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For as long as human beings have walked upon earth, | 0:00:04 | 0:00:07 | |
we've tried to make sense of our world | 0:00:07 | 0:00:09 | |
and predict what the future will bring. | 0:00:09 | 0:00:12 | |
Yet today, | 0:00:18 | 0:00:20 | |
our lives seem more complicated and unpredictable than ever. | 0:00:20 | 0:00:23 | |
And half the population of the planet | 0:00:25 | 0:00:27 | |
now live in busy, sprawling cities. | 0:00:27 | 0:00:30 | |
Every day throws up thousands of different encounters. | 0:00:31 | 0:00:35 | |
A mass of interactions and forces that seem beyond our control. | 0:00:35 | 0:00:39 | |
WOMAN LAUGHS | 0:00:39 | 0:00:41 | |
It's hard to see how any of this could be connected. | 0:00:41 | 0:00:45 | |
BABY CRIES | 0:00:45 | 0:00:46 | |
Yet when we start to look closely at all this complexity, | 0:00:47 | 0:00:51 | |
surprising patterns begin to emerge. | 0:00:51 | 0:00:54 | |
It's these patterns that I believe point to an underlying Code | 0:00:59 | 0:01:03 | |
at the very heart of existence | 0:01:03 | 0:01:06 | |
that controls not only our world and everything in it, but even us. | 0:01:06 | 0:01:10 | |
As a mathematician, I'm fascinated by the patterns we see all around us. | 0:01:46 | 0:01:51 | |
Patterns that reflect the hidden connections between everything. | 0:01:55 | 0:01:59 | |
From the movement of rush hour crowds... | 0:02:01 | 0:02:04 | |
..to the shifting shape of a flock of starlings. | 0:02:07 | 0:02:10 | |
The cacophony of a billion Internet searches... | 0:02:13 | 0:02:17 | |
and the vagaries of the weather. | 0:02:17 | 0:02:19 | |
THUNDER ROLLS | 0:02:19 | 0:02:21 | |
CHEERING | 0:02:26 | 0:02:28 | |
Together, these patterns and connections make up the Code. | 0:02:31 | 0:02:34 | |
A model of our world that describes not only how it works, | 0:02:36 | 0:02:41 | |
but can also predict what our future holds. | 0:02:41 | 0:02:44 | |
Around 500 years ago, a ship was caught in a terrible storm. | 0:03:00 | 0:03:05 | |
As rain lashed the decks | 0:03:05 | 0:03:06 | |
and gale force winds tore through the rigging, | 0:03:06 | 0:03:09 | |
the ship began to take on water. | 0:03:09 | 0:03:11 | |
The captain had no choice but to run his ship aground and wait for help. | 0:03:11 | 0:03:16 | |
But help never arrived, and the natives were hostile. | 0:03:19 | 0:03:23 | |
After eight long months, and with his crew facing certain starvation, | 0:03:26 | 0:03:30 | |
the captain came up with an ingenious plan. | 0:03:30 | 0:03:33 | |
He summoned the local chief and told him his God was angry. | 0:03:33 | 0:03:37 | |
So angry, in fact, that if they didn't bring supplies within three days, | 0:03:38 | 0:03:42 | |
God would swallow the moon. | 0:03:42 | 0:03:44 | |
And sure enough, as the moon rose on the third night, it had already begun to disappear. | 0:03:50 | 0:03:56 | |
Terrified, the locals ran from all directions towards the ship, | 0:04:04 | 0:04:08 | |
laden with provisions. | 0:04:08 | 0:04:09 | |
The year was 1504, and the captain? | 0:04:16 | 0:04:19 | |
Christopher Columbus. | 0:04:19 | 0:04:21 | |
And the reason he was apparently able to command the heavens | 0:04:21 | 0:04:25 | |
was because he had something like this. | 0:04:25 | 0:04:28 | |
It's a set of lunar tables. | 0:04:31 | 0:04:32 | |
And each one of these numbers represents a lunar eclipse. | 0:04:32 | 0:04:37 | |
Today's date is June 15th, and it says that in about five hours' time | 0:04:37 | 0:04:43 | |
the same thing is going to happen to the moon here in Cyprus. | 0:04:43 | 0:04:47 | |
During a lunar eclipse, the earth passes between the sun and the moon, | 0:04:53 | 0:04:57 | |
casting its shadow across the lunar surface. | 0:04:57 | 0:05:00 | |
And there it goes. | 0:05:09 | 0:05:10 | |
The moon has been swallowed up by the shadow of the Earth. | 0:05:10 | 0:05:15 | |
But the amazing thing is actually the moon doesn't completely disappear, cos... | 0:05:15 | 0:05:19 | |
there's a kind of... red, ghostly moon up there. | 0:05:19 | 0:05:24 | |
And that's because the light from the sun is being refracted around the Earth. | 0:05:24 | 0:05:30 | |
Really quite spooky. | 0:05:31 | 0:05:33 | |
I can imagine how terrified the islanders would have been when they saw that 500 years ago. | 0:05:38 | 0:05:44 | |
And the only explanation for them would have been that the gods really were angry with them. | 0:05:44 | 0:05:49 | |
We now know that the movement of the planets is incredibly predictable. | 0:05:57 | 0:06:03 | |
