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For as long as human beings
have walked upon earth,
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00:00:07,400 --> 00:00:09,400
we've tried to make sense
of our world
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00:00:09,400 --> 00:00:12,280
and predict
what the future will bring.
4
00:00:18,920 --> 00:00:20,080
Yet today,
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00:00:20,080 --> 00:00:23,920
our lives seem more complicated
and unpredictable than ever.
6
00:00:25,600 --> 00:00:27,400
And half the population
of the planet
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00:00:27,400 --> 00:00:30,520
now live in busy, sprawling cities.
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00:00:31,920 --> 00:00:35,440
Every day throws up
thousands of different encounters.
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00:00:35,440 --> 00:00:39,640
A mass of interactions and forces
that seem beyond our control.
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00:00:39,640 --> 00:00:41,400
WOMAN LAUGHS
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00:00:41,400 --> 00:00:45,520
It's hard to see how
any of this could be connected.
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00:00:45,520 --> 00:00:46,840
BABY CRIES
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00:00:47,880 --> 00:00:51,880
Yet when we start to look
closely at all this complexity,
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00:00:51,880 --> 00:00:54,920
surprising patterns begin to emerge.
15
00:00:59,840 --> 00:01:03,800
It's these patterns that I believe
point to an underlying Code
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00:01:03,800 --> 00:01:06,080
at the very heart of existence
17
00:01:06,080 --> 00:01:10,480
that controls not only our world
and everything in it, but even us.
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00:01:46,640 --> 00:01:51,920
As a mathematician,
I'm fascinated by the patterns
we see all around us.
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00:01:55,680 --> 00:01:59,480
Patterns that reflect the hidden
connections between everything.
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00:02:01,240 --> 00:02:04,720
From the movement
of rush hour crowds...
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00:02:07,320 --> 00:02:10,600
..to the shifting shape
of a flock of starlings.
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00:02:13,400 --> 00:02:17,040
The cacophony
of a billion Internet searches...
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00:02:17,040 --> 00:02:19,760
and the vagaries of the weather.
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00:02:19,760 --> 00:02:21,800
THUNDER ROLLS
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00:02:26,920 --> 00:02:28,880
CHEERING
26
00:02:31,080 --> 00:02:34,640
Together, these patterns
and connections make up the Code.
27
00:02:36,920 --> 00:02:41,400
A model of our world that describes
not only how it works,
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00:02:41,400 --> 00:02:44,760
but can also predict
what our future holds.
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00:03:00,520 --> 00:03:05,040
Around 500 years ago, a ship
was caught in a terrible storm.
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00:03:05,040 --> 00:03:06,600
As rain lashed the decks
31
00:03:06,600 --> 00:03:09,400
and gale force winds tore
through the rigging,
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00:03:09,400 --> 00:03:11,280
the ship began to take on water.
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00:03:11,280 --> 00:03:16,480
The captain had no choice but to run
his ship aground and wait for help.
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00:03:19,440 --> 00:03:23,480
But help never arrived,
and the natives were hostile.
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00:03:26,000 --> 00:03:30,120
After eight long months, and with
his crew facing certain starvation,
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00:03:30,120 --> 00:03:33,520
the captain came up
with an ingenious plan.
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00:03:33,520 --> 00:03:37,240
He summoned the local chief
and told him his God was angry.
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00:03:38,240 --> 00:03:42,480
So angry, in fact,
that if they didn't bring supplies
within three days,
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00:03:42,480 --> 00:03:44,760
God would swallow the moon.
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00:03:50,600 --> 00:03:56,400
And sure enough,
as the moon rose on the third night,
it had already begun to disappear.
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00:04:04,360 --> 00:04:08,360
Terrified, the locals ran from
all directions towards the ship,
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00:04:08,360 --> 00:04:09,960
laden with provisions.
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00:04:16,680 --> 00:04:19,920
The year was 1504, and the captain?
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00:04:19,920 --> 00:04:21,880
Christopher Columbus.
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00:04:21,880 --> 00:04:25,360
And the reason he was apparently
able to command the heavens
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00:04:25,360 --> 00:04:28,560
was because he had
something like this.
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00:04:31,200 --> 00:04:32,680
It's a set of lunar tables.
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00:04:32,680 --> 00:04:37,120
And each one of these numbers
represents a lunar eclipse.
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00:04:37,120 --> 00:04:43,000
Today's date is June 15th, and it
says that in about five hours' time
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00:04:43,000 --> 00:04:47,240
the same thing is going to happen
to the moon here in Cyprus.
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00:04:53,040 --> 00:04:57,600
During a lunar eclipse, the earth
passes between the sun and the moon,
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casting its shadow
across the lunar surface.
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00:05:09,000 --> 00:05:10,760
And there it goes.
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00:05:10,760 --> 00:05:15,120
The moon has been swallowed up
by the shadow of the Earth.
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00:05:15,120 --> 00:05:19,840
But the amazing thing is
actually the moon doesn't
completely disappear, cos...
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00:05:19,840 --> 00:05:24,960
there's a kind of...
red, ghostly moon up there.
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00:05:24,960 --> 00:05:30,200
And that's because
the light from the sun is being
refracted around the Earth.
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00:05:31,840 --> 00:05:33,440
Really quite spooky.
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00:05:38,440 --> 00:05:44,680
I can imagine how terrified
the islanders would have been
when they saw that 500 years ago.
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00:05:44,680 --> 00:05:49,600
And the only explanation for them
would have been that the gods
really were angry with them.
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00:05:57,800 --> 00:06:03,160
We now know that the movement of the
planets is incredibly predictable.
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00:06:03,160 --> 00:06:08,720
By understanding the Code,
we can model their orbits
far back into the past.
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00:06:08,720 --> 00:06:12,160
And see thousands of years
into the future.
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00:06:21,080 --> 00:06:25,240
It's thanks to the Code that we're
no longer frightened by an eclipse.
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00:06:25,240 --> 00:06:28,640
In fact, the Code
is such a powerful thing
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00:06:28,640 --> 00:06:32,280
that I'm even prepared
to entrust my life to it.
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00:06:54,120 --> 00:06:58,840
This strange contraption
is five and a half metres high.
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00:06:59,840 --> 00:07:02,120
Using the force of gravity,
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00:07:02,120 --> 00:07:07,800
a 30-kilogram ball will hurtle down
the ramp and fire off the end.
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00:07:07,800 --> 00:07:13,000
And when it does, I will be
sitting directly in its path.
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00:07:13,000 --> 00:07:16,960
If I get my sums wrong,
I'll be killed outright.
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00:07:23,520 --> 00:07:26,400
To calculate how far
the ball's going to go,
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00:07:26,400 --> 00:07:29,400
I need some
key measurements about the ramp.
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00:07:31,240 --> 00:07:35,720
Little h is 0.98 metres.
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00:07:35,720 --> 00:07:41,360
The angle is 49.1 degree.
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00:07:41,360 --> 00:07:43,640
So gravity, I know, on the Earth...
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is 9.8 metres per second squared.
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00:07:49,040 --> 00:07:53,080
Interestingly, you don't have
to know the weight of the ball,
the mass of the ball.
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00:07:53,080 --> 00:07:57,240
That's not relevant to how far
the thing's going to go.
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00:07:57,240 --> 00:08:02,080
Two times gravity,
times the height, 5.5,
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00:08:02,080 --> 00:08:06,120
multiplied by the speed,
divided by 49.1,
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take the cosine...
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That will give me
a distance of 9.95 metres.
84
00:08:12,480 --> 00:08:17,600
But we've got air resistance,
there's friction on the...
the ramp as well.
85
00:08:19,840 --> 00:08:22,920
What about the wind today? 9.16.
86
00:08:22,920 --> 00:08:28,520
OK, so the predicted distance
is going to be 5.6 metres.
87
00:08:31,520 --> 00:08:35,440
That's where I think
the ball is going to land.
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00:08:38,000 --> 00:08:44,600
Which means if I set up my deckchair
here, I should be able to watch
the whole thing in complete safety.
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00:08:44,600 --> 00:08:47,240
OK, release the ball.
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00:08:55,360 --> 00:08:58,880
And that is the power of the Code.
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00:09:02,000 --> 00:09:04,720
We can do this again
and again and again...
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..and the numbers mean
the ball is going to land
in the same place each time.
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00:09:18,480 --> 00:09:23,280
If everything in the world
behaved according to equations
that give definite answers,
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00:09:23,280 --> 00:09:26,160
we'd be able to predict the future
with absolute certainty.
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00:09:28,840 --> 00:09:32,560
But unfortunately
things aren't quite that simple.
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00:09:41,600 --> 00:09:44,840
The natural world often
appears so complex
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it's hard to imagine we could
write equations to describe it.
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00:09:50,160 --> 00:09:53,720
Even though we might glimpse
what we think are patterns,
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00:09:53,720 --> 00:09:56,760
they seem
almost impossible to understand.
