准偏差”。
这是一个非常重要的概念，
因为了解宽度和到中间值的距离，
人们就可以描述-
事件发生的概率。

English: 
So it turns out if you are within
one standard deviation,
that happens 68.3% of the time.
I'm going to illustrate how this works
for work example in just a second.
If you have two standard deviations,
that happens 95.4% of the time;
you're within two.
99.73% within three standard deviations.
This is a very powerful way for us
to describe things in the world.
So, it turns out this means
that I can go out
and make a measurement of, for example,
how much I weigh,
and if I use more and more
scales in Australia,
I will get a better and better answer,
provided they are good scales.
It turns out the more trials I do,
or the more measurements I make,
the better I will make that measurement.
And the accuracy increases
as the square root of the number
of times I make the measurement.
That's why I am having these guys 
do what they are doing

Chinese: 
那么如果你位于一个标准偏差范围内，
发生的几率在68.3％。
我等一下会说在实际生活用的应用。
如果你在两个偏差范围内
发生的几率是95.4％。
这是两个标准偏差。
99.73％是三个标准偏差。
这是我们用来描述世间万物
的一个非常有力的方法。
所以，这意味着我可以-
去做一些测量，
比如称量自己的体重，
我在澳大利亚使用的磅秤越多，
得到的答案就越理想，
前提是这都是好的磅秤。
也就是说，我做的测试越多，
或者我的测量规模越大，
我的测量结果就越好。
测量结果的精确度-
与测量次数的平方根成比例上升。
这也是为什么我请这两个人帮忙的原因，

Chinese: 
越快越好。
（笑声）
那么，让我们把这个原理套用
到现实世界中的问题上：
澳大利亚总理-
在过去15个月里的支持率，
每隔几个星期，
我们都会听到民意调查机构，
问澳大利亚居民
“你支持总理吗？”
过去15个月里，
他们做了28次调查，
询问过1100人。
他们没有调查全部澳大利亚2200万人口，
因为这样做的费用太昂贵了。
他们只询问了1100人，
1100的平方根是33，
所以这些人回答的准确性-
在上下33人的范围浮动，
如果他们调查的样本是1100人。
也就是3％的错误率。
1100除以33的结果。
让我们来看看他们得到的结果吧。
这是过去15个月的数据，
你可以看到，似乎在去年年中的时候，
总理的日子不大好过。

English: 
as fast as they can.
(Laughter)
So let's apply this
to a real world problem we all see:
the approval rating
of the Prime Minister of Australia.
Over the past 15 months,
every couple of weeks, we hear news poll
go out and ask the people of Australia:
"Do you approve of the Prime Minister?"
Over the last 15 months,
they have done this 28 times,
and they asked 1100 people.
They don't ask
about 22 million Australians
because it's too expensive to do that,
so they ask 1100 people.
The square root of 1100 is 33,
and so it turns out
their answers are uncertain
by plus or minus 33 people
when they asked these 1100 people.
That's a 3% error.
That's 33 divided by 1100.
So let's see what they get.
Here is last fifteen months.
You can see it seems that some time
in the middle of the last year
the Prime Minister had a very bad week,

Chinese: 
接下来的几个星期，
看起来又是一个非常好的星期。
当然你也可以从另一个角度来看。
你可以说：“如果总理的支持率-
在过去15个月里根本没有变化会怎样？”
这里有一个平均值，
在这一系列的调查中，
平均值是29.6％。
所以在过去15个月里，
她一直不怎么受欢迎。
我们知道，一个常规的钟形曲线，
的一个标准偏差是68.3%，
其准确率在上下3％的范围浮动，
取决于我们访问的样本数量。
那么我们可以看到的偏离准确值
的数量是15和23。
这样才可以在3％的范围之内。。
实际的数字是24。
对于那些极端的情况，
也就是她看起来非常糟糕，
或者非常好的星期，情况如何？

English: 
followed a few weeks later
by what appears to be a very good week.
Of course, you could look at it
in another way.
You could say, "What would happened
if the Prime Minister's popularity
hasn't changed at all
in the last fifteen months?"
Well, then there's an average,
and that mean turns out
to be 29.6% for this set of polls.
So she hasn't been very popular
over the last 15 months.
And we know that, if a basis bell curve,
that's 68.3% of the time,
it should lie within plus or minus 3%,
because of the number
of people we're asking.
So that means we expect it turns out
between 15 and 23 of the time.
So it should lie within plus or minus 3%.
And the actual number of times is 24.
What about those really extreme cases
when she seems to have
a really bad or really good week?