By understanding the Code, we can model their orbits far back into the past. | 0:06:03 | 0:06:08 | |
And see thousands of years into the future. | 0:06:08 | 0:06:12 | |
It's thanks to the Code that we're no longer frightened by an eclipse. | 0:06:21 | 0:06:25 | |
In fact, the Code is such a powerful thing | 0:06:25 | 0:06:28 | |
that I'm even prepared to entrust my life to it. | 0:06:28 | 0:06:32 | |
This strange contraption is five and a half metres high. | 0:06:54 | 0:06:58 | |
Using the force of gravity, | 0:06:59 | 0:07:02 | |
a 30-kilogram ball will hurtle down the ramp and fire off the end. | 0:07:02 | 0:07:07 | |
And when it does, I will be sitting directly in its path. | 0:07:07 | 0:07:13 | |
If I get my sums wrong, I'll be killed outright. | 0:07:13 | 0:07:16 | |
To calculate how far the ball's going to go, | 0:07:23 | 0:07:26 | |
I need some key measurements about the ramp. | 0:07:26 | 0:07:29 | |
Little h is 0.98 metres. | 0:07:31 | 0:07:35 | |
The angle is 49.1 degree. | 0:07:35 | 0:07:41 | |
So gravity, I know, on the Earth... | 0:07:41 | 0:07:43 | |
is 9.8 metres per second squared. | 0:07:43 | 0:07:49 | |
Interestingly, you don't have to know the weight of the ball, the mass of the ball. | 0:07:49 | 0:07:53 | |
That's not relevant to how far the thing's going to go. | 0:07:53 | 0:07:57 | |
Two times gravity, times the height, 5.5, | 0:07:57 | 0:08:02 | |
multiplied by the speed, divided by 49.1, | 0:08:02 | 0:08:06 | |
take the cosine... | 0:08:06 | 0:08:08 | |
That will give me a distance of 9.95 metres. | 0:08:08 | 0:08:12 | |
But we've got air resistance, there's friction on the... the ramp as well. | 0:08:12 | 0:08:17 | |
What about the wind today? 9.16. | 0:08:19 | 0:08:22 | |
OK, so the predicted distance is going to be 5.6 metres. | 0:08:22 | 0:08:28 | |
That's where I think the ball is going to land. | 0:08:31 | 0:08:35 | |
Which means if I set up my deckchair here, I should be able to watch the whole thing in complete safety. | 0:08:38 | 0:08:44 | |
OK, release the ball. | 0:08:44 | 0:08:47 | |
And that is the power of the Code. | 0:08:55 | 0:08:58 | |
We can do this again and again and again... | 0:09:02 | 0:09:04 | |
..and the numbers mean the ball is going to land in the same place each time. | 0:09:08 | 0:09:12 | |
If everything in the world behaved according to equations that give definite answers, | 0:09:18 | 0:09:23 | |
we'd be able to predict the future with absolute certainty. | 0:09:23 | 0:09:26 | |
But unfortunately things aren't quite that simple. | 0:09:28 | 0:09:32 | |
The natural world often appears so complex | 0:09:41 | 0:09:44 | |
it's hard to imagine we could write equations to describe it. | 0:09:44 | 0:09:48 | |
Even though we might glimpse what we think are patterns, | 0:09:50 | 0:09:53 | |
they seem almost impossible to understand. | 0:09:53 | 0:09:56 | |
I've come to witness a mysterious phenomenon | 0:09:56 | 0:09:59 | |
that happens here in Denmark for a few short weeks every year. | 0:09:59 | 0:10:03 | |
WINGS FLUTTER | 0:10:06 | 0:10:07 | |
BIRDS TWITTER | 0:10:10 | 0:10:13 | |
WINGS FLUTTER | 0:10:17 | 0:10:19 | |
First few appearing, I think. | 0:10:20 | 0:10:23 | |
These are starlings, | 0:10:36 | 0:10:38 | |
making their annual migration between southern Europe and Scandinavia. | 0:10:38 | 0:10:43 | |
A single flock can contain a million birds or more. | 0:10:45 | 0:10:50 | |
Their dance obscures the fading evening light, | 0:10:52 | 0:10:56 | |
giving the formation its eerie name - | 0:10:56 | 0:10:59 | |
The Black Sun. | 0:10:59 | 0:11:01 | |
There's another massive group coming in. | 0:11:02 | 0:11:06 | |
Oh! | 0:11:06 | 0:11:07 | |
There are thousands of them up there. | 0:11:11 | 0:11:13 | |
It's not really clear why they do this. | 0:11:16 | 0:11:19 | |
It's maybe like, kind of, safety in numbers. | 0:11:19 | 0:11:21 | |
The whole shape looks quite intimidating. | 0:11:21 | 0:11:24 | |
It looks like one large, black beast, | 0:11:24 | 0:11:26 | |
frightening off any predators | 0:11:26 | 0:11:29 | |
that might be looking for a bit of dinner before sunset. | 0:11:29 | 0:11:32 | |
Look at that. | 0:11:36 | 0:11:38 | |
Ah. | 0:11:38 | 0:11:40 | |
It's almost hypnotic. | 0:11:40 | 0:11:42 | |
It's amazing. There are so many of them, | 0:11:47 | 0:11:49 | |
it's a wonder they don't smash into each other | 0:11:49 | 0:11:52 | |
and sort of knock some out of the sky. But they don't seem to. | 0:11:52 | 0:11:55 | |
Incredible synchronisation. | 0:11:55 | 0:11:58 | |