100
00:09:56,760 --> 00:09:59,320
I've come to witness
a mysterious phenomenon
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00:09:59,320 --> 00:10:03,200
that happens here in Denmark
for a few short weeks every year.
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WINGS FLUTTER
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BIRDS TWITTER
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WINGS FLUTTER
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First few appearing, I think.
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These are starlings,
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00:10:38,600 --> 00:10:43,600
making their annual migration
between southern Europe
and Scandinavia.
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A single flock
can contain a million birds or more.
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Their dance obscures the fading
evening light,
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giving the formation
its eerie name -
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The Black Sun.
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00:11:02,800 --> 00:11:06,080
There's another
massive group coming in.
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Oh!
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There are
thousands of them up there.
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00:11:16,840 --> 00:11:19,200
It's not really clear why
they do this.
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00:11:19,200 --> 00:11:21,880
It's maybe like, kind of,
safety in numbers.
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00:11:21,880 --> 00:11:24,040
The whole shape
looks quite intimidating.
118
00:11:24,040 --> 00:11:26,840
It looks like
one large, black beast,
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00:11:26,840 --> 00:11:29,240
frightening off any predators
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00:11:29,240 --> 00:11:32,960
that might be looking
for a bit of dinner before sunset.
121
00:11:36,840 --> 00:11:38,160
Look at that.
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00:11:38,160 --> 00:11:40,440
Ah.
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00:11:40,440 --> 00:11:42,000
It's almost hypnotic.
124
00:11:47,360 --> 00:11:49,800
It's amazing.
There are so many of them,
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00:11:49,800 --> 00:11:52,480
it's a wonder they don't
smash into each other
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00:11:52,480 --> 00:11:55,440
and sort of knock some out of
the sky. But they don't seem to.
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Incredible synchronisation.
128
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Oh!
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00:12:05,000 --> 00:12:08,600
You're never quite sure
what it's going to do next.
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00:12:09,640 --> 00:12:12,680
'It's an almost
impossible achievement.
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00:12:12,680 --> 00:12:16,200
'How can each bird predict the
movements of thousands of others?'
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00:12:23,720 --> 00:12:25,360
That's extraordinary?
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00:12:33,520 --> 00:12:38,200
As strange as it seems, by reducing
each starling to numbers,
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00:12:38,200 --> 00:12:41,200
we can model what's happening
on a computer.
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00:12:45,200 --> 00:12:48,000
We start with a flock
of virtual starlings,
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00:12:48,000 --> 00:12:52,400
all flying at different speeds
and in different directions.
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00:12:52,400 --> 00:12:56,240
And then
we give them some simple rules.
138
00:12:56,240 --> 00:13:00,880
The first is for each bird
to fly at the same speed.
139
00:13:00,880 --> 00:13:04,360
The second rule
is to stay close to your neighbours.
140
00:13:06,520 --> 00:13:11,120
And finally, if you see a predator
nearby, get out of the way.
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00:13:14,120 --> 00:13:17,760
Three simple rules are all it takes
to create something
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that looks uncannily
like the movement of
a real flock of starlings.
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Oh, here they come.
144
00:13:26,880 --> 00:13:28,920
Oh!
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00:13:28,920 --> 00:13:30,320
HE LAUGHS
146
00:13:31,640 --> 00:13:34,320
In fact, a recent study has shown
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00:13:34,320 --> 00:13:38,040
that even in a flock of hundreds
of thousands of birds,
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each starling only has to keep track
of its seven nearest neighbours.
149
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And then...they've all gone.
150
00:13:55,440 --> 00:13:57,000
The sky's clear again.
151
00:14:02,560 --> 00:14:05,760
Who'd have thought that something
so extraordinarily complex
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00:14:05,760 --> 00:14:09,760
as a constantly shifting flock of
thousands of birds in flight
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can have at its heart such
a simple and elegant Code?
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WOMAN LAUGHS
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00:14:22,240 --> 00:14:23,480
CHILD LAUGHS
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00:14:27,080 --> 00:14:28,720
BABY CRIES
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00:14:28,720 --> 00:14:31,720
It seems inconceivable
that human beings
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00:14:31,720 --> 00:14:36,160
could ever be reduced to
a mathematical model like starlings.
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00:14:36,160 --> 00:14:38,520
CLOCK TICKS
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00:14:50,520 --> 00:14:54,360
But Iain Couzin studies
how animals behave in groups,
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and his research has revealed
some surprising parallels.
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00:14:59,320 --> 00:15:03,760
How can you possibly begin
to understand something
like this huge mass of people?
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00:15:03,760 --> 00:15:06,080
Even when you look at the crowd
for a few seconds,
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00:15:06,080 --> 00:15:09,560
you realise there's so many
complicated factors at play.
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00:15:09,560 --> 00:15:12,160
I started my research
looking at simple organisms,
166
00:15:12,160 --> 00:15:14,520
organisms like ant swarms,
schooling fish.
167
00:15:14,520 --> 00:15:17,800
And remarkably, our insights
from studying those systems
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00:15:17,800 --> 00:15:20,760
led to new insights
in studying human crowds.
169
00:15:20,760 --> 00:15:24,160
But people are much more complicated
than a...a fish or an ant.
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00:15:24,160 --> 00:15:26,960
Exactly, but that's almost
the beauty of this,
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is we're thinking
about more interesting things
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00:15:29,480 --> 00:15:33,760
when we're walking through crowds
than, "How do I avoid that person
and that obstacle?"
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00:15:33,760 --> 00:15:39,000
You know, we're thinking about
what we're going to cook for dinner
or what our friends are doing.
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00:15:39,000 --> 00:15:41,560
And so, in actual fact,
we're almost on auto-pilot,
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and we're actually using
very simple rules of interaction
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just like the schooling fish
and the swarming ants.
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00:15:50,240 --> 00:15:52,160
So can we learn things
from the ants?
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We could learn an huge amount
from the ants.
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00:15:54,240 --> 00:15:56,840
Ants don't suffer
from problems such as congestion.
180
00:15:56,840 --> 00:16:00,280
Because they're not selfish.
And I'm afraid to say we are.
181
00:16:00,280 --> 00:16:02,840
We want to minimise
our own travel time,
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00:16:02,840 --> 00:16:07,120
but we don't necessarily care
whether we do so at the expense
of other individuals.
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00:16:08,760 --> 00:16:11,800
Of all the animals Iain has studied,
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00:16:11,800 --> 00:16:15,160
human beings are, in some ways,
the most predictable.
185
00:16:15,160 --> 00:16:20,320
We walk at an optimum speed
of 1.3 metres per second,
186
00:16:20,320 --> 00:16:24,520
and prefer to walk in straight lines
to get to our destination.
187
00:16:25,840 --> 00:16:28,280
What happens
is you will naturally fall
188
00:16:28,280 --> 00:16:31,600
into the slipstream of someone
moving in the same direction as you.
189
00:16:31,600 --> 00:16:35,040
And so without you even knowing it,
you're forming a lane.
190
00:16:35,040 --> 00:16:40,240
Similarly, pedestrians moving in the
other direction will also form lanes,
191
00:16:40,240 --> 00:16:42,200
very much like the ants do.
192
00:16:42,200 --> 00:16:45,280
These lanes help us
to avoid collisions.
193
00:16:45,280 --> 00:16:47,080
However, in a large open space,
194
00:16:47,080 --> 00:16:49,880
like the concourse
at Grand Central Station,
195
00:16:49,880 --> 00:16:52,160
the lanes inevitably
cross each other,
196
00:16:52,160 --> 00:16:53,800
which could lead to congestion.
197
00:16:55,800 --> 00:16:59,320
But when you put an obstacle -
like this information desk -
198
00:16:59,320 --> 00:17:03,200
in the middle of the crowd,
rather than getting in the way,
199
00:17:03,200 --> 00:17:06,000
it acts like a roundabout
200
00:17:06,000 --> 00:17:10,160
and increases the flow through
the station by as much as 13%.
201
00:17:17,960 --> 00:17:21,120
These rules are so effective
at predicting what we'll do,
202
00:17:21,120 --> 00:17:25,480
they can even be used to
simulate crowds of people.
203
00:17:27,600 --> 00:17:31,920
Each individual is actually
described by a set of numbers
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00:17:31,920 --> 00:17:34,000
as they move through an environment.
205
00:17:34,000 --> 00:17:37,840
Exactly. We're capturing the average
type of behaviour of pedestrians.
206
00:17:37,840 --> 00:17:41,880
We're capturing these
simple and local rules that people
use within crowds
207
00:17:41,880 --> 00:17:44,600
to then make predictions
as to how the whole crowd
208
00:17:44,600 --> 00:17:47,120
is going to flow through
different environments.
209
00:17:48,760 --> 00:17:51,840
We can use this underlying
Code of the crowd
210
00:17:51,840 --> 00:17:56,040
to design buildings
that are more efficient and safer.
211
00:17:57,440 --> 00:18:01,040
Simulations like these
are able to accurately predict
212
00:18:01,040 --> 00:18:03,920
how quickly a building
can be evacuated,
213
00:18:03,920 --> 00:18:06,360
even before it has been built.