Chinese: 
实际上只可能出现零次或者两次，
也就是5％的几率，
超过平均值6％的差异。
我们看到了几次？
两次。
换句话说，在过去的15个月里，
民意调查的结果相当稳定，
总理的支持率没有任何变化。
下面，我们来看看新闻有哪些内容。
例如，就在上个星期，
有关支持率，
《澳大利亚人》的头条消息，
从29％下降到27％，
尽管对于单一的民意调查来说
误差率至少有3％。
不仅仅《澳大利亚人》犯了错误；
所有的新闻媒体都存在类似的问题。
另外一个问题是，
新闻媒体并不是唯一做民意调查的人。
例如，尼尔森也为费法斯传媒做民意调查，
这是他们的调查结果。
同样的问题，
你可以看到
他们的结果似乎也具有连贯性，

English: 
Well, you actually expect
zero to two times,
so 5% of the time,
to be more than 6% discrepant
from the mean.
And what do we see?
Two.
In other words, over the last 15 months
the polls are completely consistent
with the Prime Minister's popularity
not changing a bit.
Alright. And let's see what the news is.
For example, just last week.
Well, approval rating,
big headline in the Australians,
dropped from 29 to 27%,
even though the error on that
is at least 3% even for that single poll.
It's not just Australia that does this;
it's all the news agencies.
Now, the other thing is that
news polls are not the only people
who do this.
For example, Nielsen
does this for Fairfax,
and here are their polls.
Same question,
and you'll see that it seems
that they are also consistent

Chinese: 
总理的支持率基本没有变化。
但是他们的结论不同。
这段时期里的支持率是36.5％。
当我们比较两者的时候
这并不是1000个人的调查结果，
而是30,000人。
我们汇总了所有这些人的意见。
所以，结果准确率的误差不超过1％，
但是两者相差了6％。
这是因为并非
所有的不确定性都是随机的。
有可能是过失和错误造成的。
我们很难逐一询问所有参加
民意调查的1100名澳大利亚人，
谁最能代表普通的澳大利亚民众。
所以这里又多了一层不确定性
也就是所谓的错误。
造成了我们所看到的科学上
或者统计学上的误差。
你或许会问，
“他们为什么不询问更多的人
比如10,000人
并降低频率，比如一个月一次？”

English: 
with the Prime Minister's popularity 
not changing over time.
But they seem to get a different answer.
They get 36.5% approval over that period.
We are not talking about 1,000 people here
when we compare these two things.
We're talking about 30,000,
because we get to add up all those people.
So, the uncertainty in these measurement
is well less than 1%,
and yet they disagree by 6%.
That's because
not all uncertainty is random.
It can be done to just make
mistakes or errors.
It turns out it really hard to ask
1,100 people across Australia
who are representative
of the average Australian.
So, there is an additional uncertainty
caused by just error
that is making a scientific
or a polling error which we see here.
You might ask yourself,
"Why don't they just ask more people,
like 10,000 people,
less frequently, once a month?"

Chinese: 
爱嘲讽的人或许会说：
因为每个月都向人们
汇报一模一样的支持率，
就算不上新闻了。
（笑声）
好了。
但是，并非所有的事情
采集更多的样本，
结果就更精确。
对于这种体系，我们称之为
展现出混沌的状态。
我这里恰好有一个能够
表现出混沌状态的东西，
是一个双摆。
这个双摆-
是国家科技馆的
同僚帮我做出来的，
我非常感谢。
双摆，
就是两个摆头连在一起的装置。
其美妙之处在于
它并不总是表现出混沌的状态。
让我们来看看其运行情况。
我这样开始，
它们来回摆动，步调一致。
这里并没有出现混沌状态。
如果我开始测量，
非常详细的测量，
我可以精确预测其摆动的状态。

English: 
And a cynic might say
because there’s no news in telling people
that the popularity is the same
month after month after month.
(Laughter)
Alright.
Not all things, though,
become more accurate
the more you measure them,
and such systems we call
as exhibiting chaotic behavior.
I happen to have something
that exhibits chaotic behavior here,
which is a double pendulum.
A double pendulum -
this was made up for the people
by me at Questacon,
and I thank them for that.
A double pendulum is just two pendulums
connected to each other.
And the beautiful thing is
this doesn't always exhibit chaos.
Let me show you what happens here.
If I just start this thing,
these things swing
back and forth in unison
because there is no chaos here.
If I make measurements,
better and better measurements,
I can predict exactly
what is going on here.