Oh! | 0:12:00 | 0:12:01 | |
You're never quite sure what it's going to do next. | 0:12:05 | 0:12:08 | |
'It's an almost impossible achievement. | 0:12:09 | 0:12:12 | |
'How can each bird predict the movements of thousands of others?' | 0:12:12 | 0:12:16 | |
That's extraordinary? | 0:12:23 | 0:12:25 | |
As strange as it seems, by reducing each starling to numbers, | 0:12:33 | 0:12:38 | |
we can model what's happening on a computer. | 0:12:38 | 0:12:41 | |
We start with a flock of virtual starlings, | 0:12:45 | 0:12:48 | |
all flying at different speeds and in different directions. | 0:12:48 | 0:12:52 | |
And then we give them some simple rules. | 0:12:52 | 0:12:56 | |
The first is for each bird to fly at the same speed. | 0:12:56 | 0:13:00 | |
The second rule is to stay close to your neighbours. | 0:13:00 | 0:13:04 | |
And finally, if you see a predator nearby, get out of the way. | 0:13:06 | 0:13:11 | |
Three simple rules are all it takes to create something | 0:13:14 | 0:13:17 | |
that looks uncannily like the movement of a real flock of starlings. | 0:13:17 | 0:13:22 | |
Oh, here they come. | 0:13:25 | 0:13:26 | |
Oh! | 0:13:26 | 0:13:28 | |
HE LAUGHS | 0:13:28 | 0:13:30 | |
In fact, a recent study has shown | 0:13:31 | 0:13:34 | |
that even in a flock of hundreds of thousands of birds, | 0:13:34 | 0:13:38 | |
each starling only has to keep track of its seven nearest neighbours. | 0:13:38 | 0:13:42 | |
And then...they've all gone. | 0:13:51 | 0:13:54 | |
The sky's clear again. | 0:13:55 | 0:13:57 | |
Who'd have thought that something so extraordinarily complex | 0:14:02 | 0:14:05 | |
as a constantly shifting flock of thousands of birds in flight | 0:14:05 | 0:14:09 | |
can have at its heart such a simple and elegant Code? | 0:14:09 | 0:14:13 | |
WOMAN LAUGHS | 0:14:20 | 0:14:22 | |
CHILD LAUGHS | 0:14:22 | 0:14:23 | |
BABY CRIES | 0:14:27 | 0:14:28 | |
It seems inconceivable that human beings | 0:14:28 | 0:14:31 | |
could ever be reduced to a mathematical model like starlings. | 0:14:31 | 0:14:36 | |
CLOCK TICKS | 0:14:36 | 0:14:38 | |
But Iain Couzin studies how animals behave in groups, | 0:14:50 | 0:14:54 | |
and his research has revealed some surprising parallels. | 0:14:54 | 0:14:59 | |
How can you possibly begin to understand something like this huge mass of people? | 0:14:59 | 0:15:03 | |
Even when you look at the crowd for a few seconds, | 0:15:03 | 0:15:06 | |
you realise there's so many complicated factors at play. | 0:15:06 | 0:15:09 | |
I started my research looking at simple organisms, | 0:15:09 | 0:15:12 | |
organisms like ant swarms, schooling fish. | 0:15:12 | 0:15:14 | |
And remarkably, our insights from studying those systems | 0:15:14 | 0:15:17 | |
led to new insights in studying human crowds. | 0:15:17 | 0:15:20 | |
But people are much more complicated than a...a fish or an ant. | 0:15:20 | 0:15:24 | |
Exactly, but that's almost the beauty of this, | 0:15:24 | 0:15:26 | |
is we're thinking about more interesting things | 0:15:26 | 0:15:29 | |
when we're walking through crowds than, "How do I avoid that person and that obstacle?" | 0:15:29 | 0:15:33 | |
You know, we're thinking about what we're going to cook for dinner or what our friends are doing. | 0:15:33 | 0:15:39 | |
And so, in actual fact, we're almost on auto-pilot, | 0:15:39 | 0:15:41 | |
and we're actually using very simple rules of interaction | 0:15:41 | 0:15:44 | |
just like the schooling fish and the swarming ants. | 0:15:44 | 0:15:47 | |
So can we learn things from the ants? | 0:15:50 | 0:15:52 | |
We could learn an huge amount from the ants. | 0:15:52 | 0:15:54 | |
Ants don't suffer from problems such as congestion. | 0:15:54 | 0:15:56 | |
Because they're not selfish. And I'm afraid to say we are. | 0:15:56 | 0:16:00 | |
We want to minimise our own travel time, | 0:16:00 | 0:16:02 | |
but we don't necessarily care whether we do so at the expense of other individuals. | 0:16:02 | 0:16:07 | |
Of all the animals Iain has studied, | 0:16:08 | 0:16:11 | |
human beings are, in some ways, the most predictable. | 0:16:11 | 0:16:15 | |
We walk at an optimum speed of 1.3 metres per second, | 0:16:15 | 0:16:20 | |
and prefer to walk in straight lines to get to our destination. | 0:16:20 | 0:16:24 | |
What happens is you will naturally fall | 0:16:25 | 0:16:28 | |
into the slipstream of someone moving in the same direction as you. | 0:16:28 | 0:16:31 | |
And so without you even knowing it, you're forming a lane. | 0:16:31 | 0:16:35 | |
