0:00:11.495,0:00:13.095
Last December,

0:00:13.095,0:00:16.245
me and my fellow Nobel Laureates[br]were asked by a journalist

0:00:16.245,0:00:19.825
if there was one thing[br]that we could teach the world,

0:00:19.825,0:00:21.185
what would it be?

0:00:21.185,0:00:22.135
And to my surprise,

0:00:22.135,0:00:27.645
two economists, two biologists,[br]a chemist, and three physicists

0:00:27.645,0:00:29.535
gave the same answer.

0:00:29.535,0:00:33.085
And that answer was about uncertainty.

0:00:33.085,0:00:36.395
So I'm going to talk to you today[br]about uncertainty.

0:00:37.382,0:00:42.762
To understand anything,[br]you must understand its uncertainty.

0:00:42.762,0:00:47.812
Uncertainty is at the heart[br]of the fabric of the Universe.

0:00:47.812,0:00:51.222
I'm going to illustrate this with a laser.

0:00:51.894,0:00:53.894
A laser puts out a small,

0:00:53.894,0:00:57.894
but not infinitesimally small[br]point of light.

0:00:58.312,0:01:00.992
You might think that if I go through

0:01:00.992,0:01:04.762
and I try to make[br]that point of light smaller

0:01:04.762,0:01:08.762
by, for example,[br]bringing two jars of a slit together,

0:01:08.762,0:01:11.122
that I could make that point[br]as small as I want.

0:01:11.122,0:01:15.332
I just want to make[br]those slits closer and closer.

0:01:15.712,0:01:18.672
So let's see what happens[br]when I do this for real.

0:01:20.122,0:01:22.952
My friends at Mount Stromlo gave a call

0:01:22.952,0:01:26.952
and made up a nice little invent,[br]a little here.

0:01:28.084,0:01:33.234
By essentially adjusting[br]the laser, the slit -

0:01:33.234,0:01:36.174
we're going to go through[br]and we are going to see what happens

0:01:36.174,0:01:38.274
when I close the jaws of the slit.

0:01:38.765,0:01:40.345
The more I close it,

0:01:41.885,0:01:45.885
instead of getting smaller,[br]the laser gets spread out.

0:01:47.487,0:01:51.487
So it works exactly the opposite[br]of what I was expecting.

0:01:52.247,0:01:56.247
And that's due to something known[br]as Heisenberg's Uncertainty Principle.

0:01:57.275,0:01:59.285
Heisenberg's Uncertainty Principle states

0:01:59.285,0:02:03.285
that you can't know exactly[br]where something is

0:02:03.285,0:02:06.645
and know its momentum at the same time.

0:02:06.645,0:02:10.285
Light's momentum is really its direction.

0:02:10.741,0:02:15.551
So, as I bring those slits[br]closer and closer together,

0:02:16.263,0:02:19.123
I actually constrain where the light is.

0:02:19.752,0:02:24.642
But the quantum world says[br]you can't do that.

0:02:24.642,0:02:26.802
The light then has an uncertain direction.

0:02:26.802,0:02:32.262
So instead of being a smaller point,[br]the light has a randomness put out to it,

0:02:32.262,0:02:34.762
which is that pattern that we saw.

0:02:37.230,0:02:42.340
Many things in life you can think of[br]as a series of little decisions.

0:02:42.340,0:02:46.662
For example, if I start at a point,[br]and I can go left or right,

0:02:46.662,0:02:50.782
well, I do it, let's say, 50% of the time[br]I can go left or right.

0:02:51.369,0:02:55.519
Let's say I have another decision tree[br]down below that.

0:02:55.519,0:03:00.619
I can go left, I can go right,[br]or I can go to the middle.

0:03:00.619,0:03:04.229
Because I've had two chances[br]to go to the middle from above,

0:03:04.229,0:03:07.029
I would do that 50% of the time.

0:03:07.029,0:03:10.779
I only go one quarter to the left[br]and one quarter all the way to the right.

