
Browse content similar to Tails You Win: The Science of Chance. Check below for episodes and series from the same categories and more!
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All our lives, we are pulled about and pushed around | 0:00:02 | 0:00:05 | |
by the mysterious workings of chance. | 0:00:05 | 0:00:07 | |
When chance seems cruel, | 0:00:08 | 0:00:10 | |
some call it Fate. | 0:00:10 | 0:00:11 | |
And when chance is kind, | 0:00:13 | 0:00:15 | |
we might call it Luck. | 0:00:15 | 0:00:17 | |
Scoring a big win... | 0:00:17 | 0:00:18 | |
..being saved from disaster... | 0:00:18 | 0:00:22 | |
..or meeting that special someone. | 0:00:22 | 0:00:25 | |
But what actually is chance? | 0:00:25 | 0:00:28 | |
Is it something fundamental in the fabric of the universe? | 0:00:28 | 0:00:32 | |
Does chance have rules? | 0:00:32 | 0:00:34 | |
And does it really exist at all? | 0:00:34 | 0:00:36 | |
And if it does, | 0:00:37 | 0:00:38 | |
could we one day even overcome it? | 0:00:38 | 0:00:41 | |
This is the story of how we discovered how chance works... | 0:00:41 | 0:00:46 | |
..learnt to tame it... | 0:00:46 | 0:00:48 | |
..and even to work out the odds for the future. | 0:00:48 | 0:00:52 | |
How we tried, but so often failed, | 0:00:53 | 0:00:56 | |
to conquer it... | 0:00:56 | 0:00:57 | |
and may finally be learning to love it. | 0:00:57 | 0:01:00 | |
Chance plays its part in all our lives, | 0:01:21 | 0:01:24 | |
though mine perhaps more than most. | 0:01:24 | 0:01:28 | |
I'm a mathematician at Cambridge University | 0:01:28 | 0:01:30 | |
and trying to make sense of chance is my job. | 0:01:30 | 0:01:33 | |
I study how we can use the mathematics of chance | 0:01:34 | 0:01:37 | |
to calculate probabilities, | 0:01:37 | 0:01:39 | |
numbers that can give us a handle on what might happen in the future. | 0:01:39 | 0:01:43 | |
SPLASHING SOUND EFFECT | 0:01:51 | 0:01:54 | |
APPLAUSE SOUND EFFECT | 0:01:58 | 0:02:01 | |
GASPING SOUND EFFECT | 0:02:05 | 0:02:07 | |
Did you know that, on average, | 0:02:07 | 0:02:09 | |
each person in Britain has a one-in-a-million daily chance | 0:02:09 | 0:02:13 | |
of some kind of violent or accidental death? | 0:02:13 | 0:02:15 | |
To put it in perspective, 1 in a million | 0:02:17 | 0:02:19 | |
is roughly the chance of flipping heads 20 times. | 0:02:19 | 0:02:23 | |
Imagine it like this. | 0:02:23 | 0:02:25 | |
Flip a coin, | 0:02:25 | 0:02:26 | |
20 heads, you're dead. | 0:02:26 | 0:02:28 | |
Heads... | 0:02:28 | 0:02:30 | |
Heads. Oh, dear! | 0:02:30 | 0:02:34 | |
Heads... | 0:02:34 | 0:02:36 | |
Tails! Oh, phew! | 0:02:36 | 0:02:38 | |
It's easy to say that it's 50/50 for a coin to come up heads, | 0:02:40 | 0:02:44 | |
but we can even put a probability on things | 0:02:44 | 0:02:47 | |
that seem utterly chaotic and unpredictable. | 0:02:47 | 0:02:50 | |
San Francisco. | 0:02:52 | 0:02:55 | |
In October 1989, | 0:02:55 | 0:02:56 | |
a huge, magnitude 7 earthquake struck totally without warning. | 0:02:56 | 0:03:01 | |
Many people died. | 0:03:01 | 0:03:02 | |
Today, San Francisco is its usual laid-back and beautiful self. | 0:03:11 | 0:03:15 | |
But the people here know another disaster could hit at any moment. | 0:03:17 | 0:03:21 | |
I know that my family members, | 0:03:23 | 0:03:25 | |
we all have the earthquake kits and we try to have things ready, | 0:03:25 | 0:03:28 | |
but, other than that, we're not very fazed by it, I don't think. | 0:03:28 | 0:03:31 | |
Not until the big one comes. | 0:03:31 | 0:03:32 | |
I believe in being prepared but I also believe that it is fate. | 0:03:32 | 0:03:36 | |
I've been here for over 20 years | 0:03:36 | 0:03:38 | |
and...it kind of puts you in a place | 0:03:38 | 0:03:40 | |
where you live a bit more in the moment, | 0:03:40 | 0:03:43 | |
where you know as much as you prepare, | 0:03:43 | 0:03:45 | |
something could hit at any time. | 0:03:45 | 0:03:47 | |
For millennia, we've met the uncertainties of life | 0:03:48 | 0:03:51 | |
with just a fateful shrug of the shoulders. | 0:03:51 | 0:03:54 | |
But mathematics can help us quantify fate, | 0:03:54 | 0:03:57 | |
even if we can't banish it. | 0:03:57 | 0:03:59 | |
What we now know from our studies is that | 0:04:13 | 0:04:15 | |
the likelihood of a major earthquake hitting the Bay area | 0:04:15 | 0:04:20 | |
is something like 63% over the next 30 years. | 0:04:20 | 0:04:24 | |
But, associated with this 63% number, | 0:04:24 | 0:04:27 | |
which sounds very precise, | 0:04:27 | 0:04:29 | |
there's actually a huge range of uncertainty. | 0:04:29 | 0:04:32 | |
It could be mid-40% | 0:04:32 | 0:04:35 | |
or it could be 80%. | 0:04:35 | 0:04:37 | |
Probabilities are often as much a matter of judgement as arithmetic. | 0:04:38 | 0:04:43 | |
But they can still really help people decide what to do. | 0:04:43 | 0:04:47 | |
After the 1989 earthquake, | 0:04:48 | 0:04:51 | |
there were a lot of aftershocks | 0:04:51 | 0:04:52 | |
and a woman called me and she said, "I'm so nervous to be here." | 0:04:52 | 0:04:57 | |
"I think I want to drive to Los Angeles to visit my daughter." | 0:04:57 | 0:05:00 | |
And I said, "I don't think that's a good idea," and she said, "Why?" | 0:05:00 | 0:05:05 | |
I said, "Well, the likelihood that you'll be injured | 0:05:05 | 0:05:08 | |
"in an automobile accident is much higher | 0:05:08 | 0:05:11 | |
"than the likelihood that an aftershock will harm you." | 0:05:11 | 0:05:14 | |
There's no escaping chance. | 0:05:17 | 0:05:19 | |
But if we can understand how it works, | 0:05:19 | 0:05:22 | |
then perhaps we can even turn it to our advantage. | 0:05:22 | 0:05:25 | |
This was what the first mathematicians to investigate it | 0:05:25 | 0:05:28 | |
hoped to do. | 0:05:28 | 0:05:30 | |
To, as it were, tame chance. | 0:05:30 | 0:05:32 | |
The scholars of the ancient world, | 0:05:33 | 0:05:35 | |
the Egyptians, Babylonians, Greeks and others, | 0:05:35 | 0:05:38 | |
laid down the foundations for geometry, algebra, number theory, | 0:05:38 | 0:05:42 | |
and so much more. | 0:05:42 | 0:05:43 | |
But extraordinarily, | 0:05:44 | 0:05:46 | |
they never even got started on the maths of chance. | 0:05:46 | 0:05:49 | |
It wasn't until the Renaissance that a few pioneering thinkers | 0:05:49 | 0:05:54 | |
first got to grips with probability. | 0:05:54 | 0:05:56 | |
But unlike the ancients, | 0:05:56 | 0:05:59 | |
they weren't loftily pursuing knowledge for its own sake. | 0:05:59 | 0:06:02 | |
They were trying to crack the secrets of gambling. | 0:06:03 | 0:06:07 | |
The first was Gerolamo Cardano, | 0:06:08 | 0:06:10 | |
from the Italian city of Milan. | 0:06:10 | 0:06:13 | |
Cardano was a doctor. | 0:06:13 | 0:06:15 | |
But he was also an obsessive life-long gambler. | 0:06:15 | 0:06:19 | |
This was written in the 1570s, | 0:06:19 | 0:06:22 | |
the earliest known work on probability. | 0:06:22 | 0:06:24 | |
In it, Cardano set out a seasoned gambler's tips and insights, | 0:06:24 | 0:06:28 | |
including how to cheat, | 0:06:28 | 0:06:30 | |
and in one chapter, | 0:06:30 | 0:06:31 | |
laid out the most fundamental principle of probability. | 0:06:31 | 0:06:36 | |
Cardano realised a probability was also a fraction. | 0:06:38 | 0:06:42 | |
So with the roll of a dice, | 0:06:42 | 0:06:44 | |
the probability for each side coming up was one sixth. | 0:06:44 | 0:06:48 | |
And it gets more interesting with two dice. | 0:06:50 | 0:06:52 | |
With two dice, and 36 possible combinations, | 0:06:54 | 0:06:58 | |
there's only one way to throw a 2. | 0:06:58 | 0:07:00 | |
But you're much more likely to get a 7. | 0:07:02 | 0:07:04 | |
Cardano's insight works with games like dice | 0:07:06 | 0:07:10 | |
because we can assume that each of the faces is equally likely. | 0:07:10 | 0:07:14 | |
Provided, as Cardano puts it in his book, | 0:07:14 | 0:07:17 | |
"the dice are honest." | 0:07:17 | 0:07:19 | |
This may seem simple to us now | 0:07:19 | 0:07:21 | |
but it was the very first step in working out how to tame chance. | 0:07:21 | 0:07:24 | |
Las Vegas. | 0:07:37 | 0:07:39 | |
A place Cardano would have surely loved. | 0:07:39 | 0:07:42 | |
The people who run this city have the measure of chance so well, | 0:07:44 | 0:07:47 | |