Similarly, pedestrians moving in the other direction will also form lanes, | 0:16:35 | 0:16:40 | |
very much like the ants do. | 0:16:40 | 0:16:42 | |
These lanes help us to avoid collisions. | 0:16:42 | 0:16:45 | |
However, in a large open space, | 0:16:45 | 0:16:47 | |
like the concourse at Grand Central Station, | 0:16:47 | 0:16:49 | |
the lanes inevitably cross each other, | 0:16:49 | 0:16:52 | |
which could lead to congestion. | 0:16:52 | 0:16:53 | |
But when you put an obstacle - like this information desk - | 0:16:55 | 0:16:59 | |
in the middle of the crowd, rather than getting in the way, | 0:16:59 | 0:17:03 | |
it acts like a roundabout | 0:17:03 | 0:17:06 | |
and increases the flow through the station by as much as 13%. | 0:17:06 | 0:17:10 | |
These rules are so effective at predicting what we'll do, | 0:17:17 | 0:17:21 | |
they can even be used to simulate crowds of people. | 0:17:21 | 0:17:25 | |
Each individual is actually described by a set of numbers | 0:17:27 | 0:17:31 | |
as they move through an environment. | 0:17:31 | 0:17:34 | |
Exactly. We're capturing the average type of behaviour of pedestrians. | 0:17:34 | 0:17:37 | |
We're capturing these simple and local rules that people use within crowds | 0:17:37 | 0:17:41 | |
to then make predictions as to how the whole crowd | 0:17:41 | 0:17:44 | |
is going to flow through different environments. | 0:17:44 | 0:17:47 | |
We can use this underlying Code of the crowd | 0:17:48 | 0:17:51 | |
to design buildings that are more efficient and safer. | 0:17:51 | 0:17:56 | |
Simulations like these are able to accurately predict | 0:17:57 | 0:18:01 | |
how quickly a building can be evacuated, | 0:18:01 | 0:18:03 | |
even before it has been built. | 0:18:03 | 0:18:06 | |
As a crowd, people are incredibly predictable. | 0:18:13 | 0:18:17 | |
There are simple rules that we follow without being aware of it. | 0:18:17 | 0:18:21 | |
But most of the time, we don't live on autopilot. | 0:18:24 | 0:18:27 | |
And when the crowd disperses, so too do the rules of group behaviour. | 0:18:28 | 0:18:34 | |
SIREN BLARES | 0:18:34 | 0:18:35 | |
As individuals with our own free will, we're much harder to predict. | 0:18:35 | 0:18:41 | |
Or so we think. | 0:18:41 | 0:18:42 | |
Before we gets started, I would like to mention the rules. | 0:18:49 | 0:18:52 | |
They are very simple. | 0:18:52 | 0:18:53 | |
There are three throws and there are only three throws. | 0:18:53 | 0:18:56 | |
We use a three-prime shoot, which means you go one, two, three | 0:18:56 | 0:19:00 | |
and you release your throw on four. | 0:19:00 | 0:19:03 | |
A throw of rock is a closed fist. | 0:19:04 | 0:19:06 | |
You can throw it any way you want as long as it is a closed fist. | 0:19:06 | 0:19:10 | |
Your paper must be horizontal. | 0:19:10 | 0:19:11 | |
Your scissors must be vertical. | 0:19:11 | 0:19:13 | |
That will be foul. | 0:19:13 | 0:19:15 | |
The game of rock, paper, scissors is known all over the world. | 0:19:18 | 0:19:22 | |
And some people take it very seriously. | 0:19:24 | 0:19:27 | |
For those of you who don't know, and there should be very few, | 0:19:27 | 0:19:30 | |
the throw of paper covers the throw of rock. | 0:19:30 | 0:19:32 | |
The throw of scissors cuts the throw of paper, | 0:19:32 | 0:19:35 | |
and the throw of rock crushes the throw of scissors. | 0:19:35 | 0:19:37 | |
In Philadelphia, the Rock, Paper, Scissors League | 0:19:42 | 0:19:45 | |
competes four times a week. | 0:19:45 | 0:19:47 | |
The people in this room are fighting | 0:19:48 | 0:19:51 | |
to go to the world championship in Las Vegas | 0:19:51 | 0:19:54 | |
and the chance to win 10,000. | 0:19:54 | 0:19:57 | |
Sweetji in the lead. Rock versus scissors for Sweetji. | 0:20:03 | 0:20:06 | |
You're on the verge of elimination, Drew Bag. | 0:20:06 | 0:20:09 | |
Third and final set, winner moves on. | 0:20:09 | 0:20:12 | |
THE CROWD CHANTS AND CLAPS | 0:20:12 | 0:20:13 | |
Rock versus scissors. | 0:20:13 | 0:20:15 | |
And what a match, to take us down to the final four. | 0:20:15 | 0:20:18 | |
The intriguing thing about this game is that it should be impossible | 0:20:20 | 0:20:24 | |
to predict what your opponent's going to do next. | 0:20:24 | 0:20:27 | |
In rock, paper, scissors, they're all pretty much equivalent. | 0:20:28 | 0:20:32 | |
So each throw beats one and loses to another, | 0:20:32 | 0:20:36 | |
so essentially it's a game of even odds. | 0:20:36 | 0:20:38 | |
A bit like a flip of a coin. | 0:20:38 | 0:20:40 | |
But if the game is entirely random, | 0:20:42 | 0:20:44 | |