214
00:18:13,640 --> 00:18:17,680
As a crowd,
people are incredibly predictable.
215
00:18:17,680 --> 00:18:21,560
There are simple rules that we
follow without being aware of it.
216
00:18:24,400 --> 00:18:27,280
But most of the time,
we don't live on autopilot.
217
00:18:28,840 --> 00:18:34,080
And when the crowd disperses, so too
do the rules of group behaviour.
218
00:18:34,080 --> 00:18:35,640
SIREN BLARES
219
00:18:35,640 --> 00:18:41,040
As individuals with our own free
will, we're much harder to predict.
220
00:18:41,040 --> 00:18:42,480
Or so we think.
221
00:18:49,280 --> 00:18:52,160
Before we gets started,
I would like to mention the rules.
222
00:18:52,160 --> 00:18:53,360
They are very simple.
223
00:18:53,360 --> 00:18:56,320
There are three throws
and there are only three throws.
224
00:18:56,320 --> 00:19:00,560
We use a three-prime shoot,
which means you go one, two, three
225
00:19:00,560 --> 00:19:03,400
and you release your throw on four.
226
00:19:04,520 --> 00:19:06,840
A throw of rock is a closed fist.
227
00:19:06,840 --> 00:19:10,120
You can throw it any way you want
as long as it is a closed fist.
228
00:19:10,120 --> 00:19:11,720
Your paper must be horizontal.
229
00:19:11,720 --> 00:19:13,760
Your scissors must be vertical.
230
00:19:13,760 --> 00:19:15,920
That will be foul.
231
00:19:18,200 --> 00:19:22,320
The game of rock, paper, scissors
is known all over the world.
232
00:19:24,120 --> 00:19:27,160
And some people
take it very seriously.
233
00:19:27,160 --> 00:19:30,080
For those of you who don't know,
and there should be very few,
234
00:19:30,080 --> 00:19:32,840
the throw of paper
covers the throw of rock.
235
00:19:32,840 --> 00:19:35,160
The throw of scissors
cuts the throw of paper,
236
00:19:35,160 --> 00:19:37,760
and the throw of rock
crushes the throw of scissors.
237
00:19:42,400 --> 00:19:45,280
In Philadelphia,
the Rock, Paper, Scissors League
238
00:19:45,280 --> 00:19:47,320
competes four times a week.
239
00:19:48,760 --> 00:19:51,240
The people in this room
are fighting
240
00:19:51,240 --> 00:19:54,240
to go to the world championship
in Las Vegas
241
00:19:54,240 --> 00:19:57,400
and the chance to win 10,000.
242
00:20:03,400 --> 00:20:06,480
Sweetji in the lead.
Rock versus scissors for Sweetji.
243
00:20:06,480 --> 00:20:09,800
You're on the verge of elimination,
Drew Bag.
244
00:20:09,800 --> 00:20:12,240
Third and final set,
winner moves on.
245
00:20:12,240 --> 00:20:13,720
THE CROWD CHANTS AND CLAPS
246
00:20:13,720 --> 00:20:15,640
Rock versus scissors.
247
00:20:15,640 --> 00:20:18,480
And what a match,
to take us down to the final four.
248
00:20:20,280 --> 00:20:24,600
The intriguing thing about this game
is that it should be impossible
249
00:20:24,600 --> 00:20:27,800
to predict what
your opponent's going to do next.
250
00:20:28,960 --> 00:20:32,120
In rock, paper, scissors,
they're all pretty much equivalent.
251
00:20:32,120 --> 00:20:36,080
So each throw beats one
and loses to another,
252
00:20:36,080 --> 00:20:38,640
so essentially
it's a game of even odds.
253
00:20:38,640 --> 00:20:40,320
A bit like a flip of a coin.
254
00:20:42,360 --> 00:20:44,640
But if the game is entirely random,
255
00:20:44,640 --> 00:20:47,560
every player
would be evenly matched.
256
00:20:47,560 --> 00:20:51,120
And yet some people win time
and time again.
257
00:20:51,120 --> 00:20:52,840
It is match point, Sweetji.
258
00:20:52,840 --> 00:20:55,880
B-Pac has no points here
in round number two.
259
00:20:55,880 --> 00:20:58,040
He will need two straight throws.
260
00:20:58,040 --> 00:20:59,600
Can he get through number one?
261
00:20:59,600 --> 00:21:01,560
No. Sweetji!
262
00:21:01,560 --> 00:21:04,640
So now our final match of the night.
263
00:21:04,640 --> 00:21:08,280
Sweetji,
you're going to play dOGulas.
264
00:21:08,280 --> 00:21:12,720
The more we play, the more we're
influenced by our past throws.
265
00:21:12,720 --> 00:21:13,920
Begin.
266
00:21:13,920 --> 00:21:17,480
And that creates patterns that
can be exploited to win the game.
267
00:21:17,480 --> 00:21:21,200
Sweetji came fifth
in the league last year,
268
00:21:21,200 --> 00:21:24,920
and this season
looks set to do even better.
269
00:21:28,440 --> 00:21:30,480
dOGulas!
270
00:21:30,480 --> 00:21:32,040
Rock crushes scissors.
271
00:21:32,040 --> 00:21:35,080
Sweetji still has point...
272
00:21:35,080 --> 00:21:36,720
Rock crushes scissors!
273
00:21:36,720 --> 00:21:37,760
SHE SCREAMS
274
00:21:37,760 --> 00:21:42,440
Sweetji, Philadelphia Rock, Paper,
Scissors City League Champion
here at the Raven Lounge.
275
00:21:42,440 --> 00:21:44,400
Congratulations. Thank you.
276
00:21:44,400 --> 00:21:47,480
So that was five consecutive wins.
277
00:21:47,480 --> 00:21:52,000
What was the key to your success,
do you think? I try to read people.
278
00:21:52,000 --> 00:21:55,240
Yeah, you do, yeah? Or at least try
to think what they're thinking.
279
00:21:55,240 --> 00:21:58,480
You're looking for their patterns
then? Yeah, a little bit like...
280
00:21:58,480 --> 00:22:02,280
Their patterns,
and they'll be trying to learn mine
and go against that.
281
00:22:06,520 --> 00:22:09,720
Rock, paper, scissors reveals
a fundamental truth
282
00:22:09,720 --> 00:22:11,120
about human nature.
283
00:22:13,040 --> 00:22:15,400
We are so addicted to patterns
284
00:22:15,400 --> 00:22:17,920
that we let them seep into
almost everything we do.
285
00:22:21,480 --> 00:22:23,400
And these patterns are the key
286
00:22:23,400 --> 00:22:26,320
to predicting many aspects
of our behaviour.
287
00:22:26,320 --> 00:22:28,320
Even the darkest parts
of our nature.
288
00:22:32,800 --> 00:22:35,840
SCREAMS
289
00:22:35,840 --> 00:22:38,600
Deceased. Female, five foot two.
290
00:22:38,600 --> 00:22:42,200
Complexion, dark. Eyes, brown.
Hair, brown.
291
00:22:44,640 --> 00:22:48,240
When you see this much activity
in such a small geographic area
292
00:22:48,240 --> 00:22:49,800
in such a tight time frame,
293
00:22:49,800 --> 00:22:52,200
that's a warning bell
that something's going on,
294
00:22:52,200 --> 00:22:53,760
we have a predator operating.
295
00:22:55,280 --> 00:23:00,760
Kim Rossmo has 20 years'
experience as a Detective Inspector.
296
00:23:00,760 --> 00:23:04,440
He specialises
in hunting down serial killers.
297
00:23:06,200 --> 00:23:08,680
The victim's body was found
here in the corner
298
00:23:08,680 --> 00:23:12,760
by a police officer that came in
shortly after the crime had occurred.
299
00:23:12,760 --> 00:23:14,760
The prime crime scene would be...
300
00:23:15,960 --> 00:23:17,800
But Rossmo is no ordinary cop,
301
00:23:17,800 --> 00:23:20,640
because he's got a PhD
302
00:23:20,640 --> 00:23:25,440
and uses mathematics to understand
the patterns criminals leave behind.
303
00:23:29,720 --> 00:23:32,560
There's a logic in how
the offender hunted for the victim
304
00:23:32,560 --> 00:23:35,000
and the location where
he committed the crime.
305
00:23:35,000 --> 00:23:38,120
If we can decode that and if
we can understand that pattern,
306
00:23:38,120 --> 00:23:41,680
we can use that information to help
us focus a criminal investigation.
307
00:23:44,200 --> 00:23:51,240
The reason it's so hard to catch
serial killers is because there's
often no link to their crimes.
308
00:23:51,240 --> 00:23:53,400
They kill random strangers
309
00:23:53,400 --> 00:23:56,920
in locations they have
no obvious connection to.
310
00:23:56,920 --> 00:23:59,960
It's very common in the investigation
of a serial murder case
311
00:23:59,960 --> 00:24:03,520
to have hundreds, thousands,
even tens of thousands of suspects.