they've built an entire glittering industry out of it. | 0:07:47 | 0:07:50 | |
It's vital, even so, that anyone here CAN get lucky. | 0:07:52 | 0:07:55 | |
You could even bet one dollar and win a million. | 0:07:56 | 0:07:59 | |
Mike Shackleford is a professional gambler. | 0:08:02 | 0:08:05 | |
His living depends on his command of casino maths. | 0:08:05 | 0:08:09 | |
I analyse every casino game out there | 0:08:23 | 0:08:26 | |
and my goal is to find out the probability | 0:08:26 | 0:08:29 | |
of every possible event in every game. | 0:08:29 | 0:08:32 | |
Almost always, the odds are going to be in the casino's favour. | 0:08:34 | 0:08:37 | |
For example, | 0:08:37 | 0:08:38 | |
in roulette, the house advantage is 5.26% under American rules. | 0:08:38 | 0:08:44 | |
That means that for every dollar the player bets, | 0:08:44 | 0:08:47 | |
on average he can expect to lose 5.26 cents. | 0:08:47 | 0:08:51 | |
Not only do the casinos understand the probabilities perfectly, | 0:08:53 | 0:08:56 | |
they also know that most of the punters don't. | 0:08:56 | 0:08:59 | |
And these games can really mess with our minds. | 0:09:01 | 0:09:04 | |
You'll see a series of outcomes from a slot machine | 0:09:18 | 0:09:21 | |
and believe that there's a pattern to what you've just seen | 0:09:21 | 0:09:24 | |
but that's really just the human brain playing a trick on you | 0:09:24 | 0:09:28 | |
because what's happened in the past has no predictive value | 0:09:28 | 0:09:32 | |
for what is going to happen next. | 0:09:32 | 0:09:34 | |
Yes, the machine may have had this series of payouts in the past. | 0:09:34 | 0:09:37 | |
It may have been hot or cold. | 0:09:37 | 0:09:39 | |
But that has no bearing or no influence | 0:09:39 | 0:09:41 | |
on what is going to happen on that next game. | 0:09:41 | 0:09:44 | |
So you could hit the jackpot symbol | 0:09:44 | 0:09:47 | |
two games in a row. | 0:09:47 | 0:09:48 | |
We just hit the biggest jackpot we've ever hit here. | 0:09:51 | 0:09:54 | |
8,600 dollars! | 0:09:54 | 0:09:56 | |
We just went to this machine about half an hour ago, | 0:09:56 | 0:09:59 | |
so...we got lucky! | 0:09:59 | 0:10:01 | |
Jackpots don't worry the casinos. | 0:10:03 | 0:10:06 | |
They know the slots are programmed | 0:10:06 | 0:10:09 | |
to deliver high house edges in the long run. | 0:10:09 | 0:10:11 | |
Smart players, like Mike, rarely touch them. | 0:10:13 | 0:10:16 | |
A professional gambler plays games where the odds are in their favour. | 0:10:18 | 0:10:23 | |
Probably the most well known is card-counting in Blackjack. | 0:10:23 | 0:10:26 | |
In Blackjack, every time a card is dealt, | 0:10:29 | 0:10:31 | |
the odds change for all the cards that are left. | 0:10:31 | 0:10:34 | |
Mike tracks the cards that are dealt, | 0:10:36 | 0:10:38 | |
to work out how those odds are changing. | 0:10:38 | 0:10:40 | |
So, if the player notices that in the first 25% of the shoe | 0:10:41 | 0:10:45 | |
a lot of small cards came out, more than expected, | 0:10:45 | 0:10:49 | |
he knows that the remaining cards are going to have a surplus of big cards. | 0:10:49 | 0:10:53 | |
So he will adjust his bet size | 0:10:53 | 0:10:55 | |
and he will change how he plays | 0:10:55 | 0:10:57 | |
and by doing that, he can get the odds in his favour. | 0:10:57 | 0:11:01 | |
On a good day, Mike can get a 1% advantage over the house. | 0:11:01 | 0:11:05 | |
It doesn't sound much | 0:11:05 | 0:11:07 | |
but it could mean a lot of money. | 0:11:07 | 0:11:09 | |
The casinos, of course, | 0:11:09 | 0:11:12 | |
don't like card counters | 0:11:12 | 0:11:14 | |
and Mike's been banned from almost every joint in town. | 0:11:14 | 0:11:16 | |
In the world of games, | 0:11:20 | 0:11:22 | |
if you know the rules, you can figure out the probabilities. | 0:11:22 | 0:11:25 | |
But what about the chances of life and death itself? | 0:11:27 | 0:11:30 | |
EVIL LAUGHTER | 0:11:30 | 0:11:32 | |
BELL TOLLS, SPOOKY MUSIC | 0:11:32 | 0:11:35 | |
To be able to put probabilities on our own lives | 0:11:38 | 0:11:42 | |
needed another great mathematical leap. | 0:11:42 | 0:11:46 | |
And this time, the rewards would be even bigger. | 0:11:46 | 0:11:48 | |
For most of history, it was almost a given | 0:11:52 | 0:11:55 | |
that we had not the slightest inkling | 0:11:55 | 0:11:58 | |
of when our time on earth was up. | 0:11:58 | 0:12:00 | |
Death visited when he wanted | 0:12:00 | 0:12:02 | |
and the results were never pretty. | 0:12:02 | 0:12:04 | |
Thank goodness for the consolation of eternal life in the hereafter! | 0:12:08 | 0:12:12 | |
The sculptors who carved this terrifying monument | 0:12:14 | 0:12:17 | |
were capturing the brutal truth of our mortality | 0:12:17 | 0:12:20 | |
as a warning to everyone here, quaking in the pews. | 0:12:20 | 0:12:23 | |
But around the time this was carved, about 300 years ago, | 0:12:23 | 0:12:26 | |
scientists began trying to work out | 0:12:26 | 0:12:29 | |
the mathematical chances, for each individual, | 0:12:29 | 0:12:33 | |
that Death would soon be paying them a call. | 0:12:33 | 0:12:35 | |
The revelation was that you could study one group of people, | 0:12:37 | 0:12:42 | |
the residents of this parish, for instance, | 0:12:42 | 0:12:44 | |
and see how old they were when they died. | 0:12:44 | 0:12:46 | |
From this, you could estimate the chances of death | 0:12:46 | 0:12:49 | |
at each age for everybody else too. | 0:12:49 | 0:12:51 | |
This was a radical idea. | 0:12:52 | 0:12:54 | |
Count the dead | 0:12:54 | 0:12:55 | |
and Death would become less of a divine punishment | 0:12:55 | 0:12:58 | |
and more of a predictable force of nature. | 0:12:58 | 0:13:01 | |
The man who really cracked | 0:13:03 | 0:13:05 | |
how to apply the maths of chance to human lives | 0:13:05 | 0:13:08 | |
was Edmund Halley, the famous astronomer. | 0:13:08 | 0:13:12 | |
Edmund Halley had no interest in what went on in there. | 0:13:15 | 0:13:18 | |
What fascinated him was what had happened out here. | 0:13:18 | 0:13:21 | |
Most people now remember him for his famous comet, | 0:13:21 | 0:13:24 | |
but I salute him as one of history's greatest nerds! | 0:13:24 | 0:13:27 | |
Halley realised that he could calculate | 0:13:27 | 0:13:29 | |
the probabilities of life and death. | 0:13:29 | 0:13:32 | |
All he needed was some good data. | 0:13:32 | 0:13:34 | |
83... | 0:13:37 | 0:13:39 | |
52... | 0:13:41 | 0:13:43 | |
27... | 0:13:43 | 0:13:45 | |
In faraway Breslau, now a city in Poland, | 0:13:46 | 0:13:49 | |
locals were spooked by an ancient superstition | 0:13:49 | 0:13:52 | |
that being aged 49 or 63 | 0:13:52 | 0:13:55 | |
was particularly risky. | 0:13:55 | 0:13:58 | |
To prove the superstition wrong, | 0:13:59 | 0:14:01 | |
a Breslau clergyman collected details of all the town's deaths | 0:14:01 | 0:14:05 | |
and circulated these to the leading scientists of the day. | 0:14:05 | 0:14:09 | |
Halley got hold of the data | 0:14:11 | 0:14:13 | |
and realised the results would have an impact far beyond Breslau. | 0:14:13 | 0:14:17 | |
Halley constructed a table | 0:14:28 | 0:14:30 | |
that was made up of, essentially, two columns. | 0:14:30 | 0:14:33 | |
The first column was age | 0:14:33 | 0:14:36 | |
and the second column was how many people were alive at that age. | 0:14:36 | 0:14:40 | |
The first column started at birth with 1,000 people, | 0:14:40 | 0:14:43 | |
and as the ages increased, | 0:14:43 | 0:14:45 | |
what we saw is that the number of people alive decreased | 0:14:45 | 0:14:49 | |
and this wasn't uniformly. | 0:14:49 | 0:14:50 | |
Halley found nothing special about 49 or 63. | 0:14:52 | 0:14:57 | |
But his data showed that the older you got, | 0:14:57 | 0:15:00 | |
the greater the chance of you dying. | 0:15:00 | 0:15:03 | |
It seems obvious to us now. | 0:15:05 | 0:15:07 | |
But before Halley, | 0:15:07 | 0:15:08 | |
people thought the chances much the same for everyone, | 0:15:08 | 0:15:11 | |
young and old alike. | 0:15:11 | 0:15:12 | |
And Halley's table had an immediate practical benefit. | 0:15:14 | 0:15:18 | |
Halley's tables were also ground-breaking | 0:15:18 | 0:15:21 | |
because not only did he publish | 0:15:21 | 0:15:22 | |