0:03:10.779,0:03:14.429
And you can build up such a decision tree,[br]and Pascal did this.

0:03:14.429,0:03:16.549
It's called Pascal's triangle.

0:03:16.549,0:03:20.549
You get a probability[br]of where you are going to end up.

0:03:20.549,0:03:23.369
I brought something like this[br]with me today.

0:03:24.698,0:03:27.668
It's this machine right here.

0:03:28.759,0:03:33.079
This is a machine you can put balls into[br]and you can randomly see what happens.

0:03:33.079,0:03:36.469
So, for example, if I put a ball in here,

0:03:36.469,0:03:40.039
it'll bounce down[br]and it'll end up somewhere.

0:03:40.039,0:03:43.469
It's essentially an enactment[br]of Pascal's triangle.

0:03:43.469,0:03:45.719
I need two people[br]from the audience to help me,

0:03:45.719,0:03:49.169
and I think I am going to have[br]Sly and Jon right there

0:03:49.169,0:03:51.079
come up and help me if that's okay.

0:03:51.079,0:03:52.299
You know who you are.

0:03:52.299,0:03:53.949
(Laughter)

0:03:53.949,0:03:55.359
What they are going to do

0:03:55.359,0:03:58.089
is they are going to,[br]as fast as they can -

0:03:58.089,0:04:01.689
faster than they are going right now,[br]because I only have 18 minutes -

0:04:01.689,0:04:02.559
(Laughter)

0:04:02.559,0:04:06.689
put balls through this machine,[br]and we're going to see what happens.

0:04:07.507,0:04:09.747
This machine counts things where they end.

0:04:09.747,0:04:13.237
So you guys have to go through[br]as fast as you can.

0:04:13.237,0:04:14.517
Work together,

0:04:14.517,0:04:17.567
and during the rest of my talk,[br]you are going to build up this.

0:04:17.567,0:04:20.727
And the more you do,[br]the better it is, okay?

0:04:20.727,0:04:24.357
So go for it, and I'll keep on going.

0:04:24.357,0:04:25.127
(Laughter)

0:04:25.127,0:04:25.887
Alright.

0:04:26.307,0:04:30.307
It turns out that if you have[br]a series of random events in life,

0:04:30.307,0:04:33.667
you end up with something[br]called a Bell Shaped Curve,

0:04:33.667,0:04:38.157
which we also call a Normal Distribution[br]or Gaussian Distribution.

0:04:39.027,0:04:42.197
So, for example, if you have[br]just a few random events,

0:04:42.197,0:04:44.677
you don't get something[br]that really looks like that.

0:04:44.677,0:04:46.027
But if you do more and more,

0:04:46.027,0:04:49.707
they add up to give you[br]this very characteristic pattern

0:04:49.707,0:04:53.677
which Gauss famously[br]wrote down mathematically.

0:04:53.677,0:04:55.447
It turns out that in most cases

0:04:55.447,0:05:00.607
a series of random events[br]gives you this bell-shaped curve.

0:05:02.259,0:05:04.059
It doesn't really matter what it is.

0:05:04.059,0:05:05.879
For example, if I were going to go out

0:05:05.879,0:05:10.469
and have a million scales across Australia

0:05:10.469,0:05:12.649
measure my weight.

0:05:12.649,0:05:14.469
Well, there's some randomness to that,

0:05:14.469,0:05:19.119
and you'll get a bell-shaped curve[br]of what my weight actually is.

0:05:19.747,0:05:22.267
If I were instead to go through

0:05:22.267,0:05:25.287
and ask a million Australian males[br]what their weight is,

0:05:25.287,0:05:26.467
and actually measure it,

0:05:26.467,0:05:29.477
I would also get a bell-shaped curve,

0:05:29.477,0:05:32.187
because that is also made up[br]of a series of random events

0:05:32.187,0:05:34.207
which determine people's weight.