every player would be evenly matched. | 0:20:44 | 0:20:47 | |
And yet some people win time and time again. | 0:20:47 | 0:20:51 | |
It is match point, Sweetji. | 0:20:51 | 0:20:52 | |
B-Pac has no points here in round number two. | 0:20:52 | 0:20:55 | |
He will need two straight throws. | 0:20:55 | 0:20:58 | |
Can he get through number one? | 0:20:58 | 0:20:59 | |
No. Sweetji! | 0:20:59 | 0:21:01 | |
So now our final match of the night. | 0:21:01 | 0:21:04 | |
Sweetji, you're going to play dOGulas. | 0:21:04 | 0:21:08 | |
The more we play, the more we're influenced by our past throws. | 0:21:08 | 0:21:12 | |
Begin. | 0:21:12 | 0:21:13 | |
And that creates patterns that can be exploited to win the game. | 0:21:13 | 0:21:17 | |
Sweetji came fifth in the league last year, | 0:21:17 | 0:21:21 | |
and this season looks set to do even better. | 0:21:21 | 0:21:24 | |
dOGulas! | 0:21:28 | 0:21:30 | |
Rock crushes scissors. | 0:21:30 | 0:21:32 | |
Sweetji still has point... | 0:21:32 | 0:21:35 | |
Rock crushes scissors! | 0:21:35 | 0:21:36 | |
SHE SCREAMS | 0:21:36 | 0:21:37 | |
Sweetji, Philadelphia Rock, Paper, Scissors City League Champion here at the Raven Lounge. | 0:21:37 | 0:21:42 | |
-Congratulations. -Thank you. | 0:21:42 | 0:21:44 | |
So that was five consecutive wins. | 0:21:44 | 0:21:47 | |
-What was the key to your success, do you think? -I try to read people. | 0:21:47 | 0:21:52 | |
-Yeah, you do, yeah? -Or at least try to think what they're thinking. | 0:21:52 | 0:21:55 | |
-You're looking for their patterns then? -Yeah, a little bit like... | 0:21:55 | 0:21:58 | |
Their patterns, and they'll be trying to learn mine and go against that. | 0:21:58 | 0:22:02 | |
Rock, paper, scissors reveals a fundamental truth | 0:22:06 | 0:22:09 | |
about human nature. | 0:22:09 | 0:22:11 | |
We are so addicted to patterns | 0:22:13 | 0:22:15 | |
that we let them seep into almost everything we do. | 0:22:15 | 0:22:17 | |
And these patterns are the key | 0:22:21 | 0:22:23 | |
to predicting many aspects of our behaviour. | 0:22:23 | 0:22:26 | |
Even the darkest parts of our nature. | 0:22:26 | 0:22:28 | |
SCREAMS | 0:22:32 | 0:22:35 | |
Deceased. Female, five foot two. | 0:22:35 | 0:22:38 | |
Complexion, dark. Eyes, brown. Hair, brown. | 0:22:38 | 0:22:42 | |
When you see this much activity in such a small geographic area | 0:22:44 | 0:22:48 | |
in such a tight time frame, | 0:22:48 | 0:22:49 | |
that's a warning bell that something's going on, | 0:22:49 | 0:22:52 | |
we have a predator operating. | 0:22:52 | 0:22:53 | |
Kim Rossmo has 20 years' experience as a Detective Inspector. | 0:22:55 | 0:23:00 | |
He specialises in hunting down serial killers. | 0:23:00 | 0:23:04 | |
The victim's body was found here in the corner | 0:23:06 | 0:23:08 | |
by a police officer that came in shortly after the crime had occurred. | 0:23:08 | 0:23:12 | |
The prime crime scene would be... | 0:23:12 | 0:23:14 | |
But Rossmo is no ordinary cop, | 0:23:15 | 0:23:17 | |
because he's got a PhD | 0:23:17 | 0:23:20 | |
and uses mathematics to understand the patterns criminals leave behind. | 0:23:20 | 0:23:25 | |
There's a logic in how the offender hunted for the victim | 0:23:29 | 0:23:32 | |
and the location where he committed the crime. | 0:23:32 | 0:23:35 | |
If we can decode that and if we can understand that pattern, | 0:23:35 | 0:23:38 | |
we can use that information to help us focus a criminal investigation. | 0:23:38 | 0:23:41 | |
The reason it's so hard to catch serial killers is because there's often no link to their crimes. | 0:23:44 | 0:23:51 | |
They kill random strangers | 0:23:51 | 0:23:53 | |
in locations they have no obvious connection to. | 0:23:53 | 0:23:56 | |
It's very common in the investigation of a serial murder case | 0:23:56 | 0:23:59 | |
to have hundreds, thousands, even tens of thousands of suspects. | 0:23:59 | 0:24:03 | |
It's a needle-in-a-haystack problem. | 0:24:03 | 0:24:05 | |
Where do you start? | 0:24:07 | 0:24:09 | |
In 1888, the most notorious serial killer of all, Jack the Ripper, | 0:24:10 | 0:24:15 | |
killed five women in London's East End. | 0:24:15 | 0:24:18 | |
Since then, countless people have tried to solve the mystery of the Ripper's identity. | 0:24:19 | 0:24:25 | |
But Rossmo thinks he could have tracked him down | 0:24:25 | 0:24:28 | |
without seeing a scrap of evidence. | 0:24:28 | 0:24:30 | |
Because he's worked out where Jack the Ripper most likely lived. | 0:24:31 | 0:24:35 | |
Based only on the location of the crimes. | 0:24:35 | 0:24:38 | |