the probability of death at a certain age, | 0:15:22 | 0:15:25 | |
he took that one step further | 0:15:25 | 0:15:26 | |
and applied that to the price of a pension | 0:15:26 | 0:15:29 | |
or the price of life assurance. | 0:15:29 | 0:15:31 | |
He included formulae as to how you could actually come up | 0:15:31 | 0:15:35 | |
with a price for a pension. | 0:15:35 | 0:15:36 | |
People in the 17th century wanted to buy pensions | 0:15:39 | 0:15:41 | |
and life insurance, just like they do today. | 0:15:41 | 0:15:44 | |
But before Halley, anybody who provided them | 0:15:46 | 0:15:48 | |
was in danger of going bankrupt. | 0:15:48 | 0:15:50 | |
So Halley's breakthrough would form the foundation | 0:15:51 | 0:15:54 | |
for the entire pensions and life insurance industry. | 0:15:54 | 0:15:57 | |
And death would never seem as capricious and mysterious again. | 0:15:57 | 0:16:01 | |
And what of Edmund Halley? | 0:16:04 | 0:16:06 | |
He lived all the way to 86, | 0:16:07 | 0:16:10 | |
off his own table! | 0:16:10 | 0:16:12 | |
Costly if you were his pension provider! | 0:16:12 | 0:16:14 | |
Today, the insurance and pensions industry is huge, | 0:16:20 | 0:16:24 | |
and has collected so much data | 0:16:24 | 0:16:26 | |
they can correlate your life and death chances | 0:16:26 | 0:16:28 | |
to your gender, your address, | 0:16:28 | 0:16:31 | |
your job and your lifestyle. | 0:16:31 | 0:16:33 | |
And knowledge of the odds could help us all. | 0:16:33 | 0:16:36 | |
So what do we know about what affects our chances, | 0:16:40 | 0:16:43 | |
for better or for worse? | 0:16:43 | 0:16:45 | |
Imagine this 100 metres is 100 years of possible life. | 0:16:45 | 0:16:49 | |
How many of those years are we actually going to see? | 0:16:49 | 0:16:53 | |
How far along this track are we going to get? | 0:16:53 | 0:16:56 | |
When I was born, the average British male | 0:16:59 | 0:17:02 | |
expected a much shorter life than if born today. | 0:17:02 | 0:17:06 | |
I was born in the 1950s and back then, | 0:17:06 | 0:17:09 | |
my expected lifespan was just 67 years. | 0:17:09 | 0:17:13 | |
But thanks to medical advances | 0:17:13 | 0:17:15 | |
and changes to the way we live and work, | 0:17:15 | 0:17:18 | |
our chances are continually getting better. | 0:17:18 | 0:17:21 | |
The average lifespan is actually rising by three months a year. | 0:17:21 | 0:17:25 | |
If I were born today, I could expect to live to 78. | 0:17:25 | 0:17:29 | |
Even better, the longer you live, | 0:17:31 | 0:17:33 | |
the longer you can expect to live, | 0:17:33 | 0:17:35 | |
because you've been lucky enough not to die young. | 0:17:35 | 0:17:38 | |
So at my age now, | 0:17:38 | 0:17:39 | |
I can expect to live not to 67... | 0:17:39 | 0:17:43 | |
..or 78... | 0:17:43 | 0:17:45 | |
..but... | 0:17:45 | 0:17:48 | |
..82. | 0:17:48 | 0:17:50 | |
But what's not so cheerful | 0:17:51 | 0:17:53 | |
is the effect of all those things I might do throughout my life | 0:17:53 | 0:17:56 | |
that could stop me getting this far, or even further. | 0:17:56 | 0:17:59 | |
Research tells us that | 0:18:01 | 0:18:03 | |
for every day you're five kilos overweight, like I am, | 0:18:03 | 0:18:06 | |
you can expect to lose half an hour off your life. | 0:18:06 | 0:18:09 | |
Aah! | 0:18:13 | 0:18:14 | |
Sad to say, | 0:18:14 | 0:18:16 | |
if you're a man sinking three pints a day | 0:18:16 | 0:18:19 | |
then that's also half an hour. | 0:18:19 | 0:18:22 | |
But what about exercise? Won't that make things better? | 0:18:22 | 0:18:25 | |
Yes, it will. But there's a catch. | 0:18:25 | 0:18:28 | |
A regular run of half an hour | 0:18:28 | 0:18:30 | |
and you can expect to live longer. | 0:18:30 | 0:18:32 | |
Half an hour longer. | 0:18:32 | 0:18:34 | |
So I hope you actually like running. | 0:18:34 | 0:18:37 | |
Cos that's how you just spent your extra half hour. | 0:18:37 | 0:18:40 | |
Surprise, surprise, the worst news is for all you smokers. | 0:18:41 | 0:18:45 | |
Two cigarettes costs half an hour. | 0:18:45 | 0:18:48 | |
But the average smoker's on nearly 20 a day. | 0:18:48 | 0:18:51 | |
And it all adds up. | 0:18:51 | 0:18:53 | |
Doing something that costs half an hour a day... | 0:18:56 | 0:19:01 | |
Well, that's more than a week off each year | 0:19:01 | 0:19:03 | |
and, in the long run, that's a whole year off your life. | 0:19:03 | 0:19:07 | |
For that 20-a-day smoker, | 0:19:07 | 0:19:11 | |
that's a staggering 10 years you should expect to lose. | 0:19:11 | 0:19:14 | |
All these figures tell us a lot. | 0:19:17 | 0:19:18 | |
But as for chance itself, | 0:19:18 | 0:19:20 | |
that's certainly not disappeared. | 0:19:20 | 0:19:22 | |
When I say I can expect to live to 82, | 0:19:24 | 0:19:26 | |
I'm not actually making a prediction. | 0:19:26 | 0:19:28 | |
It may be shorter or, with luck, it may be longer. | 0:19:28 | 0:19:32 | |
82 is the average. | 0:19:32 | 0:19:33 | |
Imagine 100 possible future me's, each equally likely. | 0:19:33 | 0:19:38 | |
I'm 58 now and as the years roll by, | 0:19:41 | 0:19:44 | |
in more and more of these possible futures, I die, | 0:19:44 | 0:19:48 | |
until by the age of 82 | 0:19:48 | 0:19:51 | |
about half of my future selves will be dead | 0:19:51 | 0:19:54 | |
and about half still alive. | 0:19:54 | 0:19:56 | |
Which is going to be me? That's just chance. | 0:19:57 | 0:20:00 | |
Beyond 82, more and more drop dead. | 0:20:01 | 0:20:05 | |
And there's a very small chance I could live to be very old indeed. | 0:20:05 | 0:20:10 | |
If I were a smoker, it's just possible I'd beat the odds. | 0:20:12 | 0:20:16 | |
But overall, my chances wouldn't look nearly so good. | 0:20:19 | 0:20:22 | |
Of course, many people would say going on about risks | 0:20:25 | 0:20:29 | |
is being a big killjoy. | 0:20:29 | 0:20:31 | |
The writer Kingsley Amis famously said, | 0:20:32 | 0:20:35 | |
"No pleasure is worth giving up | 0:20:35 | 0:20:37 | |
"for the sake of two more years | 0:20:37 | 0:20:39 | |
"in a geriatric home at Weston-super-Mare." | 0:20:39 | 0:20:42 | |
But I believe understanding the risks | 0:20:43 | 0:20:45 | |
might actually help us to have more fun, not less. | 0:20:45 | 0:20:50 | |
OK. Just put one arm through there for me... | 0:20:50 | 0:20:52 | |
the other through there and turn around. Thank you. | 0:20:52 | 0:20:56 | |
What we'll do is we'll start strapping you in. | 0:20:56 | 0:20:58 | |
Many of my favourite experiences would be impossible | 0:20:59 | 0:21:02 | |
without taking some risk, | 0:21:02 | 0:21:04 | |
but I'm about to do something I've never done before | 0:21:04 | 0:21:06 | |
which really does involve risk. | 0:21:06 | 0:21:08 | |
The best way to compare risky activities | 0:21:12 | 0:21:15 | |
is to use the micromort, | 0:21:15 | 0:21:18 | |
a cheery little unit which represents | 0:21:18 | 0:21:20 | |
a one-in-a-million chance of death. | 0:21:20 | 0:21:22 | |
Skydiving is actually safer than you might think. | 0:21:34 | 0:21:36 | |
There's only about a seven-in-a-million chance of dying. | 0:21:36 | 0:21:39 | |
That's seven micromorts. | 0:21:39 | 0:21:41 | |
That's about the same risk as 40 miles on a motorbike. | 0:21:41 | 0:21:44 | |
But there's still a risk. | 0:21:44 | 0:21:46 | |
And you may think I should be old enough to know better. | 0:21:46 | 0:21:48 | |
But I think it could be rational to take more risks when you get older. | 0:21:48 | 0:21:52 | |
An average 18-year-old has a chance of dying in the next 12 months | 0:21:55 | 0:21:59 | |
of about 500 micromorts. | 0:21:59 | 0:22:01 | |
But at my age, the equivalent is 7,000 micromorts. | 0:22:03 | 0:22:07 | |
7,000 micromorts doesn't sound great, does it? | 0:22:11 | 0:22:13 | |
But my extra risk of skydiving is only seven micromorts more. | 0:22:13 | 0:22:17 | |
That's not much difference. | 0:22:17 | 0:22:18 | |
So the risk is actually pretty low. | 0:22:21 | 0:22:23 | |
But the funny thing is, | 0:22:25 | 0:22:26 | |
now I'm actually in the plane and there's no backing out, | 0:22:26 | 0:22:29 | |
it suddenly seems a lot worse. | 0:22:29 | 0:22:31 | |
Will my parachute fail? I don't know. | 0:22:33 | 0:22:37 | |
Will we be blown into a tree? I don't know. | 0:22:37 | 0:22:40 | |