0:05:34.914,0:05:38.314
So the way a bell-shaped curve[br]is characterized

0:05:38.314,0:05:42.004
is by its mean -[br]that's the most likely value -

0:05:42.004,0:05:46.474
and its width, which we call[br]a standard deviation.

0:05:47.121,0:05:49.311
This is a very important concept

0:05:49.311,0:05:53.071
because the width[br]and how close you are to the mean

0:05:53.071,0:05:54.361
you can characterize,

0:05:54.361,0:05:57.201
so the likelihood of things is occurring.

0:05:57.795,0:06:01.905
So it turns out if you are within[br]one standard deviation,

0:06:01.905,0:06:06.045
that happens 68.3% of the time.

0:06:06.045,0:06:10.002
I'm going to illustrate how this works[br]for work example in just a second.

0:06:10.729,0:06:14.519
If you have two standard deviations,[br]that happens 95.4% of the time;

0:06:14.519,0:06:15.779
you're within two.

0:06:15.779,0:06:19.759
99.73% within three standard deviations.

0:06:19.759,0:06:24.829
This is a very powerful way for us[br]to describe things in the world.

0:06:24.829,0:06:28.769
So, it turns out this means[br]that I can go out

0:06:28.769,0:06:31.569
and make a measurement of, for example,

0:06:31.569,0:06:33.249
how much I weigh,

0:06:33.249,0:06:36.319
and if I use more and more[br]scales in Australia,

0:06:36.319,0:06:39.199
I will get a better and better answer,

0:06:39.199,0:06:41.179
provided they are good scales.

0:06:41.731,0:06:46.531
It turns out the more trials I do,[br]or the more measurements I make,

0:06:46.531,0:06:49.421
the better I will make that measurement.

0:06:49.421,0:06:51.661
And the accuracy increases

0:06:51.661,0:06:56.601
as the square root of the number[br]of times I make the measurement.

0:06:56.601,0:06:59.281
That's why I am having these guys [br]do what they are doing

0:06:59.281,0:07:00.321
as fast as they can.

0:07:00.321,0:07:01.141
(Laughter)

0:07:01.141,0:07:04.851
So let's apply this[br]to a real world problem we all see:

0:07:04.851,0:07:08.061
the approval rating[br]of the Prime Minister of Australia.

0:07:08.739,0:07:10.629
Over the past 15 months,

0:07:10.629,0:07:13.859
every couple of weeks, we hear news poll

0:07:13.859,0:07:19.269
go out and ask the people of Australia:[br]"Do you approve of the Prime Minister?"

0:07:19.269,0:07:20.427
Over the last 15 months,

0:07:20.427,0:07:24.097
they have done this 28 times,[br]and they asked 1100 people.

0:07:24.097,0:07:26.887
They don't ask[br]about 22 million Australians

0:07:26.887,0:07:28.707
because it's too expensive to do that,

0:07:28.707,0:07:30.557
so they ask 1100 people.

0:07:30.557,0:07:33.087
The square root of 1100 is 33,

0:07:33.087,0:07:36.317
and so it turns out[br]their answers are uncertain

0:07:36.317,0:07:40.537
by plus or minus 33 people[br]when they asked these 1100 people.

0:07:40.537,0:07:44.937
That's a 3% error.[br]That's 33 divided by 1100.

0:07:44.937,0:07:46.930
So let's see what they get.

0:07:46.930,0:07:49.290
Here is last fifteen months.

0:07:49.290,0:07:52.870
You can see it seems that some time[br]in the middle of the last year

0:07:52.870,0:07:55.820
the Prime Minister had a very bad week,

0:07:55.820,0:08:00.250
followed a few weeks later[br]by what appears to be a very good week.

0:08:00.968,0:08:04.568
Of course, you could look at it[br]in another way.

0:08:04.568,0:08:08.028
You could say, "What would happened[br]if the Prime Minister's popularity

0:08:08.028,0:08:12.908
hasn't changed at all[br]in the last fifteen months?"