312
00:24:03,520 --> 00:24:05,520
It's a needle-in-a-haystack problem.
313
00:24:07,600 --> 00:24:09,160
Where do you start?
314
00:24:10,320 --> 00:24:15,680
In 1888, the most notorious serial
killer of all, Jack the Ripper,
315
00:24:15,680 --> 00:24:18,840
killed five women
in London's East End.
316
00:24:19,920 --> 00:24:25,720
Since then, countless people
have tried to solve the mystery
of the Ripper's identity.
317
00:24:25,720 --> 00:24:28,360
But Rossmo thinks he
could have tracked him down
318
00:24:28,360 --> 00:24:30,200
without seeing a scrap of evidence.
319
00:24:31,560 --> 00:24:35,280
Because he's worked out where
Jack the Ripper most likely lived.
320
00:24:35,280 --> 00:24:38,800
Based only
on the location of the crimes.
321
00:24:38,800 --> 00:24:44,400
Flower and Dean Street should have
been the epicentre of their search.
322
00:24:44,400 --> 00:24:47,640
And all he used to do it
is an equation.
323
00:24:51,200 --> 00:24:52,680
Inherently, we're all lazy,
324
00:24:52,680 --> 00:24:55,560
and criminals just
as much as anyone else.
325
00:24:55,560 --> 00:24:59,200
They want to accomplish
their goals close to home
rather than further away,
326
00:24:59,200 --> 00:25:02,760
because it involves too much effort,
too much time, too much travel.
327
00:25:04,360 --> 00:25:06,400
The first half of Rossmo's equation
328
00:25:06,400 --> 00:25:09,920
models what's known as
the least-effort principle.
329
00:25:09,920 --> 00:25:12,040
It means that the crime locations
330
00:25:12,040 --> 00:25:17,080
are statistically more likely
the nearer they are
to where the offender lives.
331
00:25:17,080 --> 00:25:20,240
If you have a choice of going to
the corner store for a loaf of bread
332
00:25:20,240 --> 00:25:23,680
or one that's seven miles down the
road, you'll pick the corner store.
333
00:25:23,680 --> 00:25:27,160
It seems a bit gruesome to apply
the same thing to a serial killer
334
00:25:27,160 --> 00:25:29,960
as to going and buying
a loaf of bread or milk.
335
00:25:29,960 --> 00:25:34,960
Well, actually, if we can get over
the horrible nature of these crimes
336
00:25:34,960 --> 00:25:38,560
and recognise that these are
human beings like the rest of us,
337
00:25:38,560 --> 00:25:41,640
we can,
because we understand ourselves,
338
00:25:41,640 --> 00:25:44,320
maybe bet some understanding
of these individuals.
339
00:25:46,080 --> 00:25:48,160
The second half of the equation
340
00:25:48,160 --> 00:25:51,080
describes something
called the buffer zone.
341
00:25:51,080 --> 00:25:54,440
Criminals avoid committing crimes
too close to home,
342
00:25:54,440 --> 00:25:56,800
for fear of
drawing attention to themselves.
343
00:25:58,320 --> 00:26:02,000
It's the interaction of these
two behaviours that allows Rossmo
344
00:26:02,000 --> 00:26:05,760
to calculate the most probable
location of the criminal.
345
00:26:05,760 --> 00:26:09,280
These individuals have to
not only obtain their target -
346
00:26:09,280 --> 00:26:10,480
or capture a victim -
347
00:26:10,480 --> 00:26:15,000
but avoid apprehension by the police
and identification by witnesses.
348
00:26:18,720 --> 00:26:21,840
The technique,
known as geographic profiling,
349
00:26:21,840 --> 00:26:25,000
is now used by police
all over the world.
350
00:26:30,720 --> 00:26:34,160
Police are examining the possibility
that a small explosion
351
00:26:34,160 --> 00:26:36,960
near a branch of Barclays Bank
in West London
352
00:26:36,960 --> 00:26:38,960
was the work of an extortionist.
353
00:26:38,960 --> 00:26:40,280
Police believe the demand
354
00:26:40,280 --> 00:26:42,680
came from the blackmailer
known as Mardi Gra.
355
00:26:44,960 --> 00:26:48,600
In the late '90s, Rossmo
was called in by Scotland Yard
356
00:26:48,600 --> 00:26:52,400
to help catch
the notorious Mardi Gra bomber,
357
00:26:52,400 --> 00:26:55,600
who for three years
waged a campaign of terror
358
00:26:55,600 --> 00:26:58,000
against banks and supermarkets.
359
00:26:58,000 --> 00:27:01,160
A 17-year-old man is recovering
in hospital after being injured
360
00:27:01,160 --> 00:27:04,000
in an explosion at a Sainsbury's
store in South London.
361
00:27:04,000 --> 00:27:06,400
'Police are advising
the public to be vigilant.
362
00:27:06,400 --> 00:27:10,440
'In truth, they can only wait
to see what Mardi Gra does next.'
363
00:27:16,440 --> 00:27:19,400
How many bombs did he
let off during that time?
364
00:27:19,400 --> 00:27:23,480
Total, 36 known linked offences.
365
00:27:23,480 --> 00:27:26,800
So you can see, they range
from the north of Cambridge,
366
00:27:26,800 --> 00:27:29,080
all the way down
to the strait of Dover.
367
00:27:29,080 --> 00:27:31,800
But most of them
are in Greater London.
368
00:27:31,800 --> 00:27:35,920
So this is a map showing
the locations of all the bombs
that were set off?
369
00:27:35,920 --> 00:27:39,240
That's right. There's certainly
a concentration on London,
370
00:27:39,240 --> 00:27:42,200
but it looks
pretty randomly scattered.
371
00:27:42,200 --> 00:27:45,640
So now you're feeding those
locations into the equation?
372
00:27:45,640 --> 00:27:49,400
Right. And what we have here now
is the geo-profile.
373
00:27:49,400 --> 00:27:52,840
And that's going to show us
the most likely location
374
00:27:52,840 --> 00:27:54,200
where the offender lived.
375
00:27:54,200 --> 00:27:58,040
With dark orange being the most
likely or the most probable.
376
00:27:58,040 --> 00:28:02,040
So we can see that the major
focus is around the Chiswick area.
377
00:28:02,040 --> 00:28:05,080
In fact, in the report
we prepared for Scotland Yard,
378
00:28:05,080 --> 00:28:07,800
we even prioritised
postcodes for that.
379
00:28:07,800 --> 00:28:09,880
And how successful
was it in this case?
380
00:28:09,880 --> 00:28:12,840
Well, let me show you
the locations...
381
00:28:12,840 --> 00:28:15,800
of the two brothers,
Edgar and Ronald Pearce.
382
00:28:15,800 --> 00:28:19,320
Right, that is really in
the hot zone, isn't it? Yes.
383
00:28:19,320 --> 00:28:21,600
Edgar's home is in the top 0.8%
384
00:28:21,600 --> 00:28:24,880
of the area of the crimes
in Greater London.
385
00:28:24,880 --> 00:28:27,280
So less than 1%.
That's extraordinary.
386
00:28:31,320 --> 00:28:35,480
Edgar Pearce had demanded
£10,000 a day from Barclays.
387
00:28:35,480 --> 00:28:39,560
And when he and his brother
tried to collect it
from a cash point in Chiswick,
388
00:28:39,560 --> 00:28:41,360
the police were waiting.
389
00:28:41,360 --> 00:28:44,520
Two bothers in their 60s were
remanded in custody by magistrates
390
00:28:44,520 --> 00:28:47,760
in connection with
the so-called Mardi Gra bombings.
391
00:28:47,760 --> 00:28:51,000
Ronald and Edgar Pearce,
both from Chiswick in West London,
392
00:28:51,000 --> 00:28:54,800
each face three conspiracy charges.
393
00:28:54,800 --> 00:28:58,720
Based on the apparently
random location of 36 bombs,
394
00:28:58,720 --> 00:29:04,000
Rossmo's geographic profile narrowed
the location of the Mardi Gra bomber
395
00:29:04,000 --> 00:29:07,960
from 300 square miles
to a postcode in Chiswick.
396
00:29:10,800 --> 00:29:13,040
Although his bother, Ronald,
was acquitted,
397
00:29:13,040 --> 00:29:18,160
Edgar Pearce pleaded guilty
and was jailed for 21 years.
398
00:29:18,160 --> 00:29:22,320
So do you think the bomber was aware
that he was creating these patterns?
399
00:29:22,320 --> 00:29:25,880
No, he wasn't.
But it's very difficult for humans
400
00:29:25,880 --> 00:29:28,760
to engage in
completely random behaviour.
401
00:29:34,640 --> 00:29:38,760
Very few of us are aware of
the patterns we leave behind.
402
00:29:38,760 --> 00:29:40,760
WOMAN LAUGHS
403
00:29:40,760 --> 00:29:43,560
From the way we move in a crowd...
404
00:29:46,120 --> 00:29:48,200
..to the choices we make
in a game...