Will I be sick with fright over my jumpsuit? | 0:22:42 | 0:22:45 | |
The probability of that is getting close to 100%! | 0:22:45 | 0:22:48 | |
It's the moment of truth. | 0:22:53 | 0:22:55 | |
Here we go! | 0:22:58 | 0:22:59 | |
Yes, I'm a Professor of Risk | 0:23:04 | 0:23:06 | |
and I've made a sound decision rooted in the numbers, | 0:23:06 | 0:23:10 | |
but as I fall, I can't help thinking | 0:23:10 | 0:23:12 | |
there's a chance I'll die very soon indeed. | 0:23:12 | 0:23:14 | |
I could buy myself a pair of silver hairbrushes. | 0:23:19 | 0:23:22 | |
Oh, hello! | 0:23:22 | 0:23:24 | |
I'm having a go at these premium bonds. | 0:23:24 | 0:23:25 | |
They're wonderful things - you can't lose. | 0:23:25 | 0:23:28 | |
Look, there are staggering prizes each month, | 0:23:28 | 0:23:31 | |
you can get your money back any time you like, | 0:23:31 | 0:23:33 | |
and, what's more, all your tickets go back into each draw | 0:23:33 | 0:23:36 | |
whether you've been lucky before or not! | 0:23:36 | 0:23:38 | |
I might win a thousand quid! | 0:23:38 | 0:23:40 | |
I love a bit of a flutter. | 0:23:40 | 0:23:42 | |
Not a word to Bessie about that! | 0:23:42 | 0:23:43 | |
In 1956, | 0:23:45 | 0:23:46 | |
Britain introduced a brand new kind of savings scheme, | 0:23:46 | 0:23:49 | |
Premium Bonds, | 0:23:49 | 0:23:51 | |
that instead of paying you interest | 0:23:51 | 0:23:54 | |
gave you the chance to win big prizes. | 0:23:54 | 0:23:57 | |
At its heart was something created by mathematicians, | 0:23:57 | 0:24:00 | |
a world of pure chance, randomness. | 0:24:00 | 0:24:04 | |
This is a world where every element | 0:24:06 | 0:24:08 | |
is disconnected from every other, | 0:24:08 | 0:24:10 | |
that operates beyond our influence or control. | 0:24:10 | 0:24:13 | |
The Premium Bonds monthly prize draw | 0:24:14 | 0:24:17 | |
needed complete randomness | 0:24:17 | 0:24:20 | |
to make sure it was scrupulously fair. | 0:24:20 | 0:24:22 | |
There was quite a lot of human interest in randomness | 0:24:38 | 0:24:40 | |
for the first time, | 0:24:40 | 0:24:42 | |
where people began to think about, | 0:24:42 | 0:24:44 | |
"what are the chances of my winning?" | 0:24:44 | 0:24:46 | |
But what it required was | 0:24:46 | 0:24:49 | |
a source of random numbers | 0:24:49 | 0:24:52 | |
and a special purpose computer was built for this | 0:24:52 | 0:24:55 | |
and it was one of the very first special purpose computers. | 0:24:55 | 0:24:58 | |
We're going to an electronic machine, if you understand what that is, | 0:24:58 | 0:25:01 | |
but thank goodness its complicated name is ERNIE for short. | 0:25:01 | 0:25:05 | |
ERNIE stood for Electronic Random Number Indicating Equipment. | 0:25:05 | 0:25:11 | |
Truly random numbers are hard to produce, | 0:25:11 | 0:25:13 | |
and ERNIE got them by sampling the electrical noise | 0:25:13 | 0:25:16 | |
from a series of vacuum tubes. | 0:25:16 | 0:25:18 | |
It was state-of-the-art engineering. | 0:25:18 | 0:25:20 | |
Randomness really, in a certain extent, means unpredictable, | 0:25:22 | 0:25:26 | |
but also, for the purposes of ERNIE, | 0:25:26 | 0:25:28 | |
it needed to be unpredictable and unbiased | 0:25:28 | 0:25:31 | |
and my job as a young mathematician | 0:25:31 | 0:25:34 | |
was to show that it really was unbiased | 0:25:34 | 0:25:36 | |
to any particular Premium Bond number. | 0:25:36 | 0:25:39 | |
This was quite a skilled and lengthy task | 0:25:39 | 0:25:42 | |
to say those weasel words that mathematicians use, | 0:25:42 | 0:25:45 | |
"We have no reason to suppose that ERNIE is not random." | 0:25:45 | 0:25:50 | |
For me, as a mathematician, complete randomness is fascinating. | 0:25:58 | 0:26:02 | |
It's full of curiosities. | 0:26:02 | 0:26:05 | |
And unexpectedly, it turns out to have its own rules, | 0:26:05 | 0:26:08 | |
patterns and structure. | 0:26:08 | 0:26:10 | |
This is officially the most boring book in the world. Ever. | 0:26:13 | 0:26:19 | |
It's called One Million Random Digits | 0:26:19 | 0:26:22 | |
and that's literally what it is. | 0:26:22 | 0:26:24 | |
Page after page of random numbers. | 0:26:24 | 0:26:27 | |
Say what you like about this book, though, | 0:26:29 | 0:26:32 | |
at least the plot is unpredictable. | 0:26:32 | 0:26:34 | |
Printed in 1955, | 0:26:36 | 0:26:37 | |
these numbers were produced by an early computer rather like ERNIE. | 0:26:37 | 0:26:43 | |
And people have used them since | 0:26:43 | 0:26:45 | |
for everything from randomised clinical trials | 0:26:45 | 0:26:48 | |
to encrypting communications. | 0:26:48 | 0:26:50 | |
I might not read this book cover to cover, | 0:26:51 | 0:26:54 | |
but I promise you there are some really interesting parts. | 0:26:54 | 0:26:57 | |
I mean, look at this. 00000. | 0:26:57 | 0:27:01 | |
And here's another great bit. | 0:27:01 | 0:27:03 | |
12345. | 0:27:06 | 0:27:08 | |
It seems really strange to see these. | 0:27:08 | 0:27:11 | |
I mean, how can these be random? | 0:27:11 | 0:27:12 | |
But, of course, they're as random as the numbers next to them. | 0:27:12 | 0:27:16 | |
Not only can you expect to find patterns like these, | 0:27:16 | 0:27:20 | |
you can even calculate how often you expect to find them. | 0:27:20 | 0:27:23 | |
A perfect sequence of five numbers. | 0:27:23 | 0:27:25 | |
There should be 50 of these in the book. | 0:27:25 | 0:27:28 | |
And the same number five times in a row, | 0:27:28 | 0:27:31 | |
there should be about 100 of these. | 0:27:31 | 0:27:33 | |
You can even expect, | 0:27:33 | 0:27:34 | |
somewhere in these one million random numbers, | 0:27:34 | 0:27:37 | |
the same number to occur seven times in a row. | 0:27:37 | 0:27:40 | |
And I've found it. | 0:27:40 | 0:27:42 | |
6666666. | 0:27:43 | 0:27:47 | |
What makes randomness so useful | 0:27:53 | 0:27:55 | |
is that it is completely unpredictable... | 0:27:55 | 0:27:58 | |
but in a predictable way. | 0:27:58 | 0:27:59 | |
So predictable that it has its own shape. | 0:28:02 | 0:28:04 | |
A lottery is a great example. | 0:28:07 | 0:28:09 | |
Each National Lottery draw is... | 0:28:11 | 0:28:13 | |
well, random. | 0:28:13 | 0:28:15 | |
There seems no pattern at all. | 0:28:15 | 0:28:18 | |
But there are also seemingly strange results. | 0:28:18 | 0:28:21 | |
Today, after something approaching 2,000 National Lottery draws | 0:28:22 | 0:28:27 | |
over 20 years, there are huge differences | 0:28:27 | 0:28:30 | |
in how often different numbers have come up. | 0:28:30 | 0:28:32 | |
Number 38 has been picked 241 times... | 0:28:33 | 0:28:37 | |
..while number 20 has come up just 171. | 0:28:37 | 0:28:41 | |
It might look like something's wrong, | 0:28:43 | 0:28:45 | |
but taking all the results together, | 0:28:45 | 0:28:47 | |
the totals match the shape of randomness remarkably well. | 0:28:47 | 0:28:51 | |
And even the outlying results | 0:28:53 | 0:28:55 | |
are just where the shape shows they should be. | 0:28:55 | 0:28:58 | |
Here we go. Let's pick some numbers. | 0:29:04 | 0:29:06 | |
It's not a great bet, I admit. | 0:29:08 | 0:29:11 | |
There's only a 1-in-14-million chance of me winning the jackpot. | 0:29:11 | 0:29:15 | |
In fact, I'm very unlikely to win anything at all. | 0:29:15 | 0:29:17 | |
There's only a 1-in-56 chance | 0:29:17 | 0:29:19 | |
of me getting the smallest prize of £10. | 0:29:19 | 0:29:23 | |
Overall, the lottery only pays back | 0:29:23 | 0:29:25 | |
45% of the money it takes in. | 0:29:25 | 0:29:27 | |
Far, far worse than any casino game. | 0:29:27 | 0:29:30 | |
If you must play, | 0:29:30 | 0:29:33 | |
though you can't change your chances of winning, | 0:29:33 | 0:29:35 | |
you can improve your chances of not sharing the jackpot. | 0:29:35 | 0:29:39 | |
Many people pick birthdays or other significant dates, | 0:29:39 | 0:29:42 | |
so avoid the numbers up to 31. | 0:29:42 | 0:29:44 | |
You may even want to steer clear | 0:29:44 | 0:29:46 | |
of that supposedly lucky number, 38. | 0:29:46 | 0:29:48 | |
In the end, it doesn't matter what numbers you choose, | 0:29:50 | 0:29:53 | |
every combination, say 1, 2, 3, 4, 5, 6, | 0:29:53 | 0:29:56 | |
is as likely as any other. | 0:29:56 | 0:29:58 | |
That's because it's completely random. | 0:29:58 | 0:30:01 | |