Flower and Dean Street should have been the epicentre of their search. | 0:24:38 | 0:24:44 | |
And all he used to do it is an equation. | 0:24:44 | 0:24:47 | |
Inherently, we're all lazy, | 0:24:51 | 0:24:52 | |
and criminals just as much as anyone else. | 0:24:52 | 0:24:55 | |
They want to accomplish their goals close to home rather than further away, | 0:24:55 | 0:24:59 | |
because it involves too much effort, too much time, too much travel. | 0:24:59 | 0:25:02 | |
The first half of Rossmo's equation | 0:25:04 | 0:25:06 | |
models what's known as the least-effort principle. | 0:25:06 | 0:25:09 | |
It means that the crime locations | 0:25:09 | 0:25:12 | |
are statistically more likely the nearer they are to where the offender lives. | 0:25:12 | 0:25:17 | |
If you have a choice of going to the corner store for a loaf of bread | 0:25:17 | 0:25:20 | |
or one that's seven miles down the road, you'll pick the corner store. | 0:25:20 | 0:25:23 | |
It seems a bit gruesome to apply the same thing to a serial killer | 0:25:23 | 0:25:27 | |
as to going and buying a loaf of bread or milk. | 0:25:27 | 0:25:29 | |
Well, actually, if we can get over the horrible nature of these crimes | 0:25:29 | 0:25:34 | |
and recognise that these are human beings like the rest of us, | 0:25:34 | 0:25:38 | |
we can, because we understand ourselves, | 0:25:38 | 0:25:41 | |
maybe bet some understanding of these individuals. | 0:25:41 | 0:25:44 | |
The second half of the equation | 0:25:46 | 0:25:48 | |
describes something called the buffer zone. | 0:25:48 | 0:25:51 | |
Criminals avoid committing crimes too close to home, | 0:25:51 | 0:25:54 | |
for fear of drawing attention to themselves. | 0:25:54 | 0:25:56 | |
It's the interaction of these two behaviours that allows Rossmo | 0:25:58 | 0:26:02 | |
to calculate the most probable location of the criminal. | 0:26:02 | 0:26:05 | |
These individuals have to not only obtain their target - | 0:26:05 | 0:26:09 | |
or capture a victim - | 0:26:09 | 0:26:10 | |
but avoid apprehension by the police and identification by witnesses. | 0:26:10 | 0:26:15 | |
The technique, known as geographic profiling, | 0:26:18 | 0:26:21 | |
is now used by police all over the world. | 0:26:21 | 0:26:25 | |
Police are examining the possibility that a small explosion | 0:26:30 | 0:26:34 | |
near a branch of Barclays Bank in West London | 0:26:34 | 0:26:36 | |
was the work of an extortionist. | 0:26:36 | 0:26:38 | |
Police believe the demand | 0:26:38 | 0:26:40 | |
came from the blackmailer known as Mardi Gra. | 0:26:40 | 0:26:42 | |
In the late '90s, Rossmo was called in by Scotland Yard | 0:26:44 | 0:26:48 | |
to help catch the notorious Mardi Gra bomber, | 0:26:48 | 0:26:52 | |
who for three years waged a campaign of terror | 0:26:52 | 0:26:55 | |
against banks and supermarkets. | 0:26:55 | 0:26:58 | |
A 17-year-old man is recovering in hospital after being injured | 0:26:58 | 0:27:01 | |
in an explosion at a Sainsbury's store in South London. | 0:27:01 | 0:27:04 | |
'Police are advising the public to be vigilant. | 0:27:04 | 0:27:06 | |
'In truth, they can only wait to see what Mardi Gra does next.' | 0:27:06 | 0:27:10 | |
How many bombs did he let off during that time? | 0:27:16 | 0:27:19 | |
Total, 36 known linked offences. | 0:27:19 | 0:27:23 | |
So you can see, they range from the north of Cambridge, | 0:27:23 | 0:27:26 | |
all the way down to the strait of Dover. | 0:27:26 | 0:27:29 | |
But most of them are in Greater London. | 0:27:29 | 0:27:31 | |
So this is a map showing the locations of all the bombs that were set off? | 0:27:31 | 0:27:35 | |
-That's right. -There's certainly a concentration on London, | 0:27:35 | 0:27:39 | |
but it looks pretty randomly scattered. | 0:27:39 | 0:27:42 | |
So now you're feeding those locations into the equation? | 0:27:42 | 0:27:45 | |
Right. And what we have here now is the geo-profile. | 0:27:45 | 0:27:49 | |
And that's going to show us the most likely location | 0:27:49 | 0:27:52 | |
where the offender lived. | 0:27:52 | 0:27:54 | |
With dark orange being the most likely or the most probable. | 0:27:54 | 0:27:58 | |
So we can see that the major focus is around the Chiswick area. | 0:27:58 | 0:28:02 | |
In fact, in the report we prepared for Scotland Yard, | 0:28:02 | 0:28:05 | |
we even prioritised postcodes for that. | 0:28:05 | 0:28:07 | |
And how successful was it in this case? | 0:28:07 | 0:28:09 | |
Well, let me show you the locations... | 0:28:09 | 0:28:12 | |
of the two brothers, Edgar and Ronald Pearce. | 0:28:12 | 0:28:15 | |