405
00:29:48,200 --> 00:29:50,240
Paper covers rock!
406
00:29:50,240 --> 00:29:53,320
The victim's body was found here...
407
00:29:53,320 --> 00:29:55,960
..or even how we commit murder.
408
00:29:55,960 --> 00:29:59,400
In reality,
these crimes are not random...
409
00:29:59,400 --> 00:30:01,400
None of it is random.
410
00:30:01,400 --> 00:30:05,560
It's all part of the Code.
411
00:30:05,560 --> 00:30:08,200
There are always
tell-tale patterns.
412
00:30:08,200 --> 00:30:11,640
And if we're able to decode them,
413
00:30:11,640 --> 00:30:15,120
we can use those patterns
to model our behaviour.
414
00:30:15,120 --> 00:30:19,520
And this leads
to the intriguing possibility
415
00:30:19,520 --> 00:30:25,800
that if we can reduce human beings
to numbers, we might
be able to predict our future
416
00:30:25,800 --> 00:30:30,760
in the same way as we can predict
the movement of the planets
or the trajectory of a ball.
417
00:30:39,800 --> 00:30:44,480
But the course of our lives never
seems to run entirely smoothly,
418
00:30:44,480 --> 00:30:49,800
and the future rarely turns out
exactly as we'd planned.
419
00:30:49,800 --> 00:30:53,760
I may have a good idea
what I'm going to be doing tomorrow,
or even next week,
420
00:30:53,760 --> 00:30:58,560
but as the weeks turn into months
and months to years,
our future becomes less certain.
421
00:31:02,160 --> 00:31:06,600
Every decision we make,
every situation we encounter,
422
00:31:06,600 --> 00:31:11,440
every person we meet, sends
our life down a different path.
423
00:31:12,440 --> 00:31:17,760
As you watch each stick floating
off downstream, there's no sure way
of predicting their fate.
424
00:31:17,760 --> 00:31:22,760
I might be able to hazard
a guess where a stick
will be in two minutes.
425
00:31:22,760 --> 00:31:25,760
But what about two hours? Two days?
426
00:31:25,760 --> 00:31:29,000
'..Turn into years, our future
becomes far less certain.'
427
00:31:29,000 --> 00:31:35,240
Life sometimes seems
so unpredictable
that we think of it as being random.
428
00:31:35,240 --> 00:31:38,560
But in fact it isn't random at all.
429
00:31:38,560 --> 00:31:40,880
Simply a sequence
of cause and effect.
430
00:31:40,880 --> 00:31:44,320
A freak accident.
431
00:31:44,320 --> 00:31:45,800
I'm so sorry.
432
00:31:45,800 --> 00:31:47,480
A slight delay.
433
00:31:47,480 --> 00:31:49,240
A missed bus.
434
00:31:49,240 --> 00:31:51,880
A broken promise.
435
00:31:51,880 --> 00:31:58,960
There are millions of factors
that intervene to affect
our journey through life,
436
00:31:58,960 --> 00:32:04,000
and the tiniest shift
in any one of these can completely
change its future course.
437
00:32:05,000 --> 00:32:07,880
The white one's caught in a dam,
but the red one's fast.
438
00:32:19,560 --> 00:32:23,280
I think this'd be
a good finishing line.
439
00:32:23,280 --> 00:32:27,480
And here comes the white.
It's way ahead of the red.
440
00:32:28,040 --> 00:32:32,080
And white's the winner.
441
00:32:32,080 --> 00:32:33,760
Right, let's give it another go.
442
00:32:33,760 --> 00:32:40,560
The truth is,
our lives are controlled
by the strangest code of all...
443
00:32:40,560 --> 00:32:42,760
the code of chaos.
444
00:32:46,000 --> 00:32:49,520
Our lives aren't random,
they're chaotic,
445
00:32:49,520 --> 00:32:54,920
a tangled web of cause and effect
in which insignificant moments
446
00:32:54,920 --> 00:32:59,600
can escalate into events
that change our lives forever.
447
00:32:59,600 --> 00:33:05,680
Any difference, no matter how small,
can have a huge effect
on the outcome.
448
00:33:05,680 --> 00:33:09,640
It's this incredible sensitivity
to even the slightest change
449
00:33:09,640 --> 00:33:12,320
which is one
of the defining features of chaos.
450
00:33:18,320 --> 00:33:23,800
Because chaotic systems
appear so random, it's often
difficult to see a pattern.
451
00:33:25,400 --> 00:33:31,920
And that has led us to sometimes
misinterpret our world
in a spectacular manner.
452
00:33:37,640 --> 00:33:40,920
'In this land of many mysteries,
it's a strange fact
453
00:33:40,920 --> 00:33:44,720
'that large legends seem to collect
around the smallest creatures.
454
00:33:44,720 --> 00:33:49,320
'One of these is a mousy little
rodent called the lemming.
455
00:33:49,320 --> 00:33:53,200
'Here's an actual living legend,
for it's said of this tiny animal
456
00:33:53,200 --> 00:33:57,360
''that it commits mass suicide
by rushing into the sea in droves.
457
00:33:57,360 --> 00:34:00,280
This film from 1958
458
00:34:00,280 --> 00:34:03,200
set out to explain
the wildly fluctuating population
459
00:34:03,200 --> 00:34:05,040
of these tiny rodents.
460
00:34:11,560 --> 00:34:19,000
'Ahead lies the Arctic shore,
and beyond, the sea. And still
the little animals surge forward.
461
00:34:21,840 --> 00:34:25,680
'Their frenzy takes them
tumbling down the terraced cliffs,
462
00:34:25,680 --> 00:34:29,400
'creating tiny avalanches
of sliding soil and rocks.'
463
00:34:33,120 --> 00:34:35,000
The legend of suicidal lemmings
464
00:34:35,000 --> 00:34:39,800
was the accepted explanation
for why the Arctic can be overrun
with them one year
465
00:34:39,800 --> 00:34:41,480
and completely empty the next.
466
00:34:41,480 --> 00:34:45,520
'They reach the final precipice.
467
00:34:45,520 --> 00:34:48,240
'This is the last
chance to turn back.
468
00:34:52,520 --> 00:34:56,720
'Yet over they go, casting
themselves bodily out into space.'
469
00:35:01,240 --> 00:35:07,560
This film popularised the belief
that lemmings are stupid,
reckless and suicidal.
470
00:35:07,560 --> 00:35:10,280
The very word "lemming"
has come to mean as much.
471
00:35:15,400 --> 00:35:18,520
The trouble is,
though, it isn't true.
472
00:35:18,520 --> 00:35:22,160
In fact, it's been claimed
that the whole thing was faked.
473
00:35:26,240 --> 00:35:30,840
The film-makers apparently flew in
hundreds of captive-bred lemmings
474
00:35:30,840 --> 00:35:33,840
and drove them over the cliffs
and out to sea.
475
00:35:37,080 --> 00:35:40,200
'Soon the Arctic Sea is dotted
with tiny bobbing bodies.
476
00:35:43,800 --> 00:35:48,800
'And so is acted out
the legend of mass suicide.'
477
00:35:48,800 --> 00:35:53,960
Now, as appalling as this sounds,
the reason for the alleged lemming
abuse stems not so much
478
00:35:53,960 --> 00:35:55,840
from ignoring the moral code,
479
00:35:55,840 --> 00:35:58,600
but rather an ignorance
of the mathematical one.
480
00:35:58,600 --> 00:36:04,640
What no-one knew at the time
was that the incredible fluctuation
481
00:36:04,640 --> 00:36:08,760
in lemming numbers has nothing
to do with mass suicide.
482
00:36:09,960 --> 00:36:12,640
It's all because of chaos.
483
00:36:12,640 --> 00:36:15,880
And there's a simple equation
at its heart.
484
00:36:17,800 --> 00:36:22,720
So, if I want to know how many
lemmings there'll be next year,
485
00:36:22,720 --> 00:36:26,480
what I need to do is take
this year's population, "P",
486
00:36:26,480 --> 00:36:29,400
and multiply that
by the growth rate "R".
487
00:36:29,400 --> 00:36:31,840
But not all lemmings will survive,
488
00:36:31,840 --> 00:36:36,480
so there's a bit of the equation
which tells me how many lemmings
will die during the year.
489
00:36:36,480 --> 00:36:39,160
So that's R times P times P.
490
00:36:39,160 --> 00:36:42,440
So we can rewrite this equation
491
00:36:42,440 --> 00:36:47,320
as the growth rate R times P
times one minus P.
492
00:36:47,320 --> 00:36:50,040
Now, this equation isn't specific
to lemmings,
493
00:36:50,040 --> 00:36:52,680
it actually applies
to any animal population.
494
00:36:52,680 --> 00:36:57,360
And the interesting part
of the equation
is this number R, the growth rate.
495
00:36:57,360 --> 00:37:00,400
Because when we choose
different values for R,
496
00:37:00,400 --> 00:37:03,680
we get a very different behaviour
for the population growth.