But randomness can confuse us. | 0:30:06 | 0:30:10 | |
For example, use the shuffle feature on the original iPod | 0:30:10 | 0:30:14 | |
to play its tracks in random order and before too long | 0:30:14 | 0:30:17 | |
you're very likely to land on the same album again. | 0:30:17 | 0:30:20 | |
People found it so off-putting | 0:30:22 | 0:30:24 | |
that the shuffle on later-generation iPods was supposedly tweaked. | 0:30:24 | 0:30:27 | |
Apple famously explained, | 0:30:30 | 0:30:31 | |
"We're making it less random so it feels more random." | 0:30:31 | 0:30:35 | |
Patterns and connections like this are what we call coincidences. | 0:30:42 | 0:30:47 | |
And no matter how much we should expect them, | 0:30:47 | 0:30:49 | |
they nonetheless still make our heads spin. | 0:30:49 | 0:30:51 | |
I love coincidences so much | 0:30:53 | 0:30:57 | |
I decided to try to collect them. | 0:30:57 | 0:30:59 | |
Luckily, it's an interest the nation shares. | 0:30:59 | 0:31:02 | |
Let's talk about coincidences now, at 7:24, why do they happen? | 0:31:02 | 0:31:06 | |
-Professor David Spiegelhalter, good morning. -Good morning. | 0:31:06 | 0:31:08 | |
You are an expert in risk and chance, is what I'm reading, | 0:31:08 | 0:31:11 | |
at Cambridge University, | 0:31:11 | 0:31:13 | |
but why are you interested in chance and coincidence? | 0:31:13 | 0:31:15 | |
Well, it's part of my job. | 0:31:15 | 0:31:16 | |
I'm Professor of the Public Understanding of Risk, | 0:31:16 | 0:31:19 | |
so everything to do with chance, uncertainty and coincidences | 0:31:19 | 0:31:22 | |
is what I'm interested in. | 0:31:22 | 0:31:23 | |
And we've set up this website where we're collecting coincidence stories | 0:31:23 | 0:31:27 | |
which people are sending in, | 0:31:27 | 0:31:29 | |
and the sort of things where people, when they happen to them, say, | 0:31:29 | 0:31:32 | |
"Ooh, what are the chances of that?" | 0:31:32 | 0:31:34 | |
And we're trying to work out what the chances of that really are. | 0:31:34 | 0:31:37 | |
It's like a family having three children all with the same birthday, | 0:31:37 | 0:31:41 | |
born in different years, but all three children being born | 0:31:41 | 0:31:44 | |
on the same birthday. You'd think, "Wow, what are the chances of that?" | 0:31:44 | 0:31:47 | |
Well, we can work those out. | 0:31:47 | 0:31:49 | |
Since there's a million families in this country with three children, | 0:31:49 | 0:31:52 | |
we'd expect there's about 8 families like that. | 0:31:52 | 0:31:54 | |
Now, we've found three of them. | 0:31:54 | 0:31:56 | |
People read great significance into these things, though. | 0:31:56 | 0:31:59 | |
Are they misguided in doing that? | 0:31:59 | 0:32:01 | |
Well, it's Friday 13th, exactly the day that shows people do believe | 0:32:01 | 0:32:04 | |
in luck and fortune and things like that... | 0:32:04 | 0:32:06 | |
But I suppose I'm being a bit scientific about them, | 0:32:06 | 0:32:08 | |
so some of them we try to take apart and do the maths, | 0:32:08 | 0:32:11 | |
but other ones are just amazing. | 0:32:11 | 0:32:13 | |
There's a lovely example last year where a French family, | 0:32:13 | 0:32:16 | |
their house was hit by a meteorite. | 0:32:16 | 0:32:17 | |
Well, that's pretty surprising itself, | 0:32:17 | 0:32:20 | |
but their name was Comette. Isn't that just beautiful? | 0:32:20 | 0:32:23 | |
"What are the chances of never experiencing a coincidence?" | 0:32:23 | 0:32:25 | |
says Steve in Cheshire. | 0:32:25 | 0:32:26 | |
Oh, very low indeed. That would be really, really bizarre. | 0:32:26 | 0:32:30 | |
Good one, Steve. | 0:32:30 | 0:32:32 | |
7:29. What are the chances of any decent weather over the weekend? | 0:32:32 | 0:32:35 | |
'Pretty good, actually, Rachel. | 0:32:35 | 0:32:37 | |
'We've got some clear skies out there at the moment, | 0:32:37 | 0:32:40 | |
'but because of those clear skies | 0:32:40 | 0:32:42 | |
'temperatures are hovering at or just below freezing...' | 0:32:42 | 0:32:45 | |
The radio show was a huge success. | 0:32:45 | 0:32:48 | |
The stories flooded in. Over 3,000 of them. | 0:32:48 | 0:32:51 | |
We got lots of coincidences with numbers, names and words. | 0:32:51 | 0:32:57 | |
And loads of calendar ones, | 0:32:57 | 0:32:59 | |
including one more of those rare triple birthdays. | 0:32:59 | 0:33:03 | |
Some of these stories are really amazing. | 0:33:03 | 0:33:06 | |
Lots of them are about running into friends and acquaintances | 0:33:06 | 0:33:09 | |
in the most unlikely places. And I love this one. | 0:33:09 | 0:33:12 | |
Mick Preston was on a cycling holiday in the Pyrenees | 0:33:13 | 0:33:16 | |
and during one stop-over, he wrote his friend, Alan, a postcard. | 0:33:16 | 0:33:19 | |
But, incredibly, on the way to post it, he bumped into Alan, | 0:33:19 | 0:33:23 | |
who just by chance was on holiday in the same place, | 0:33:23 | 0:33:26 | |
so Mick gave him the postcard in person. | 0:33:26 | 0:33:29 | |
As Mick himself said, | 0:33:29 | 0:33:31 | |
that was a waste of a good stamp! | 0:33:31 | 0:33:33 | |
What's striking is that although these and other coincidences | 0:33:38 | 0:33:42 | |
happened a long time ago, people were so jolted by them | 0:33:42 | 0:33:46 | |
they still remember them years later. | 0:33:46 | 0:33:48 | |
I think our brains are hard-wired to look for cause and effect, | 0:33:49 | 0:33:53 | |
to try to come up with reasons why things happen. | 0:33:53 | 0:33:57 | |
So when things happen for no apparent reason at all, | 0:33:57 | 0:34:00 | |
we find it really spooky. | 0:34:00 | 0:34:02 | |
We just don't seem to easily accept | 0:34:02 | 0:34:05 | |
that we might not be able to understand or control | 0:34:05 | 0:34:08 | |
what happens in our lives. | 0:34:08 | 0:34:09 | |
Random events that have no explanation beyond chance | 0:34:14 | 0:34:18 | |
saturate our lives... | 0:34:18 | 0:34:20 | |
..but some people think they can eliminate the random - | 0:34:22 | 0:34:24 | |
control everything - and that chance has nothing to do with them at all. | 0:34:24 | 0:34:29 | |
Ed Smith was once said to be the golden boy of English cricket. | 0:34:30 | 0:34:35 | |
For years he held an idea about chance - | 0:34:35 | 0:34:37 | |
or, as he called it, "luck" - | 0:34:37 | 0:34:39 | |
that he shared with many of his fellow sporting professionals. | 0:34:39 | 0:34:43 | |
When I turned full-time professional in 1999, | 0:34:55 | 0:34:58 | |
we had all these meetings | 0:34:58 | 0:34:59 | |
about how we were going to approach the season | 0:34:59 | 0:35:02 | |
and someone put his hand up and said, | 0:35:02 | 0:35:04 | |
"I don't think we should say, 'bad luck,' to each other. | 0:35:04 | 0:35:06 | |
"That's an excuse. It's not bad luck. | 0:35:06 | 0:35:08 | |
"If someone gets out, it's their fault." | 0:35:08 | 0:35:10 | |
I think as sportsmen we're conditioned to think that, | 0:35:10 | 0:35:13 | |
that you are in total control. | 0:35:13 | 0:35:16 | |
I mean, if you, if you walk out to bat in professional cricket | 0:35:16 | 0:35:18 | |
and you say, "Well, maybe I'll be lucky and maybe I won't, | 0:35:18 | 0:35:21 | |
"and maybe someone will bowl a good ball I'll be out, and I can't do anything about it," | 0:35:21 | 0:35:24 | |
then you're stacking the deck against yourself before you even begin. | 0:35:24 | 0:35:27 | |
Ed played for England and became captain of Middlesex. | 0:35:30 | 0:35:34 | |
Everything went well for him, | 0:35:35 | 0:35:37 | |
until one day during a county cricket match at Lord's. | 0:35:37 | 0:35:43 | |
So, we're in the middle of this match, it's going well, | 0:35:43 | 0:35:45 | |
we're pretty much cantering to victory. | 0:35:45 | 0:35:47 | |
We're on a bit of a streak of five, six wins in a row, everything's going well | 0:35:47 | 0:35:50 | |
and I'm doing the most routine thing in cricket, I'm running a two. | 0:35:50 | 0:35:54 | |
It happens all the time, you know... | 0:35:54 | 0:35:56 | |
it's not particularly demanding, athletically, | 0:35:56 | 0:35:58 | |
to run 20 yards and then come back again. | 0:35:58 | 0:36:00 | |
And I ran the first one and then you just rub the bat in, | 0:36:00 | 0:36:04 | |
and I just, sort of, collapsed. | 0:36:04 | 0:36:06 | |