Chinese: 
测量得越详细，预测得越准确。
也就是双摆未来的运行状态。
但是如果我用
更大的幅度摆动双摆，
就会出现一些异常的现象，
它们的运行状态不再一致，
我对此束手无策。
无论我做多么详细的测量，
我也无法预测
双摆未来的运行状态。
因为存在无限的差异因素
会导致不同的结果，
这也并不是一件
完全无意义的事情。
我们也了解到一些事情。
例如，
我知道通过测量，
双摆旋转360°
发生的几率有多大，
其发生的频率如何。
所以你们了解了所谓的混沌状态，
但是你无法精确地预测。
那么，我们日常接触到的
混沌状态都有哪些？
地球的气候就是
一个很好的混沌状态实例。

English: 
The better I do, the better I will know
what pendulum is going to be
in the future.
But if I take a double pendulum
and I swing it a lot,
then something else happens.
They don't do the same thing,
and there is nothing I can do,
no matter how many measurements I make,
that I can predict what is going to happen
with these pendulums,
because infinite testable differences
lead to different outcomes.
Not is all lost here.
It turns out there are things
we can learn.
For example, I can know
through my measurements,
what the likelihood of the things
swinging all the way around is,
how often that's going to happen.
So, you can know things
about chaotic systems,
but you cannot predict exactly
what they're going to do.
Alright, so what is a chaotic system
that we are used to?
Well, it turns out the Earth's climate
is a good example of a chaotic system.

English: 
I show you here the temperature record
from Antarctic ice cores
over the last 650,000 years.
You can see in grey regions times
when the Earth is quite warm,
and then it seemingly cools down.
And why does it do that?
It's a chaotic process that is related
to how the Earth goes around the Sun
in a quite complex way.
So it's very difficult to predict exactly
what the Earth is going to do
at any given time.
Also, it's just hard to measure
what's going on with the Earth.
For the last thousand years,
here are temperature reconstructions
from different groups.
You can see over the last thousand years,
we get quite different answers
back in time.
We more or less agree
where we have better information,
which is in the last hundred years or so,
that the Earth is warmed up
about 8/10 of a degree.
So, modeling and measuring
the climate is hard.

Chinese: 
这是南极洲冰层的温度记录，
在过去650,000年里。
你可以看到在灰色区域
地球相当温暖，
之后似乎冷却下来。
为什么会发生这种现象？
这是一个混沌的过程，
与地球围绕太阳运行有关。
是一个非常复杂的过程。
所以我们很难预测地球未来-
某一个时间点的状态。
而且，我们很难
测量地球目前的情况，
在过去一千年里，
不同的团体重新构建了气温的走势。
你可以看到在过去一千年里，
我们得到的信息有非常大的差异。
我们或许认同
在哪段时间拥有较准确的信息，
即在过去一百年左右的时间里，
地球的气温上升了8到10°。
所以，测量气候，
为其建模是相当困难的。

Chinese: 
对于使用这些数据的一致观点是：
我们可以90％地确定
气候变暖不是偶然事件，
而是来自于-
人类的活动，
以及人类产生的二氧化碳。
从科学家的角度来说
如果要做测试，
90％的准确性并不足够。
我们对结果并不是很肯定。
但是，如果有人
想预测我未来的生活，
90％已经足够我赌一把了。
所以两者的概念完全不同。
但是从我作为一个科学家的观点，
我可以99.99999％地肯定，
物理学告诉我们
在大气中增加二氧化碳-
导致更多阳光被
困在大气层中-
从而导致气温升高。
困难之处在于-
也是我们不太确定的问题-
就是会有多少云量，
会蒸发掉多少水，
进而提高地表的温度，

English: 
The consensus view of just using the data
is that we are 90% sure
that the warming trend is not an accident,
that it is actually caused
by anthropogenic
or man-made carbon dioxide.
As a scientist trying
to make an experiment,
90% isn't a very good result.
You're not very sure about it.
However, if someone's trying to figure out
my future of my life,
90% is a pretty big risk factor.
So, that's a very different thing
between those two things.
But from my point as a scientist,
I am 99.99999% sure
that physics tells us
that adding CO2 to the atmosphere
causes sunlight to be more effectively
trapped in our atmosphere,
raising the temperature a bit.
The hard part is -
and what we are much less sure of -
is how many clouds there are going to be,
how much water vapor will be released,
which warms the Earth up even more,