0:08:12.908,0:08:14.608
Well, then there's an average,

0:08:14.608,0:08:19.548
and that mean turns out[br]to be 29.6% for this set of polls.

0:08:19.548,0:08:23.338
So she hasn't been very popular[br]over the last 15 months.

0:08:23.338,0:08:27.599
And we know that, if a basis bell curve,

0:08:27.599,0:08:31.039
that's 68.3% of the time,

0:08:31.039,0:08:34.489
it should lie within plus or minus 3%,

0:08:34.489,0:08:38.489
because of the number[br]of people we're asking.

0:08:38.489,0:08:43.639
So that means we expect it turns out[br]between 15 and 23 of the time.

0:08:43.639,0:08:46.809
So it should lie within plus or minus 3%.

0:08:46.809,0:08:49.899
And the actual number of times is 24.

0:08:51.064,0:08:52.874
What about those really extreme cases

0:08:52.874,0:08:56.764
when she seems to have[br]a really bad or really good week?

0:08:56.764,0:09:01.604
Well, you actually expect[br]zero to two times,

0:09:01.604,0:09:03.274
so 5% of the time,

0:09:03.274,0:09:06.814
to be more than 6% discrepant[br]from the mean.

0:09:06.814,0:09:08.114
And what do we see?

0:09:08.754,0:09:09.543
Two.

0:09:09.876,0:09:12.576
In other words, over the last 15 months

0:09:12.576,0:09:15.986
the polls are completely consistent

0:09:15.986,0:09:20.236
with the Prime Minister's popularity[br]not changing a bit.

0:09:21.763,0:09:25.273
Alright. And let's see what the news is.

0:09:25.273,0:09:27.393
For example, just last week.

0:09:27.393,0:09:31.203
Well, approval rating,[br]big headline in the Australians,

0:09:31.203,0:09:33.763
dropped from 29 to 27%,

0:09:33.763,0:09:38.823
even though the error on that[br]is at least 3% even for that single poll.

0:09:38.823,0:09:40.733
It's not just Australia that does this;

0:09:40.733,0:09:43.403
it's all the news agencies.

0:09:44.756,0:09:46.546
Now, the other thing is that

0:09:46.546,0:09:48.996
news polls are not the only people[br]who do this.

0:09:48.996,0:09:51.856
For example, Nielsen[br]does this for Fairfax,

0:09:51.856,0:09:53.876
and here are their polls.

0:09:53.876,0:09:55.616
Same question,

0:09:55.616,0:09:59.016
and you'll see that it seems[br]that they are also consistent

0:09:59.016,0:10:03.286
with the Prime Minister's popularity [br]not changing over time.

0:10:03.286,0:10:05.216
But they seem to get a different answer.

0:10:05.216,0:10:09.216
They get 36.5% approval over that period.

0:10:09.778,0:10:12.788
We are not talking about 1,000 people here

0:10:12.788,0:10:14.948
when we compare these two things.

0:10:14.948,0:10:16.778
We're talking about 30,000,

0:10:16.778,0:10:19.248
because we get to add up all those people.

0:10:19.248,0:10:23.248
So, the uncertainty in these measurement[br]is well less than 1%,

0:10:23.248,0:10:26.328
and yet they disagree by 6%.

0:10:26.328,0:10:29.438
That's because[br]not all uncertainty is random.

0:10:29.438,0:10:34.208
It can be done to just make[br]mistakes or errors.

0:10:34.208,0:10:39.078
It turns out it really hard to ask[br]1,100 people across Australia

0:10:39.078,0:10:42.818
who are representative[br]of the average Australian.

0:10:42.818,0:10:46.728
So, there is an additional uncertainty[br]caused by just error

0:10:46.728,0:10:51.788
that is making a scientific[br]or a polling error which we see here.

0:10:51.788,0:10:53.778
You might ask yourself,

0:10:53.778,0:10:57.278
"Why don't they just ask more people,[br]like 10,000 people,

0:10:57.278,0:10:59.958
less frequently, once a month?"