And I'm lying in this, and have this shooting pain in my ankle, | 0:36:06 | 0:36:09 | |
and it was only quite a few weeks later that there was an X-ray, | 0:36:09 | 0:36:14 | |
and it turned out that I'd broken my ankle, | 0:36:14 | 0:36:17 | |
and I wasn't going to play any time soon! | 0:36:17 | 0:36:20 | |
I missed the rest of that season and then I retired, effectively, | 0:36:20 | 0:36:23 | |
at the end of that season and didn't play professional cricket again. | 0:36:23 | 0:36:27 | |
In a single moment, Ed's entire career vanished. | 0:36:27 | 0:36:31 | |
He had been touched by chance. | 0:36:31 | 0:36:34 | |
No-one and nothing was to blame. | 0:36:34 | 0:36:37 | |
I think I found it hard to accept. You know, my own willpower, | 0:36:37 | 0:36:41 | |
my determination to control, to shape my own life, was so great | 0:36:41 | 0:36:46 | |
but the reality is that I wasn't in control. | 0:36:46 | 0:36:49 | |
The fact that I had a broken ankle was just a fact. | 0:36:49 | 0:36:52 | |
It was a circumstance that had happened to me. | 0:36:52 | 0:36:55 | |
So, it was like a clash between, er, my own desire to control everything | 0:36:55 | 0:36:59 | |
and the fact of luck, and, you know, luck won. | 0:36:59 | 0:37:03 | |
The moral of Ed's story is clear - | 0:37:05 | 0:37:07 | |
don't beat yourself up about every failure. | 0:37:07 | 0:37:10 | |
But the opposite is also true - | 0:37:10 | 0:37:12 | |
don't be too chuffed with yourself about every success. | 0:37:12 | 0:37:16 | |
Remember this? | 0:37:19 | 0:37:21 | |
I know you can't get rid of luck, but right now I wish you could! | 0:37:21 | 0:37:25 | |
The parachute hasn't failed at least! | 0:37:28 | 0:37:31 | |
I don't seem to be being blown into a forest! | 0:37:33 | 0:37:36 | |
And I haven't even been sick! | 0:37:36 | 0:37:38 | |
That was so cool! Can we do it again? | 0:37:41 | 0:37:44 | |
You know, the really interesting thing is that whilst I was confident | 0:37:47 | 0:37:51 | |
I would land safely, I couldn't be absolutely certain. | 0:37:51 | 0:37:56 | |
The question is, "Why not? Why does chance exist?" | 0:37:56 | 0:38:00 | |
The story of science, for centuries, has been a triumph - | 0:38:10 | 0:38:14 | |
unlocking the mathematical laws behind everything, | 0:38:14 | 0:38:18 | |
from the atom to the universe. | 0:38:18 | 0:38:20 | |
So why is there still room for the random? For unpredictability? | 0:38:24 | 0:38:30 | |
Why, instead, can't everything in nature be determined? | 0:38:30 | 0:38:33 | |
In which case, we could get rid of chance altogether | 0:38:34 | 0:38:38 | |
and I would be out of a job. | 0:38:38 | 0:38:41 | |
In the 1680s Isaac Newton revolutionised science | 0:38:46 | 0:38:51 | |
with a set of universal laws. | 0:38:51 | 0:38:54 | |
He calculated the orbits of moons and planets... | 0:38:54 | 0:38:57 | |
even predicted the timings of eclipses | 0:38:57 | 0:39:01 | |
and, of course, explained the fall of an earthbound apple. | 0:39:01 | 0:39:05 | |
Oh! | 0:39:05 | 0:39:06 | |
Newton's friend, Edmund Halley, predicted the returns of comets... | 0:39:08 | 0:39:12 | |
..and other scientists eagerly worked to discover new laws | 0:39:15 | 0:39:19 | |
and make more predictions. | 0:39:19 | 0:39:21 | |
"The Enlightenment", it came to be called. | 0:39:21 | 0:39:24 | |
In 1779, the French scientist Pierre-Simon Laplace | 0:39:27 | 0:39:31 | |
had a bold vision. | 0:39:31 | 0:39:32 | |
If some vast intellect | 0:39:33 | 0:39:36 | |
could not only comprehend all the laws of nature, | 0:39:36 | 0:39:38 | |
but could also measure everything, even down to the tiniest atom, | 0:39:38 | 0:39:42 | |
then he might predict the future precisely. | 0:39:42 | 0:39:45 | |
And uncertainty would simply disappear. | 0:39:45 | 0:39:48 | |
Hmm. | 0:39:50 | 0:39:51 | |
In theory, with the right mathematics, | 0:39:52 | 0:39:55 | |
everything in the physical universe could be measured and predicted, | 0:39:55 | 0:39:58 | |
just like the movement of the stars and the planets. | 0:39:58 | 0:40:01 | |
So, for example, if I threw a dice | 0:40:01 | 0:40:04 | |
I could predict exactly how it would land. | 0:40:04 | 0:40:07 | |
This theory is what we call "scientific determinism". | 0:40:10 | 0:40:14 | |
In theory, if we gather the data and do the calculations, | 0:40:14 | 0:40:18 | |
we should be able to get rid of chance altogether, | 0:40:18 | 0:40:21 | |
but, in practice, prediction has proved frustratingly hard. | 0:40:21 | 0:40:25 | |
It's as if there is something about our physical world | 0:40:25 | 0:40:28 | |
that makes prediction all but impossible. | 0:40:28 | 0:40:31 | |
Despite the promise of the laws of Newton | 0:40:33 | 0:40:35 | |
and all the scientists who followed him, we remain in the dark. | 0:40:35 | 0:40:39 | |
But why? | 0:40:39 | 0:40:40 | |
In the 20th century, scientists - like meteorologist Ed Lorenz - | 0:40:41 | 0:40:46 | |
discovered that even tiny influences could have immense | 0:40:46 | 0:40:49 | |
and unpredictable consequences. | 0:40:49 | 0:40:51 | |
As Lorenz put it, "The flap of a butterfly's wings in Brazil | 0:40:54 | 0:40:58 | |
"could cause a tornado in Texas." | 0:40:58 | 0:41:00 | |
The theory of determinism had to acknowledge complexity and chaos. | 0:41:02 | 0:41:07 | |
The laws of physics weren't wrong, | 0:41:07 | 0:41:09 | |
but the real world was just too complicated | 0:41:09 | 0:41:12 | |
to ever fully comprehend. | 0:41:12 | 0:41:14 | |
Also in the 20th century, physicists, like Werner Heisenberg, | 0:41:15 | 0:41:20 | |
delving ever deeper into the nature of matter, | 0:41:20 | 0:41:22 | |
realised there was an absolute limit to what they could ever know. | 0:41:22 | 0:41:27 | |
In his work on quantum mechanics, | 0:41:27 | 0:41:30 | |
Heisenberg set out the uncertainty principle - | 0:41:30 | 0:41:33 | |
essential parts of the subatomic world | 0:41:33 | 0:41:36 | |
could at best only ever be described as a probability. | 0:41:36 | 0:41:40 | |
The dreams scientists once had of conquering chance | 0:41:43 | 0:41:47 | |
have been shattered. | 0:41:47 | 0:41:48 | |
Quantum mechanics has shown us a subatomic world | 0:41:48 | 0:41:51 | |
that is fundamentally uncertain. | 0:41:51 | 0:41:53 | |
Beyond the subatomic, we are still governed by mechanical | 0:41:53 | 0:41:57 | |
and therefore deterministic laws, | 0:41:57 | 0:41:59 | |
but, paradoxically, the mathematics of chaos and complexity | 0:41:59 | 0:42:03 | |
means that things are still ultimately unpredictable. | 0:42:03 | 0:42:06 | |
So what is chance? | 0:42:07 | 0:42:09 | |
Is it real? Is it something out there in the fabric of the universe? | 0:42:09 | 0:42:13 | |
Or is chance in here? Just an excuse? | 0:42:13 | 0:42:17 | |
What Laplace called, "Merely the measure of our ignorance?" | 0:42:17 | 0:42:21 | |
Or is it a bit of both? | 0:42:21 | 0:42:22 | |
After centuries of discovery, | 0:42:22 | 0:42:24 | |
we are still not much closer to knowing what chance really is. | 0:42:24 | 0:42:28 | |
One thing is certain - chance is here to stay. | 0:42:31 | 0:42:35 | |
What's more, it has actually been put to work. | 0:42:35 | 0:42:39 | |
Faced with complex and unpredictable problems, | 0:42:39 | 0:42:42 | |
scientists have found ways to use chance itself | 0:42:42 | 0:42:45 | |
to convert blind uncertainty into computable probability. | 0:42:45 | 0:42:49 | |
In the early years of the Cold War, | 0:42:52 | 0:42:54 | |
nuclear physicists at Los Alamos | 0:42:54 | 0:42:57 | |
were working to design a new atomic bomb. | 0:42:57 | 0:43:00 | |
They wanted to predict when an atomic chain reaction | 0:43:00 | 0:43:03 | |
might go critical, | 0:43:03 | 0:43:05 | |
but the physics was so complex that at each step in the chain | 0:43:05 | 0:43:09 | |
they were uncertain about what would happen next. | 0:43:09 | 0:43:12 | |
So they turned to the mathematics of chance. | 0:43:12 | 0:43:15 | |
For each step, they chose an outcome at random | 0:43:17 | 0:43:20 | |
and then calculated what the resulting chain reaction would do. | 0:43:20 | 0:43:25 | |
Then they randomly chose a new set of outcomes | 0:43:25 | 0:43:28 | |
and calculated a new result. | 0:43:28 | 0:43:30 | |