Chinese: 
接下来会释放出多少甲烷，
以及海洋会具体起到什么作用-
来网罗二氧化碳，保持温度。
当然，我们不知道未来
二氧化碳的具体释放量。
所以只好尽可能准确地预测。
红线表示未来的状态，
如果我们不采取任何措施
限制未来二氧化碳的释放量。
我们的燃烧量就会越来越多，
随着我们生活的世界越来越发达。
蓝线表示采取非常严格的
由政府间气候变化专门委员会倡议的
碳减排策略。
接下来我们就可以
使用物理学进行预测-
未来会发生什么变化。
这是两个选择的不同结果。
蓝色曲线表示大幅减排后的结果。
气温在下一个世纪还会继续升高，
不超过2摄氏度，
其准确性为90％。

English: 
how many methane releases will follow,
and precisely how the oceans
will interact with all this
to trap the CO2 and hold the warmth.
Of course, we have no idea really
how much CO2 we will emit into the future.
So here is our best estimate.
The red curve shows
what we think will happen
if we don't do anything
about our CO2 emission into the future.
We're going to burn more and more
as we become
more and more developed as a world.
The blue line shows a very aggressive
carbon reduction strategy
proposed by the IPCC.
And then we can estimate
using our best physics
of what we think is going to happen.
Here is the outcome of the two ideas.
The blue curve shows what happens
if we do that very aggressive drop.
It keeps the rise of temperature
over the next century
to less than 2 degrees C
with about 90% confidence.

English: 
On the other hand,
if we let things keep going,
the best prediction is, of course,
that it's going to get warmer and warmer,
with a great deal of uncertainty
of about exactly how warm we'll go.
According to the Australian
Academy of Science
they say, "Expect climate surprises,"
and we should,
because the Earth's climate
is a chaotic system.
We don't exactly know
what it's going to do,
and that is what scares
the hell out of me.
So, life is not black and white.
Life is really shades of grey.
But it's not all bad.
You guys have done an excellent job,
so what I want you to do now is to stop,
and we are going to read out
your numbers here,
and we're going to compare them
to what I thought
which we were going to predict, okay?
So I have here hopefully
a functioning computer.
So what I need you to do
is to just go through from the left

Chinese: 
另一方面，
如果我们任其发展，
比较可靠的预测是，
当然，气温会不断升高，
具体气温会达到多高
我们并不是很确定。
根据澳大利亚科学院的说法，
他们说：“准备大吃一惊吧。”
的确如此。
因为地球的气候是一个混沌体系。
我们不知道该做些什么，
这才是让我深感恐惧的问题。
所以，生活并不是非黑即白。
生活全是灰色区域，
但这也并非坏事。
你们做得非常好，
现在可以停下来，
我们要记录下你们所得到的数字，
并进行比较。
与我的预测进行比较，好吗？
我这里有一台电脑，
希望它能正常运行。
我需要你们从左边开始，

English: 
and read out the numbers
that you have achieved.
Assistant: 5. Brian Schmidt: 5.
A: 10. BS: 10.
A: 21. BS:21.
A: 21. BS:21 again? A: That's right. 24.
BS: 24? A: Yes. Then 30. BS: 30.
A: 37. BS: 37.
A: 47. BS: 47.
A: 41. BS: 41.
A: 43. BS: 43.
A: 29. BS: 29.
A: 21. BS: 21.
A: 8. BS: 8.
A:10. BS: 10.
A: 3. BS: 3.
Well, I am proud to say
you guys were completely random.
It was perfect.
(Laughter)
I show here the prediction
of what should happen and what happened.
Bang on.
(Applause)
There is certainty in uncertainty,
(Laughter)
and that is the beauty of it.
But to make policy decisions
based on what we know about science,

Chinese: 
读出你们得到的数字。
5
10
21
又是21？24
24？30
37
47
41
43
29
21
8
10
3
我很自豪地宣布
这完全是随机的数字。
很完美。
（笑声）
我这里展示的
是对未来发生事件的预测，
以及已经发生事件的记录。
丝毫不差。
（掌声）
不确定中包含着确定。
（笑声）
这就是精彩之处。
但是在我们对科学
和对经济了解的基础上-

Chinese: 
制定政策-
需要我们的政客、
政策制定者和公民-
了解不确定性。
让我用理查德·费曼的话
作为演讲的结束语，
这也是我自己想说的话，
“我可以接受质疑和不确定性
我觉得更有趣的事情是无知，
而不是知道一个
或许是错误的答案。”
非常感谢。
（掌声）
谢谢，非常好。
（掌声）

English: 
about what we know about economics,
requires our politicians,
our policy makers, and our citizens
to understand uncertainty.
I'm going to finish with the words
of Richard Feynman,
with words that really could be my own,
which is, "I can live
with doubt and uncertainty.
I think it's much more interesting
to live not knowing
than to have answers
which might be wrong."
Thank you very much.
(Applause)
Thank you. Excellent.
(Applause)