497
00:37:04,600 --> 00:37:08,840
The growth rate determines how
quickly a population expands.
498
00:37:08,840 --> 00:37:12,800
For most species of mammal,
this is usually below 2.
499
00:37:12,800 --> 00:37:16,640
With a growth rate in this range,
the equation predicts
500
00:37:16,640 --> 00:37:20,760
that a population will rise until
it stabilises at a fixed value.
501
00:37:20,760 --> 00:37:26,640
But it turns out lemmings
are one of the fastest-reproducing
mammals on the planet.
502
00:37:26,640 --> 00:37:30,360
Let's take R equals 3.1.
503
00:37:30,360 --> 00:37:35,480
The lemmings
don't stabilise now, but ping-pong
between two different values.
504
00:37:35,480 --> 00:37:40,080
So the population is high, then low,
and back to high again, low again.
505
00:37:40,080 --> 00:37:44,640
But when the growth rate reaches
a value just over 3.57
506
00:37:44,640 --> 00:37:49,080
then something incredibly
unexpected happens.
507
00:37:49,080 --> 00:37:52,080
Rather than levelling off
at a fixed number,
508
00:37:52,080 --> 00:37:58,680
or fluctuating between two values,
their population erupts into chaos.
509
00:37:58,680 --> 00:38:04,800
A plague of almost biblical
proportions one year can plummet
to near extinction the next.
510
00:38:04,800 --> 00:38:09,080
It's almost impossible
to predict how many lemmings
you're going to have.
511
00:38:09,080 --> 00:38:12,280
In fact, there doesn't seem to be
any pattern to this at all.
512
00:38:12,280 --> 00:38:16,520
And of course, this is exactly
what's seen in reality.
513
00:38:16,520 --> 00:38:20,280
Unpredictable
boom-and-bust lemming populations.
514
00:38:21,800 --> 00:38:25,640
Lemmings are one of the few
creatures on Earth
that breed so quickly
515
00:38:25,640 --> 00:38:29,120
their growth rate can sometimes
exceed this tipping point.
516
00:38:32,600 --> 00:38:37,760
It's such an odd phenomenon
that mass suicide
seems like a plausible answer.
517
00:38:37,760 --> 00:38:41,320
But the real explanation
comes from the Code.
518
00:38:41,320 --> 00:38:42,920
From this equation.
519
00:38:48,080 --> 00:38:53,640
The problem is we can never know
exactly how many lemmings
are born or how many die.
520
00:38:53,640 --> 00:39:00,080
And just the smallest difference
in the growth rate R, produces
a totally different answer.
521
00:39:00,080 --> 00:39:04,360
And this is true
of all equations that model chaos.
522
00:39:04,360 --> 00:39:08,240
Although they can explain how
something happens,
523
00:39:08,240 --> 00:39:11,440
they're almost useless
at predicting the future.
524
00:39:19,320 --> 00:39:22,640
I can use an equation to calculate
where this ball will land,
525
00:39:22,640 --> 00:39:25,960
because even if I'm slightly out
in any of my measurements,
526
00:39:25,960 --> 00:39:28,960
it will only make a small
difference to the final result.
527
00:39:28,960 --> 00:39:34,280
The ball will be released
from the ramp at 49.1 degrees.
528
00:39:34,280 --> 00:39:37,640
But if this ball behaved according
to the laws of chaos,
529
00:39:37,640 --> 00:39:40,360
the tiniest shift in the
ball's position
530
00:39:40,360 --> 00:39:45,720
or the angle of release could
dramatically alter its trajectory.
531
00:39:47,520 --> 00:39:50,760
I'd have no idea whether it
would just simply fall harmlessly
532
00:39:50,760 --> 00:39:52,040
off the end of the ramp.
533
00:39:55,040 --> 00:39:58,080
Or be sent into orbit.
534
00:40:02,520 --> 00:40:06,480
I'd have no idea
where to put my deckchair.
535
00:40:08,240 --> 00:40:12,320
It turns out
that much of the world is chaotic,
536
00:40:12,320 --> 00:40:15,920
making it almost impossible
to predict.
537
00:40:17,760 --> 00:40:20,920
But that doesn't stop us trying.
538
00:40:24,880 --> 00:40:27,360
Knowing whether the sun
is going to shine
539
00:40:27,360 --> 00:40:30,720
or the heavens are going to open,
is a British obsession.
540
00:40:30,720 --> 00:40:34,840
But trying to plan our lives
around the vagaries of the weather
541
00:40:34,840 --> 00:40:36,360
seems almost futile.
542
00:40:42,920 --> 00:40:48,360
Even though we have precise
equations that can describe
how clashing air masses interact
543
00:40:48,360 --> 00:40:50,840
to create clouds,
wind and rainfall,
544
00:40:50,840 --> 00:40:56,520
it doesn't really help us
very much with our predictions.
545
00:40:56,520 --> 00:40:59,920
THUNDER CRASHES
546
00:40:59,920 --> 00:41:05,520
That's because
we can never know the exact speed
of every air particle.
547
00:41:05,520 --> 00:41:08,320
The precise temperature
at every point in space,
548
00:41:08,320 --> 00:41:11,280
or the pressure
across the whole planet.
549
00:41:11,280 --> 00:41:14,160
And just a small variation
in any one of these
550
00:41:14,160 --> 00:41:16,880
can produce
a vastly different forecast.
551
00:41:22,400 --> 00:41:26,040
This is a map of how
the weather looks right now.
552
00:41:26,040 --> 00:41:31,600
The blue lines represent
cold fronts and the red lines
represent warm fronts.
553
00:41:31,600 --> 00:41:33,400
In order to make a prediction
554
00:41:33,400 --> 00:41:36,880
what we do is to take
the mathematical equations
for the weather
555
00:41:36,880 --> 00:41:38,760
and create a model.
556
00:41:38,760 --> 00:41:43,200
Now the trouble is, I can't know
the precise atmospheric conditions,
557
00:41:43,200 --> 00:41:45,640
so I take as much data as possible.
558
00:41:45,640 --> 00:41:50,760
Then I make small variations in
the data and run the model again
559
00:41:50,760 --> 00:41:56,680
and again and again and what I get
is different predictions according
to those slight variations.
560
00:41:56,680 --> 00:42:00,800
So for the weather tomorrow,
the predictions are petty similar.
561
00:42:00,800 --> 00:42:05,000
We've got a lot of blue lines
together predicting a cold front.
562
00:42:05,000 --> 00:42:08,080
A lot of red lines together
predicting a warm front.
563
00:42:08,080 --> 00:42:11,840
But look what happens when
I look a little bit further ahead.
564
00:42:11,840 --> 00:42:15,560
So two days, three days ahead...
565
00:42:15,560 --> 00:42:20,280
so you can see these
different predictions
are beginning to spread out.
566
00:42:20,280 --> 00:42:23,280
You can still see some sort
of pattern in the weather
567
00:42:23,280 --> 00:42:26,400
but if I move a week ahead...
568
00:42:27,480 --> 00:42:31,040
..and I couldn't hazard a guess as
to what the weather's going to be.
569
00:42:31,040 --> 00:42:33,800
There are red and blue lines
all over the place.
570
00:42:33,800 --> 00:42:37,000
One prediction says it's going
to be hot, the other says cold,
571
00:42:37,000 --> 00:42:40,800
and if I go ten days ahead,
572
00:42:40,800 --> 00:42:44,120
it just looks like
a scrambled mess of spaghetti.
573
00:42:44,120 --> 00:42:49,840
There's absolutely no way to make
any prediction that far in advance.
574
00:42:49,840 --> 00:42:52,280
And that's why beyond just
a few days,
575
00:42:52,280 --> 00:42:55,680
the weather forecast
can be so spectacularly wrong.
576
00:43:00,400 --> 00:43:03,720
Once we understand
that the atmosphere is chaotic,
577
00:43:03,720 --> 00:43:08,440
we can appreciate that the smallest
change in the initial conditions
578
00:43:08,440 --> 00:43:11,360
can dramatically alter
what will happen.
579
00:43:14,480 --> 00:43:18,720
The movement of just one molecule
of air can be magnified over time
580
00:43:18,720 --> 00:43:21,680
to have a huge effect
on the weather as a whole.
581
00:43:24,560 --> 00:43:28,000
We refer to this phenomenon
as the "butterfly effect".
582
00:43:28,000 --> 00:43:33,840
The idea that something
as small as the flap
of a butterfly's wings
583
00:43:33,840 --> 00:43:36,000
might create changes
in the atmosphere
584
00:43:36,000 --> 00:43:40,760
that could ultimately
lead to a tornado
on the other side of the world.
585
00:43:40,760 --> 00:43:43,760
CRASHING THUNDER
586
00:43:58,720 --> 00:44:03,000
As a crowd, the patterns we make
are incredibly predictable.
587
00:44:04,840 --> 00:44:09,840
Even as individuals our actions
are controlled by the Code.