They did this repeatedly until they had hundreds of different, | 0:43:32 | 0:43:35 | |
but equally likely, possible results. | 0:43:35 | 0:43:39 | |
And combining them all gave the Los Alamos scientists | 0:43:39 | 0:43:42 | |
an extremely accurate probability | 0:43:42 | 0:43:44 | |
for what the chain reaction would do for real. | 0:43:44 | 0:43:46 | |
They called it the Monte Carlo method, | 0:43:48 | 0:43:51 | |
like rolling a dice over and over again. | 0:43:51 | 0:43:55 | |
And the bomb worked. | 0:43:57 | 0:43:59 | |
Today, that very same Monte Carlo method, | 0:44:09 | 0:44:12 | |
creating arrays of possible futures to compute probabilities, | 0:44:12 | 0:44:16 | |
is being used to try to solve problems in many different fields. | 0:44:16 | 0:44:20 | |
And what's most exciting for me and my fellow Brits | 0:44:20 | 0:44:23 | |
is that this might help to answer | 0:44:23 | 0:44:25 | |
that all-important question: When I go out, do I take an umbrella? | 0:44:25 | 0:44:30 | |
In the 1920s, the economist John Maynard Keynes | 0:44:38 | 0:44:43 | |
wrote a famous book about chance. | 0:44:43 | 0:44:46 | |
And for the ultimate metaphor of impenetrable uncertainty | 0:44:46 | 0:44:50 | |
he chose the British weather. | 0:44:50 | 0:44:52 | |
He wrote, "Is our expectation of rain, when we start out for a walk, | 0:44:56 | 0:45:01 | |
"always MORE likely than not, | 0:45:01 | 0:45:03 | |
"or LESS likely than not, or AS likely as not? | 0:45:03 | 0:45:06 | |
"I am prepared to argue that on some occasions none of these alternatives hold, | 0:45:06 | 0:45:12 | |
"and that it will be an arbitrary matter | 0:45:12 | 0:45:15 | |
"to decide for or against the umbrella." | 0:45:15 | 0:45:17 | |
But we want certainty. | 0:45:20 | 0:45:22 | |
And so we demand it from our weather forecasters. | 0:45:22 | 0:45:26 | |
And then after wet weekends and washed-out holidays | 0:45:26 | 0:45:29 | |
we blame the poor old forecasters for getting it wrong. | 0:45:29 | 0:45:33 | |
Hello, it was a disappointing day in many places | 0:45:33 | 0:45:37 | |
and I'm optimistic it's going to be a better day for most of us tomorrow. | 0:45:37 | 0:45:41 | |
Britain's most famously wrong weather forecast | 0:45:41 | 0:45:45 | |
was on 15th October, 1987. | 0:45:45 | 0:45:47 | |
Good afternoon. Earlier on today, | 0:45:47 | 0:45:49 | |
a woman rang the BBC and said she heard a hurricane was on the way. | 0:45:49 | 0:45:53 | |
Well, don't worry, there isn't. | 0:45:53 | 0:45:55 | |
But there was! | 0:45:55 | 0:45:57 | |
That night England was lashed by the strongest winds | 0:45:57 | 0:46:01 | |
for almost 300 years. | 0:46:01 | 0:46:03 | |
NEWS: Southern England suffered the full fury of the freak hurricane force winds, | 0:46:03 | 0:46:07 | |
in their wake, a trail of devastation, | 0:46:07 | 0:46:09 | |
the worst damage to property since the Second World War. | 0:46:09 | 0:46:12 | |
Nowhere escaped unscathed. | 0:46:12 | 0:46:14 | |
Today the most advanced meteorologists don't try making predictions | 0:46:18 | 0:46:22 | |
like Michael Fish did. | 0:46:22 | 0:46:24 | |
In Reading at the European Centre for Medium-Range Weather Forecasts, | 0:46:24 | 0:46:28 | |
they use a form of Monte Carlo method | 0:46:28 | 0:46:32 | |
to make forecasts using probabilities instead. | 0:46:32 | 0:46:36 | |
To show why they do this, | 0:46:36 | 0:46:38 | |
they've revisited the same weather data Michael Fish had in 1987. | 0:46:38 | 0:46:43 | |
What this shows us is that October '87 was an exceptionally | 0:46:54 | 0:46:58 | |
unpredictable and exceptionally chaotic situation | 0:46:58 | 0:47:03 | |
and so it was always going to be impossible to make a precise, deterministic forecast. | 0:47:03 | 0:47:08 | |
Weather forecasts go wrong because even small errors | 0:47:09 | 0:47:14 | |
at the beginning can grow into huge differences after just a few days. | 0:47:14 | 0:47:18 | |
And that's as true for everyday weather as it is for hurricanes. | 0:47:18 | 0:47:22 | |
To tackle the problem, Tim Palmer and his colleagues | 0:47:25 | 0:47:28 | |
routinely compute 50 different forecasts, | 0:47:28 | 0:47:30 | |
each with slightly varying starting points to reflect the uncertainty. | 0:47:30 | 0:47:35 | |
Before returning to the hurricane, Tim shows us an everyday example. | 0:47:35 | 0:47:40 | |
So we're looking at today's weather forecast | 0:47:40 | 0:47:44 | |
right at the beginning of the forecast period. | 0:47:44 | 0:47:47 | |
These are all basically giving the same type of weather. | 0:47:47 | 0:47:50 | |
A weather forecaster would look at these pressure maps and say, | 0:47:50 | 0:47:53 | |
"There's a northwesterly airstream coming down over the UK, | 0:47:53 | 0:47:57 | |
it's giving us slightly cool temperatures, | 0:47:57 | 0:48:00 | |
but fundamentally it's exactly the same no matter which of these 50 forecasts you're looking at. | 0:48:00 | 0:48:05 | |
Taking the same set of forecasts to three days in the future, | 0:48:05 | 0:48:09 | |
it's a different story. | 0:48:09 | 0:48:12 | |
Now there are discernible differences. | 0:48:12 | 0:48:14 | |
For example, member 14 has a stronger wind, there are tighter gradients | 0:48:14 | 0:48:18 | |
in the pressure than member 15 and that's telling us that | 0:48:18 | 0:48:22 | |
although we can be certain of the general direction of the wind, it's coming from the northwest, | 0:48:22 | 0:48:27 | |
the strength of the wind we cannot be so certain about. | 0:48:27 | 0:48:30 | |
So we have to make a prediction in probabilistic terms. | 0:48:30 | 0:48:33 | |
To work out the probabilities, Tim counts how many | 0:48:33 | 0:48:37 | |
of the three-day forecasts show a particular kind of weather. | 0:48:37 | 0:48:40 | |
It turns out that in about 30% of the forecasts | 0:48:42 | 0:48:45 | |
there are gale force winds over much of England. | 0:48:45 | 0:48:48 | |
Similarly rainfall, we find across much of England about 30%. | 0:48:48 | 0:48:52 | |
What this DOESN'T mean is that it's raining for 30% of the day. | 0:48:52 | 0:48:57 | |
What it means is that over the 50 possible futures, | 0:48:57 | 0:49:01 | |
in 30% of them it is raining. | 0:49:01 | 0:49:04 | |
So what can Tim see using the new method with the 1987 hurricane data? | 0:49:04 | 0:49:10 | |
There's around a 20 to 30% probability | 0:49:10 | 0:49:13 | |
over parts of southern England of hurricane force winds. | 0:49:13 | 0:49:17 | |
Now, the probability normally of hurricane force winds | 0:49:17 | 0:49:20 | |
in southern England is negligibly small, | 0:49:20 | 0:49:23 | |
so even though there's a divergence of solutions, there's real information here. | 0:49:23 | 0:49:28 | |
Adapting the Monte Carlo method and embracing chance | 0:49:28 | 0:49:32 | |
gives much better results. | 0:49:32 | 0:49:34 | |
But in Britain the forecasts most of us see don't give us | 0:49:34 | 0:49:37 | |
this kind of information yet. | 0:49:37 | 0:49:39 | |
We should now be trying to get this type of information out on the daily weather forecast. | 0:49:40 | 0:49:45 | |
And indeed I think it will enhance | 0:49:45 | 0:49:47 | |
the credibility of meteorologists themselves to be able to say | 0:49:47 | 0:49:51 | |
not only is weather forecasting an uncertain science, | 0:49:51 | 0:49:56 | |
but we can actually quantify the uncertainty in a very precise way. | 0:49:56 | 0:50:00 | |
If you were a cynic, you might think that weather forecasters | 0:50:11 | 0:50:15 | |
who give you probabilities and not predictions are just | 0:50:15 | 0:50:19 | |
hedging their bets, ducking out of doing the one thing they're supposed to | 0:50:19 | 0:50:23 | |
so they can never be accused of being wrong again. | 0:50:23 | 0:50:27 | |
But I don't agree. | 0:50:27 | 0:50:28 | |
Better a reliable probability than a wrong prediction. | 0:50:28 | 0:50:32 | |
And knowing the probabilities we can all make our own decisions. | 0:50:32 | 0:50:36 | |
THUNDER CLAPS | 0:50:36 | 0:50:38 | |
Like to bring that umbrella. | 0:50:40 | 0:50:42 | |
Remember that San Francisco probability? | 0:50:53 | 0:50:56 | |
A 40 to 80% chance of an earthquake? | 0:50:56 | 0:51:00 | |
In 1906, the city's worst-ever earthquake | 0:51:00 | 0:51:03 | |
killed 3,000 people | 0:51:03 | 0:51:06 | |