0:10:59.958,0:11:00.968
And a cynic might say

0:11:00.968,0:11:06.328
because there’s no news in telling people[br]that the popularity is the same

0:11:06.328,0:11:07.958
month after month after month.

0:11:07.958,0:11:10.038
(Laughter)

0:11:10.038,0:11:10.988
Alright.

0:11:10.988,0:11:13.978
Not all things, though,[br]become more accurate

0:11:13.978,0:11:15.548
the more you measure them,

0:11:15.548,0:11:20.268
and such systems we call[br]as exhibiting chaotic behavior.

0:11:20.268,0:11:24.518
I happen to have something[br]that exhibits chaotic behavior here,

0:11:24.518,0:11:27.528
which is a double pendulum.

0:11:27.528,0:11:28.708
A double pendulum -

0:11:28.708,0:11:32.928
this was made up for the people[br]by me at Questacon,

0:11:32.928,0:11:34.808
and I thank them for that.

0:11:35.492,0:11:38.732
A double pendulum is just two pendulums[br]connected to each other.

0:11:38.732,0:11:42.522
And the beautiful thing is[br]this doesn't always exhibit chaos.

0:11:42.522,0:11:44.162
Let me show you what happens here.

0:11:44.162,0:11:45.562
If I just start this thing,

0:11:45.562,0:11:49.482
these things swing[br]back and forth in unison

0:11:49.482,0:11:51.602
because there is no chaos here.

0:11:51.602,0:11:54.612
If I make measurements,[br]better and better measurements,

0:11:54.612,0:11:58.742
I can predict exactly[br]what is going on here.

0:11:58.742,0:12:00.952
The better I do, the better I will know

0:12:00.952,0:12:03.682
what pendulum is going to be[br]in the future.

0:12:03.682,0:12:08.270
But if I take a double pendulum[br]and I swing it a lot,

0:12:08.270,0:12:10.510
then something else happens.

0:12:10.511,0:12:12.811
They don't do the same thing,

0:12:12.811,0:12:15.071
and there is nothing I can do,

0:12:15.071,0:12:17.381
no matter how many measurements I make,

0:12:17.381,0:12:22.391
that I can predict what is going to happen[br]with these pendulums,

0:12:22.391,0:12:27.651
because infinite testable differences[br]lead to different outcomes.

0:12:27.906,0:12:29.556
Not is all lost here.

0:12:29.556,0:12:32.126
It turns out there are things[br]we can learn.

0:12:32.126,0:12:36.526
For example, I can know[br]through my measurements,

0:12:36.526,0:12:40.526
what the likelihood of the things[br]swinging all the way around is,

0:12:40.526,0:12:42.546
how often that's going to happen.

0:12:42.546,0:12:45.375
So, you can know things[br]about chaotic systems,

0:12:45.375,0:12:49.045
but you cannot predict exactly[br]what they're going to do.

0:12:49.950,0:12:53.110
Alright, so what is a chaotic system[br]that we are used to?

0:12:53.110,0:12:59.120
Well, it turns out the Earth's climate[br]is a good example of a chaotic system.

0:12:59.120,0:13:03.460
I show you here the temperature record[br]from Antarctic ice cores

0:13:03.460,0:13:06.300
over the last 650,000 years.

0:13:06.300,0:13:10.300
You can see in grey regions times[br]when the Earth is quite warm,

0:13:10.300,0:13:13.390
and then it seemingly cools down.

0:13:13.390,0:13:14.540
And why does it do that?

0:13:14.540,0:13:20.630
It's a chaotic process that is related[br]to how the Earth goes around the Sun

0:13:20.630,0:13:23.510
in a quite complex way.

0:13:23.510,0:13:27.680
So it's very difficult to predict exactly[br]what the Earth is going to do

0:13:27.680,0:13:29.460
at any given time.

0:13:30.533,0:13:34.213
Also, it's just hard to measure[br]what's going on with the Earth.