-Right, that is really in the hot zone, isn't it? -Yes. | 0:28:15 | 0:28:19 | |
Edgar's home is in the top 0.8% | 0:28:19 | 0:28:21 | |
of the area of the crimes in Greater London. | 0:28:21 | 0:28:24 | |
-So less than 1%. -That's extraordinary. | 0:28:24 | 0:28:27 | |
Edgar Pearce had demanded £10,000 a day from Barclays. | 0:28:31 | 0:28:35 | |
And when he and his brother tried to collect it from a cash point in Chiswick, | 0:28:35 | 0:28:39 | |
the police were waiting. | 0:28:39 | 0:28:41 | |
Two bothers in their 60s were remanded in custody by magistrates | 0:28:41 | 0:28:44 | |
in connection with the so-called Mardi Gra bombings. | 0:28:44 | 0:28:47 | |
Ronald and Edgar Pearce, both from Chiswick in West London, | 0:28:47 | 0:28:51 | |
each face three conspiracy charges. | 0:28:51 | 0:28:54 | |
Based on the apparently random location of 36 bombs, | 0:28:54 | 0:28:58 | |
Rossmo's geographic profile narrowed the location of the Mardi Gra bomber | 0:28:58 | 0:29:04 | |
from 300 square miles to a postcode in Chiswick. | 0:29:04 | 0:29:07 | |
Although his bother, Ronald, was acquitted, | 0:29:10 | 0:29:13 | |
Edgar Pearce pleaded guilty and was jailed for 21 years. | 0:29:13 | 0:29:18 | |
So do you think the bomber was aware that he was creating these patterns? | 0:29:18 | 0:29:22 | |
No, he wasn't. But it's very difficult for humans | 0:29:22 | 0:29:25 | |
to engage in completely random behaviour. | 0:29:25 | 0:29:28 | |
Very few of us are aware of the patterns we leave behind. | 0:29:34 | 0:29:38 | |
WOMAN LAUGHS | 0:29:38 | 0:29:40 | |
From the way we move in a crowd... | 0:29:40 | 0:29:43 | |
..to the choices we make in a game... | 0:29:46 | 0:29:48 | |
Paper covers rock! | 0:29:48 | 0:29:50 | |
The victim's body was found here... | 0:29:50 | 0:29:53 | |
..or even how we commit murder. | 0:29:53 | 0:29:55 | |
In reality, these crimes are not random... | 0:29:55 | 0:29:59 | |
None of it is random. | 0:29:59 | 0:30:01 | |
It's all part of the Code. | 0:30:01 | 0:30:05 | |
There are always tell-tale patterns. | 0:30:05 | 0:30:08 | |
And if we're able to decode them, | 0:30:08 | 0:30:11 | |
we can use those patterns to model our behaviour. | 0:30:11 | 0:30:15 | |
And this leads to the intriguing possibility | 0:30:15 | 0:30:19 | |
that if we can reduce human beings to numbers, we might be able to predict our future | 0:30:19 | 0:30:25 | |
in the same way as we can predict the movement of the planets or the trajectory of a ball. | 0:30:25 | 0:30:30 | |
But the course of our lives never seems to run entirely smoothly, | 0:30:39 | 0:30:44 | |
and the future rarely turns out exactly as we'd planned. | 0:30:44 | 0:30:49 | |
I may have a good idea what I'm going to be doing tomorrow, or even next week, | 0:30:49 | 0:30:53 | |
but as the weeks turn into months and months to years, our future becomes less certain. | 0:30:53 | 0:30:58 | |
Every decision we make, every situation we encounter, | 0:31:02 | 0:31:06 | |
every person we meet, sends our life down a different path. | 0:31:06 | 0:31:11 | |
As you watch each stick floating off downstream, there's no sure way of predicting their fate. | 0:31:12 | 0:31:17 | |
I might be able to hazard a guess where a stick will be in two minutes. | 0:31:17 | 0:31:22 | |
But what about two hours? Two days? | 0:31:22 | 0:31:25 | |
'..Turn into years, our future becomes far less certain.' | 0:31:25 | 0:31:29 | |
Life sometimes seems so unpredictable that we think of it as being random. | 0:31:29 | 0:31:35 | |
But in fact it isn't random at all. | 0:31:35 | 0:31:38 | |
Simply a sequence of cause and effect. | 0:31:38 | 0:31:40 | |
A freak accident. | 0:31:40 | 0:31:44 | |
I'm so sorry. | 0:31:44 | 0:31:45 | |
A slight delay. | 0:31:45 | 0:31:47 | |
A missed bus. | 0:31:47 | 0:31:49 | |
A broken promise. | 0:31:49 | 0:31:51 | |
There are millions of factors that intervene to affect our journey through life, | 0:31:51 | 0:31:58 | |
and the tiniest shift in any one of these can completely change its future course. | 0:31:58 | 0:32:04 | |
The white one's caught in a dam, but the red one's fast. | 0:32:05 | 0:32:07 | |
I think this'd be a good finishing line. | 0:32:19 | 0:32:23 | |
And here comes the white. It's way ahead of the red. | 0:32:23 | 0:32:27 | |
And white's the winner. | 0:32:28 | 0:32:32 | |
Right, let's give it another go. | 0:32:32 | 0:32:33 | |
The truth is, our lives are controlled by the strangest code of all... | 0:32:33 | 0:32:40 | |
the code of chaos. | 0:32:40 | 0:32:42 | |