and destroyed almost 30,000 buildings. | 0:51:06 | 0:51:09 | |
Even if a similar catastrophe in the future can't be predicted, | 0:51:12 | 0:51:16 | |
it certainly can't be ignored. | 0:51:16 | 0:51:18 | |
So today scientists are applying new mathematical methods to the problem. | 0:51:19 | 0:51:24 | |
They're computing probabilities literally building by building, | 0:51:25 | 0:51:29 | |
so bold decisions can be taken about what to do. | 0:51:29 | 0:51:32 | |
In Berkeley, across the bay from San Francisco, | 0:51:39 | 0:51:42 | |
one major fault runs right across the pitch | 0:51:42 | 0:51:46 | |
of the California Memorial Stadium, | 0:51:46 | 0:51:49 | |
home of the Golden Bears Football Team. | 0:51:49 | 0:51:52 | |
They're rebuilding the stadium at a cost of over 200 million dollars. | 0:51:52 | 0:51:57 | |
The fault starts | 0:51:57 | 0:51:58 | |
just to the west of the south scoreboard, | 0:51:58 | 0:52:01 | |
and you can see in the bowl | 0:52:01 | 0:52:04 | |
there are those double stair-step curves at two points, | 0:52:04 | 0:52:08 | |
-that's where our joints are for that piece of the stadium. -Right. | 0:52:08 | 0:52:12 | |
It allows this part of the building to move independently | 0:52:12 | 0:52:16 | |
in an earthquake from the two sides of the stadium on either side of it. | 0:52:16 | 0:52:20 | |
-Right. -The base of the entire part of that building is on layers of sand | 0:52:20 | 0:52:25 | |
-and high density polyethylene plastic. -That's amazing. | 0:52:25 | 0:52:28 | |
It allows that part of the building to move a little easier than it would otherwise, | 0:52:28 | 0:52:33 | |
so when the ground moves six feet horizontal and two feet vertical, | 0:52:33 | 0:52:37 | |
it can just go along for the ride and the rest of the stadium is protected. | 0:52:37 | 0:52:41 | |
The stadium is just one part of a massive building | 0:52:42 | 0:52:46 | |
and strengthening programme all round San Francisco Bay. | 0:52:46 | 0:52:49 | |
A colossal 30 billion has been committed in total. | 0:52:50 | 0:52:54 | |
Will it be enough? They can only hope so. | 0:52:54 | 0:52:57 | |
Even if we knew exactly what earthquake is going to occur, | 0:53:00 | 0:53:03 | |
we may not know exactly how strong the shaking will be | 0:53:03 | 0:53:06 | |
and how it will vary across the city because of different soil types. | 0:53:06 | 0:53:10 | |
So you set a standard, you agree the buildings will be built to that | 0:53:10 | 0:53:15 | |
and then you hope that that's good enough. | 0:53:15 | 0:53:17 | |
You can't actually engineer chance out of the system altogether. | 0:53:17 | 0:53:21 | |
At least in San Francisco they've a good idea of what to expect, | 0:53:24 | 0:53:29 | |
even if they can't know exactly. | 0:53:29 | 0:53:31 | |
But there's one last sting in the tail. | 0:53:31 | 0:53:33 | |
Chance can sometimes come up with something you never even thought of. | 0:53:33 | 0:53:38 | |
As we know, there are known knowns, | 0:53:39 | 0:53:42 | |
there are things we know we know. | 0:53:42 | 0:53:44 | |
We also know there are known unknowns. | 0:53:44 | 0:53:47 | |
That is to say we know there are some things we do not know. | 0:53:47 | 0:53:50 | |
But there are also unknown unknowns, | 0:53:50 | 0:53:53 | |
the ones we don't know we don't know. | 0:53:53 | 0:53:55 | |
And if one looks throughout the history of our country and other free countries, | 0:53:55 | 0:53:59 | |
it is the latter category that tend to be the difficult ones. | 0:53:59 | 0:54:03 | |
Donald Rumsfeld may have just been trying to excuse an unfolding disaster in Iraq. | 0:54:03 | 0:54:09 | |
But "unknown unknowns" are a real and profound challenge for us all. | 0:54:09 | 0:54:13 | |
And don't we just know it. | 0:54:14 | 0:54:16 | |
The Bank of England is the rock-solid institution | 0:54:21 | 0:54:26 | |
to which we all turn in these turbulent times. | 0:54:26 | 0:54:29 | |
Surely I can find some certainty here? | 0:54:30 | 0:54:33 | |
I'm meeting Spencer Dale. | 0:54:37 | 0:54:40 | |
The Bank of England is the main financial institution in the country. | 0:54:51 | 0:54:55 | |
People want it to tell them what's going on in the economy, but can you predict what's going to happen? | 0:54:55 | 0:55:00 | |
Unfortunately not. Forecasting the economy is very difficult to do, | 0:55:00 | 0:55:04 | |
in part because the economy is very large and complex | 0:55:04 | 0:55:08 | |
and it's made even more difficult because it depends on people | 0:55:08 | 0:55:13 | |
and their decisions and that makes trying to model behaviour | 0:55:13 | 0:55:17 | |
and how the economy is going to change over time even more difficult. | 0:55:17 | 0:55:21 | |
Every quarter, the Bank makes a forecast for the nation | 0:55:23 | 0:55:26 | |
in the form of what it calls a "fan chart". | 0:55:26 | 0:55:29 | |
And it deliberately builds in uncertainty. | 0:55:29 | 0:55:31 | |
The chart shows that Britain's future economic growth | 0:55:33 | 0:55:36 | |
might have a 5% chance of lying in each one of the shaded bands. | 0:55:36 | 0:55:41 | |
This was the Bank's chart from 2007, | 0:55:42 | 0:55:46 | |
just before the big crash. | 0:55:46 | 0:55:48 | |
At the time we made this forecast, | 0:55:48 | 0:55:50 | |
we thought in three years' time | 0:55:50 | 0:55:52 | |
the annual growth of the economy may be anywhere | 0:55:52 | 0:55:55 | |
between 5% or close to zero. | 0:55:55 | 0:55:58 | |
But the Bank is even less certain than that. | 0:55:58 | 0:56:02 | |
It also leaves room for the unknown unknowns. | 0:56:02 | 0:56:05 | |
This only shows 90% of probability. | 0:56:05 | 0:56:08 | |
So it's shows you 90 times out of 100 we think the economy will go somewhere in this range. | 0:56:08 | 0:56:13 | |
So there's a one-in-ten chance it could just do anything? | 0:56:13 | 0:56:16 | |
There's a one-in-ten chance it will fall outside of this fan chart. | 0:56:16 | 0:56:20 | |
We don't try and put precise probabilities on those very extreme outcomes. | 0:56:20 | 0:56:25 | |
With these charts, the Bank is making one thing clear - | 0:56:27 | 0:56:31 | |
we must expect the unexpected. | 0:56:31 | 0:56:33 | |
And soon after the Bank made this chart, chance struck. | 0:56:34 | 0:56:38 | |
It was a genuinely surprising event, the economy to behave in a way | 0:56:40 | 0:56:43 | |
which we hadn't seen for almost an entire generation. | 0:56:43 | 0:56:47 | |
The environment which we operate in is inherently uncertain, | 0:56:47 | 0:56:50 | |
the future is uncertain | 0:56:50 | 0:56:53 | |
and the impact of our decisions are often very uncertain. | 0:56:53 | 0:56:56 | |
Some people might want to hammer the Bank of England | 0:56:58 | 0:57:02 | |
for not knowing what's around the corner. | 0:57:02 | 0:57:04 | |
But you can't blame them for the nature of chance. | 0:57:04 | 0:57:07 | |
And though the Bank can't give us the information we want, | 0:57:07 | 0:57:11 | |
I think they show the way to the wisdom we need. | 0:57:11 | 0:57:14 | |
There's just no use in looking for absolute certainty. | 0:57:20 | 0:57:24 | |
We can never rely on predictions. | 0:57:24 | 0:57:27 | |
We can tame chance, but only up to a point. | 0:57:29 | 0:57:33 | |
Putting numbers on chance is a powerful way | 0:57:34 | 0:57:37 | |
to get a handle on the future. | 0:57:37 | 0:57:40 | |
But these numbers can only ever be as good | 0:57:40 | 0:57:43 | |
as the information we have to hand. | 0:57:43 | 0:57:46 | |
Though we try to measure reality with precision, | 0:57:46 | 0:57:49 | |
sometimes they're little more than guesses. | 0:57:49 | 0:57:52 | |
What all this means is that uncertainty | 0:57:52 | 0:57:55 | |
is an essential part of being alive. | 0:57:55 | 0:57:58 | |
And whether our uncertainty | 0:57:58 | 0:58:00 | |
ultimately comes from out there or in here | 0:58:00 | 0:58:03 | |
won't, in the end, matter, | 0:58:03 | 0:58:05 | |
because either way surprises will most certainly happen. | 0:58:05 | 0:58:10 | |
For instance, in this year of the Diamond Jubilee, | 0:58:10 | 0:58:12 | |
I found a chicken nugget in the shape of Her Majesty the Queen! | 0:58:12 | 0:58:17 | |
What's the chances of that? | 0:58:17 | 0:58:19 | |
Subtitles by Red Bee Media Ltd | 0:58:53 | 0:58:56 |