0:13:34.213,0:13:35.563
For the last thousand years,

0:13:35.563,0:13:38.953
here are temperature reconstructions[br]from different groups.

0:13:38.953,0:13:41.303
You can see over the last thousand years,

0:13:41.303,0:13:45.383
we get quite different answers[br]back in time.

0:13:45.383,0:13:48.003
We more or less agree[br]where we have better information,

0:13:48.003,0:13:51.013
which is in the last hundred years or so,

0:13:51.013,0:13:54.743
that the Earth is warmed up[br]about 8/10 of a degree.

0:13:55.772,0:13:58.302
So, modeling and measuring[br]the climate is hard.

0:13:58.926,0:14:01.646
The consensus view of just using the data

0:14:01.646,0:14:06.516
is that we are 90% sure[br]that the warming trend is not an accident,

0:14:06.516,0:14:08.199
that it is actually caused

0:14:08.199,0:14:12.909
by anthropogenic[br]or man-made carbon dioxide.

0:14:12.909,0:14:15.329
As a scientist trying[br]to make an experiment,

0:14:15.329,0:14:19.599
90% isn't a very good result.

0:14:19.599,0:14:21.319
You're not very sure about it.

0:14:21.319,0:14:24.909
However, if someone's trying to figure out[br]my future of my life,

0:14:24.909,0:14:27.479
90% is a pretty big risk factor.

0:14:27.479,0:14:31.429
So, that's a very different thing[br]between those two things.

0:14:31.429,0:14:33.401
But from my point as a scientist,

0:14:33.401,0:14:37.491
I am 99.99999% sure

0:14:37.491,0:14:41.611
that physics tells us[br]that adding CO2 to the atmosphere

0:14:41.611,0:14:45.811
causes sunlight to be more effectively[br]trapped in our atmosphere,

0:14:45.811,0:14:48.021
raising the temperature a bit.

0:14:48.021,0:14:49.941
The hard part is -

0:14:49.941,0:14:51.761
and what we are much less sure of -

0:14:51.761,0:14:53.771
is how many clouds there are going to be,

0:14:53.771,0:14:55.611
how much water vapor will be released,

0:14:55.611,0:14:57.971
which warms the Earth up even more,

0:14:57.971,0:15:00.291
how many methane releases will follow,

0:15:00.291,0:15:03.841
and precisely how the oceans[br]will interact with all this

0:15:03.841,0:15:07.281
to trap the CO2 and hold the warmth.

0:15:07.281,0:15:12.811
Of course, we have no idea really[br]how much CO2 we will emit into the future.

0:15:14.064,0:15:16.064
So here is our best estimate.

0:15:16.514,0:15:19.144
The red curve shows[br]what we think will happen

0:15:19.144,0:15:23.684
if we don't do anything[br]about our CO2 emission into the future.

0:15:23.684,0:15:25.334
We're going to burn more and more

0:15:25.334,0:15:27.844
as we become[br]more and more developed as a world.

0:15:28.401,0:15:33.471
The blue line shows a very aggressive[br]carbon reduction strategy

0:15:33.471,0:15:35.961
proposed by the IPCC.

0:15:36.665,0:15:39.225
And then we can estimate[br]using our best physics

0:15:39.225,0:15:41.315
of what we think is going to happen.

0:15:41.315,0:15:43.785
Here is the outcome of the two ideas.

0:15:43.785,0:15:48.745
The blue curve shows what happens[br]if we do that very aggressive drop.

0:15:48.745,0:15:51.985
It keeps the rise of temperature[br]over the next century

0:15:51.985,0:15:56.915
to less than 2 degrees C[br]with about 90% confidence.

0:15:57.799,0:16:00.089
On the other hand,[br]if we let things keep going,

0:16:00.089,0:16:04.139
the best prediction is, of course,[br]that it's going to get warmer and warmer,

0:16:04.139,0:16:08.759
with a great deal of uncertainty[br]of about exactly how warm we'll go.