588
00:44:13,960 --> 00:44:16,720
And by untangling chaotic systems
like the weather,
589
00:44:16,720 --> 00:44:21,480
we've uncovered evidence of the
Code in what we once thought of
590
00:44:21,480 --> 00:44:24,360
as impossibly complex.
591
00:44:26,200 --> 00:44:28,920
When we look at things
from a different angle,
592
00:44:28,920 --> 00:44:31,560
surprising patterns emerge.
593
00:44:33,840 --> 00:44:39,800
Patterns that can reveal
defining truths
about ourselves and our future.
594
00:44:47,760 --> 00:44:51,880
In 1906, an unfortunate cow
laid down its life
595
00:44:51,880 --> 00:44:54,400
for a place
in mathematical history.
596
00:44:54,400 --> 00:44:55,720
One.
597
00:44:55,720 --> 00:44:57,440
Ten.
598
00:44:57,440 --> 00:44:59,160
264.
599
00:44:59,160 --> 00:45:01,400
417.
600
00:45:01,400 --> 00:45:06,760
'The cow was the subject
of a guess-the-weight competition
at a village fare.
601
00:45:06,760 --> 00:45:09,120
'The lucky person who came closest
602
00:45:09,120 --> 00:45:12,240
'would win the slaughtered
animal's meat.'
603
00:45:13,240 --> 00:45:14,800
1,020.
604
00:45:16,280 --> 00:45:17,480
2,137.
605
00:45:17,480 --> 00:45:20,520
'The amazing thing
was nobody guessed correctly.'
606
00:45:20,520 --> 00:45:21,720
..570.
607
00:45:21,720 --> 00:45:24,720
'And yet everybody got it right.'
608
00:45:26,240 --> 00:45:28,760
4,510.
609
00:45:30,160 --> 00:45:32,160
To show you how they did it,
610
00:45:32,160 --> 00:45:36,800
I'm not going to use a cow, I'm
going to use a jar of jelly beans.
611
00:45:40,240 --> 00:45:41,720
450?
612
00:45:41,720 --> 00:45:43,000
800?
613
00:45:43,000 --> 00:45:44,040
12,000.
614
00:45:44,040 --> 00:45:45,640
7,000.
615
00:45:45,640 --> 00:45:48,800
How many jellybeans do
you think there are in this jar?
616
00:45:48,800 --> 00:45:50,320
Um,
617
00:45:50,320 --> 00:45:52,840
50...
618
00:45:52,840 --> 00:45:54,440
80 thousand.
619
00:45:54,440 --> 00:45:55,920
80 thousand?
620
00:45:55,920 --> 00:45:58,880
No, actually 50,000.
621
00:45:58,880 --> 00:46:00,600
50,000. OK, yeah.
622
00:46:04,600 --> 00:46:09,080
It's incredibly difficult
for anyone to guess how
many jellybeans there are.
623
00:46:10,040 --> 00:46:15,000
I asked 160 people
and most were way off the mark.
624
00:46:15,000 --> 00:46:20,200
Everything from 400
right up to 50,000 beans.
625
00:46:20,200 --> 00:46:27,080
In fact only four people
got anywhere near the correct
answer of 4,510.
626
00:46:27,080 --> 00:46:36,840
Plus 1,500, plus 3,217, plus 83... .
627
00:46:36,840 --> 00:46:43,680
If I add all the answers together
and take the average, I get the
combined guess of the entire group.
628
00:46:43,680 --> 00:46:46,240
Plus, 4,000, plus 5,000,
629
00:46:46,240 --> 00:46:48,800
463,
630
00:46:48,800 --> 00:46:52,520
Plus 853, plus 1,000,
631
00:46:52,520 --> 00:46:55,400
plus 5,000...
632
00:46:55,400 --> 00:46:58,040
Which gives a grand total
633
00:46:58,040 --> 00:47:04,000
of 722,383.5.
634
00:47:04,000 --> 00:47:06,960
Somebody thought there
was half a bean in there.
635
00:47:06,960 --> 00:47:13,320
Now there are 160 guesses made,
so let's see how close
they are collectively.
636
00:47:13,320 --> 00:47:16,600
Wow, that's extraordinary.
637
00:47:16,600 --> 00:47:20,440
You remember there were 4,510.
638
00:47:20,440 --> 00:47:26,680
The average guess
to the nearest bean is 4,515.
639
00:47:26,680 --> 00:47:30,280
I thought it would be close, but I
didn't think it would be THAT close.
640
00:47:30,280 --> 00:47:31,360
That is ridiculous.
641
00:47:31,360 --> 00:47:36,160
Though we had guesses that were
all over the place, up in 30,000s
right down in the 400s,
642
00:47:36,160 --> 00:47:40,600
collectively we get something
which is just 0.1% away
643
00:47:40,600 --> 00:47:43,800
from the real number
of beans in there.
644
00:47:43,800 --> 00:47:47,840
So as individuals the guesses
are just that, guesses.
645
00:47:47,840 --> 00:47:52,240
But when you take them collectively
they become something else entirely.
646
00:47:52,240 --> 00:47:57,800
5,000. 1,450. 9,200.
647
00:47:57,800 --> 00:48:01,440
What tends to happen
is that more or less as many people
648
00:48:01,440 --> 00:48:05,720
will underestimate the number
of jellybeans as overestimate it.
649
00:48:05,720 --> 00:48:08,600
1,763... 6,000.
650
00:48:08,600 --> 00:48:13,400
A few people will be
way off the mark either way,
but that doesn't matter.
651
00:48:13,400 --> 00:48:19,480
Provided you ask enough people,
the errors should
cancel each other out.
652
00:48:19,480 --> 00:48:24,400
1,000. 1,275. 700?
653
00:48:24,400 --> 00:48:28,640
The accuracy of the group is far
greater than the individual.
654
00:48:28,640 --> 00:48:31,880
We call it
"the wisdom of the crowd".
655
00:48:31,880 --> 00:48:40,880
160 people is a powerful tool
for working out how many jellybeans
there are in the jar.
656
00:48:40,880 --> 00:48:44,560
But imagine what you could do
with a crowd of millions.
657
00:48:47,080 --> 00:48:50,640
That's exactly
what they use here at Google.
658
00:48:52,160 --> 00:48:55,320
With access to over two billion web
searches a day,
659
00:48:55,320 --> 00:49:00,240
Google have found a way of tapping
into the wisdom
of the biggest crowd on Earth.
660
00:49:00,240 --> 00:49:02,480
And by doing so,
661
00:49:02,480 --> 00:49:07,960
they've been able to reveal
the forces that control our lives,
662
00:49:07,960 --> 00:49:11,000
and harness them
to make predictions about us.
663
00:49:11,000 --> 00:49:14,280
'Think of the things that people
search for on a daily basis.
664
00:49:14,280 --> 00:49:17,760
'Think of the things that YOU
search for on a daily basis.'
665
00:49:17,760 --> 00:49:23,480
I searched for cities in Mexico
and films in Hackney today.
666
00:49:23,480 --> 00:49:26,160
Lots of people may be searching
for the similar...
667
00:49:26,160 --> 00:49:29,200
a similar thing,
movies in Hackney, for example.
668
00:49:29,200 --> 00:49:33,040
And if you look at that query
over the past three years,
669
00:49:33,040 --> 00:49:36,200
um, what the pattern of searches
for that term looks like.
670
00:49:40,360 --> 00:49:43,600
Google had a hunch
they could use all our searches
671
00:49:43,600 --> 00:49:46,080
to make predictions about our lives.
672
00:49:47,600 --> 00:49:51,720
They wanted to see
if they could match the pattern
of certain searches
673
00:49:51,720 --> 00:49:53,560
with events in the real world.
674
00:49:55,400 --> 00:49:59,720
Google began by seeing if they
could predict outbreaks of flu.
675
00:50:01,440 --> 00:50:04,720
So flu has a nice seasonal pattern
676
00:50:04,720 --> 00:50:09,600
and because it has that pattern
every year over many years,
677
00:50:09,600 --> 00:50:12,000
we're able to...to take that trend
678
00:50:12,000 --> 00:50:16,880
and say which search queries
match that pattern.
679
00:50:16,880 --> 00:50:21,280
So we built a database
that included over 50 million
different search terms.
680
00:50:21,280 --> 00:50:22,800
50 million?
681
00:50:22,800 --> 00:50:24,440
Yes. Wow, yeah.
682
00:50:24,440 --> 00:50:27,520
We didn't only include things
that may be related to flu.
683
00:50:27,520 --> 00:50:29,920
We included things
like Britney Spears or...
684
00:50:29,920 --> 00:50:32,840
Everything people search
for would be included.
685
00:50:35,000 --> 00:50:38,680
When Google looked back over
the past five years of data,
686
00:50:38,680 --> 00:50:43,400
there were certain search terms
whose popularity exactly matched
687
00:50:43,400 --> 00:50:45,240
the pattern of flu cases.
688
00:50:45,240 --> 00:50:49,600
So people were searching
for things like "symptoms"
689
00:50:49,600 --> 00:50:52,880
or "medications"
or "sore throat".