0:16:09.352,0:16:12.412
According to the Australian[br]Academy of Science

0:16:12.412,0:16:15.748
they say, "Expect climate surprises,"

0:16:15.748,0:16:16.628
and we should,

0:16:16.628,0:16:20.358
because the Earth's climate[br]is a chaotic system.

0:16:20.358,0:16:22.728
We don't exactly know[br]what it's going to do,

0:16:22.728,0:16:26.148
and that is what scares[br]the hell out of me.

0:16:27.253,0:16:30.103
So, life is not black and white.

0:16:30.679,0:16:33.689
Life is really shades of grey.

0:16:34.909,0:16:37.059
But it's not all bad.

0:16:37.059,0:16:39.359
You guys have done an excellent job,

0:16:39.359,0:16:41.379
so what I want you to do now is to stop,

0:16:41.379,0:16:43.669
and we are going to read out[br]your numbers here,

0:16:43.669,0:16:45.189
and we're going to compare them

0:16:45.189,0:16:48.759
to what I thought[br]which we were going to predict, okay?

0:16:48.759,0:16:53.519
So I have here hopefully[br]a functioning computer.

0:16:53.519,0:16:57.519
So what I need you to do[br]is to just go through from the left

0:16:57.519,0:17:00.169
and read out the numbers[br]that you have achieved.

0:17:00.834,0:17:02.354
Assistant: 5. Brian Schmidt: 5.

0:17:02.751,0:17:04.471
A: 10. BS: 10.

0:17:04.471,0:17:07.101
A: 21. BS:21.

0:17:07.101,0:17:12.011
A: 21. BS:21 again? A: That's right. 24.

0:17:12.011,0:17:15.481
BS: 24? A: Yes. Then 30. BS: 30.

0:17:15.481,0:17:17.491
A: 37. BS: 37.

0:17:17.491,0:17:19.431
A: 47. BS: 47.

0:17:19.431,0:17:21.161
A: 41. BS: 41.

0:17:21.161,0:17:22.921
A: 43. BS: 43.

0:17:22.921,0:17:25.291
A: 29. BS: 29.

0:17:25.291,0:17:27.281
A: 21. BS: 21.

0:17:27.281,0:17:29.141
A: 8. BS: 8.

0:17:29.141,0:17:31.011
A:10. BS: 10.

0:17:31.011,0:17:33.221
A: 3. BS: 3.

0:17:33.221,0:17:37.751
Well, I am proud to say[br]you guys were completely random.

0:17:37.751,0:17:38.661
It was perfect.

0:17:38.661,0:17:40.351
(Laughter)

0:17:40.351,0:17:45.231
I show here the prediction[br]of what should happen and what happened.

0:17:45.231,0:17:46.111
Bang on.

0:17:46.111,0:17:47.981
(Applause)

0:17:47.981,0:17:51.011
There is certainty in uncertainty,

0:17:51.011,0:17:52.081
(Laughter)

0:17:52.081,0:17:53.731
and that is the beauty of it.

0:17:53.731,0:17:59.881
But to make policy decisions[br]based on what we know about science,

0:17:59.881,0:18:01.861
about what we know about economics,

0:18:01.861,0:18:06.761
requires our politicians,[br]our policy makers, and our citizens

0:18:06.761,0:18:08.891
to understand uncertainty.

0:18:08.891,0:18:12.211
I'm going to finish with the words[br]of Richard Feynman,

0:18:12.211,0:18:14.651
with words that really could be my own,

0:18:14.651,0:18:17.911
which is, "I can live[br]with doubt and uncertainty.

0:18:17.911,0:18:21.181
I think it's much more interesting[br]to live not knowing

0:18:21.181,0:18:23.671
than to have answers[br]which might be wrong."

0:18:23.671,0:18:25.061
Thank you very much.

0:18:25.061,0:18:28.061
(Applause)

0:18:28.699,0:18:31.269
Thank you. Excellent.

0:18:31.269,0:18:33.299
(Applause)