690
00:50:52,880 --> 00:50:56,720
There are other things
like complications.
691
00:50:56,720 --> 00:51:01,440
So you're saying that
the sort of number of search
terms for flu-related things
692
00:51:01,440 --> 00:51:07,000
almost exactly mirrors the actual
cases of flu that we see
in the population? That's true.
693
00:51:07,000 --> 00:51:12,000
It is an indicator of flu activity
just based on lots of people
searching for these terms.
694
00:51:12,000 --> 00:51:14,600
We were amazed by this finding.
695
00:51:19,680 --> 00:51:22,680
As soon as they see this pattern
of search terms
696
00:51:22,680 --> 00:51:26,000
Google can predict
there will be an outbreak of flu.
697
00:51:26,000 --> 00:51:29,520
Often before people
had even gone to the doctor.
698
00:51:31,920 --> 00:51:36,280
This is the extraordinary power
of the Code.
699
00:51:38,520 --> 00:51:42,200
But it's just the tip
of the iceberg.
700
00:51:42,200 --> 00:51:44,480
The searches we make
can be used to predict
701
00:51:44,480 --> 00:51:46,000
where we'll go on holiday.
702
00:51:46,000 --> 00:51:48,720
What model of car
we're going to buy.
703
00:51:48,720 --> 00:51:50,960
Or how we're going to vote,
704
00:51:50,960 --> 00:51:53,360
often before we know ourselves.
705
00:51:55,360 --> 00:51:59,160
It's even been possible to forecast
the movement of the Stock Market
706
00:51:59,160 --> 00:52:02,400
from the number of negative
words used on Twitter.
707
00:52:06,600 --> 00:52:12,440
Analysing such vast amounts
of data, doesn't just allow us
to make predictions.
708
00:52:12,440 --> 00:52:17,000
It can also tell us something
fundamental about ourselves.
709
00:52:20,840 --> 00:52:26,440
You look out at a city like this
and it looks like, you know,
some arbitrary jumbled mess.
710
00:52:26,440 --> 00:52:30,480
Yet the city IS people.
711
00:52:30,480 --> 00:52:32,800
It's not the buildings
and the streets.
712
00:52:32,800 --> 00:52:36,240
They're the stage upon which
the real actors
713
00:52:36,240 --> 00:52:39,560
are playing out
the story of civilisation.
714
00:52:42,640 --> 00:52:48,520
Geoffrey West is a physicist
who's spent his life trying to see
meaningful patterns in the universe.
715
00:52:48,520 --> 00:52:55,000
And how he's turned
his attention to the dynamics
of human life in cities.
716
00:52:59,360 --> 00:53:03,800
So you can see there's all kinds
of infrastructure here.
717
00:53:03,800 --> 00:53:09,760
There's the obvious, the roads, the
electrical lines, the sewer lines.
718
00:53:09,760 --> 00:53:13,680
They're an extraordinary network
that is sustaining New York City.
719
00:53:13,680 --> 00:53:16,320
You know,
coming at it as a physicist,
720
00:53:16,320 --> 00:53:21,720
I had this hunch that there is
an underlying code to all this.
721
00:53:24,480 --> 00:53:29,480
West amassed data
about cities all over the world.
722
00:53:29,480 --> 00:53:33,360
And the patterns he found mean that
for any given population size,
723
00:53:33,360 --> 00:53:35,480
he can predict the amount of roads,
724
00:53:35,480 --> 00:53:40,160
electrical wiring
or office space that city has.
725
00:53:43,000 --> 00:53:46,840
But he also discovered something
much more surprising.
726
00:53:50,320 --> 00:53:56,080
One of the most interesting results
we discovered was that, um...
727
00:53:56,080 --> 00:54:00,040
Wages scale
in a very systematic way
728
00:54:00,040 --> 00:54:04,160
and the rule that came out was that
if you doubled the size of the city,
729
00:54:04,160 --> 00:54:08,840
you get this marvellous
15% increase in the wages.
730
00:54:08,840 --> 00:54:13,440
If you live in a large city,
you're going to earn more? Yes.
731
00:54:13,440 --> 00:54:15,680
So what, if there
are two mathematicians
732
00:54:15,680 --> 00:54:19,280
in two different cities - one twice
the size - doing the same job,
733
00:54:19,280 --> 00:54:24,080
one will have a bigger income?
On the average,
that is what the data say.
734
00:54:24,080 --> 00:54:26,960
Was that a surprise to you
to see that? A huge surprise.
735
00:54:26,960 --> 00:54:29,880
I thought there was
something wrong with the data.
736
00:54:29,880 --> 00:54:35,560
And then it was like, "Of course!
That's why cities exist."
737
00:54:40,240 --> 00:54:43,800
Incredibly, it's not just people's
salaries that increase.
738
00:54:43,800 --> 00:54:49,280
When a city doubles in size,
every measure of social
and economic activity
739
00:54:49,280 --> 00:54:52,000
goes up by 15% per person.
740
00:54:52,000 --> 00:54:56,200
That's 15% more restaurants
to choose from.
741
00:54:56,200 --> 00:55:00,880
15% more art galleries to visit.
15% more shops to go to.
742
00:55:01,920 --> 00:55:05,400
In short, life gets 15% better.
743
00:55:11,120 --> 00:55:13,680
You know it looks like
it's a magic formula
744
00:55:13,680 --> 00:55:17,280
that we as social human beings
have discovered...
745
00:55:20,120 --> 00:55:25,720
..this 15% bonus, so to speak,
is, I believe, the reason
746
00:55:25,720 --> 00:55:28,080
that people
are attracted to cities
747
00:55:28,080 --> 00:55:32,200
and why there has been
this continuous migration
748
00:55:32,200 --> 00:55:35,760
from the countryside
and into the cities.
749
00:55:35,760 --> 00:55:40,720
And at some deeper level,
actually drive our civilisation.
750
00:55:43,120 --> 00:55:48,120
According to Geoffrey West,
humankind has an ultimate number.
751
00:55:48,120 --> 00:55:51,360
It's this extra 15%
752
00:55:51,360 --> 00:55:53,280
or 1.15.
753
00:55:53,280 --> 00:55:58,600
He believes it's the most important
driving force in humanity.
754
00:56:00,680 --> 00:56:03,680
This single number, 1.15,
755
00:56:03,680 --> 00:56:05,680
predicts our future.
756
00:56:06,960 --> 00:56:10,520
It will bring us together
in ever expanding cities
757
00:56:10,520 --> 00:56:14,840
and shape our destiny for
as long as human beings exist.
758
00:56:25,440 --> 00:56:29,160
Five hundred years ago,
when faced with an eclipse,
759
00:56:29,160 --> 00:56:33,320
many of us would have believed it
was the work of an angry god.
760
00:56:33,320 --> 00:56:35,920
But as we've unearthed
the language of the Code,
761
00:56:35,920 --> 00:56:39,800
we've discovered that the apparent
mysteries of our world
762
00:56:39,800 --> 00:56:43,240
can be understood
without invoking the supernatural.
763
00:56:43,240 --> 00:56:46,360
And this for me
is what's so remarkable.
764
00:56:46,360 --> 00:56:50,120
That despite the incredible
complexity of the world we live in,
765
00:56:50,120 --> 00:56:54,640
it can all, ultimately,
be explained by numbers.
766
00:56:57,360 --> 00:57:02,080
Just like the orbit of the planets,
life too follows a pattern.
767
00:57:04,320 --> 00:57:07,920
And it can all be reduced
to cause and effect.
768
00:57:11,040 --> 00:57:13,920
In the end,
even the flip of a coin
769
00:57:13,920 --> 00:57:16,520
is determined by how fast
it's spinning
770
00:57:16,520 --> 00:57:18,680
and how long it takes
to hit the ground.
771
00:57:18,680 --> 00:57:23,640
The ultimate symbol of chance
isn't random at all.
772
00:57:23,640 --> 00:57:26,080
It only appears that way.
773
00:57:28,760 --> 00:57:31,920
When we don't understand the Code,
774
00:57:31,920 --> 00:57:36,200
the only way we can make sense
of our world is to make up stories.
775
00:57:37,880 --> 00:57:40,840
But the truth
is far more extraordinary.
776
00:57:42,440 --> 00:57:46,480
Everything has mathematics
at its heart.
777
00:57:47,520 --> 00:57:52,560
When everything is stripped away
all that remains is the Code.
778
00:57:57,720 --> 00:58:05,520
Find clues to help you solve
the Code's treasure hunt at...
779
00:58:05,520 --> 00:58:10,360
Plus get a free set of mathematical
puzzles and a treasure hunt clue
780
00:58:10,360 --> 00:58:14,200
when you follow the links
to the Open University.
781
00:58:14,200 --> 00:58:19,760
Or call 0845 366 8026.
782
00:58:32,720 --> 00:58:35,760
Subtitles by Red Bee Media Ltd
783
00:58:35,760 --> 00:58:38,800
E-mail subtitling@bbc.co.uk
68978
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