Our lives aren't random, they're chaotic, | 0:32:46 | 0:32:49 | |
a tangled web of cause and effect in which insignificant moments | 0:32:49 | 0:32:54 | |
can escalate into events that change our lives forever. | 0:32:54 | 0:32:59 | |
Any difference, no matter how small, can have a huge effect on the outcome. | 0:32:59 | 0:33:05 | |
It's this incredible sensitivity to even the slightest change | 0:33:05 | 0:33:09 | |
which is one of the defining features of chaos. | 0:33:09 | 0:33:12 | |
Because chaotic systems appear so random, it's often difficult to see a pattern. | 0:33:18 | 0:33:23 | |
And that has led us to sometimes misinterpret our world in a spectacular manner. | 0:33:25 | 0:33:31 | |
'In this land of many mysteries, it's a strange fact | 0:33:37 | 0:33:40 | |
'that large legends seem to collect around the smallest creatures. | 0:33:40 | 0:33:44 | |
'One of these is a mousy little rodent called the lemming. | 0:33:44 | 0:33:49 | |
'Here's an actual living legend, for it's said of this tiny animal | 0:33:49 | 0:33:53 | |
''that it commits mass suicide by rushing into the sea in droves. | 0:33:53 | 0:33:57 | |
This film from 1958 | 0:33:57 | 0:34:00 | |
set out to explain the wildly fluctuating population | 0:34:00 | 0:34:03 | |
of these tiny rodents. | 0:34:03 | 0:34:05 | |
'Ahead lies the Arctic shore, and beyond, the sea. And still the little animals surge forward. | 0:34:11 | 0:34:19 | |
'Their frenzy takes them tumbling down the terraced cliffs, | 0:34:21 | 0:34:25 | |
'creating tiny avalanches of sliding soil and rocks.' | 0:34:25 | 0:34:29 | |
The legend of suicidal lemmings | 0:34:33 | 0:34:35 | |
was the accepted explanation for why the Arctic can be overrun with them one year | 0:34:35 | 0:34:39 | |
and completely empty the next. | 0:34:39 | 0:34:41 | |
'They reach the final precipice. | 0:34:41 | 0:34:45 | |
'This is the last chance to turn back. | 0:34:45 | 0:34:48 | |
'Yet over they go, casting themselves bodily out into space.' | 0:34:52 | 0:34:56 | |
This film popularised the belief that lemmings are stupid, reckless and suicidal. | 0:35:01 | 0:35:07 | |
The very word "lemming" has come to mean as much. | 0:35:07 | 0:35:10 | |
The trouble is, though, it isn't true. | 0:35:15 | 0:35:18 | |
In fact, it's been claimed that the whole thing was faked. | 0:35:18 | 0:35:22 | |
The film-makers apparently flew in hundreds of captive-bred lemmings | 0:35:26 | 0:35:30 | |
and drove them over the cliffs and out to sea. | 0:35:30 | 0:35:33 | |
'Soon the Arctic Sea is dotted with tiny bobbing bodies. | 0:35:37 | 0:35:40 | |
'And so is acted out the legend of mass suicide.' | 0:35:43 | 0:35:48 | |
Now, as appalling as this sounds, the reason for the alleged lemming abuse stems not so much | 0:35:48 | 0:35:53 | |
from ignoring the moral code, | 0:35:53 | 0:35:55 | |
but rather an ignorance of the mathematical one. | 0:35:55 | 0:35:58 | |
What no-one knew at the time was that the incredible fluctuation | 0:35:58 | 0:36:04 | |
in lemming numbers has nothing to do with mass suicide. | 0:36:04 | 0:36:08 | |
It's all because of chaos. | 0:36:09 | 0:36:12 | |
And there's a simple equation at its heart. | 0:36:12 | 0:36:15 | |
So, if I want to know how many lemmings there'll be next year, | 0:36:17 | 0:36:22 | |
what I need to do is take this year's population, "P", | 0:36:22 | 0:36:26 | |
and multiply that by the growth rate "R". | 0:36:26 | 0:36:29 | |
But not all lemmings will survive, | 0:36:29 | 0:36:31 | |
so there's a bit of the equation which tells me how many lemmings will die during the year. | 0:36:31 | 0:36:36 | |
So that's R times P times P. | 0:36:36 | 0:36:39 | |
So we can rewrite this equation | 0:36:39 | 0:36:42 | |
as the growth rate R times P times one minus P. | 0:36:42 | 0:36:47 | |
Now, this equation isn't specific to lemmings, | 0:36:47 | 0:36:50 | |
it actually applies to any animal population. | 0:36:50 | 0:36:52 | |
And the interesting part of the equation is this number R, the growth rate. | 0:36:52 | 0:36:57 | |
Because when we choose different values for R, | 0:36:57 | 0:37:00 | |
we get a very different behaviour for the population growth. | 0:37:00 | 0:37:03 | |
The growth rate determines how quickly a population expands. | 0:37:04 | 0:37:08 | |
For most species of mammal, this is usually below 2. | 0:37:08 | 0:37:12 | |
With a growth rate in this range, the equation predicts | 0:37:12 | 0:37:16 | |
that a population will rise until it stabilises at a fixed value. | 0:37:16 | 0:37:20 | |
But it turns out lemmings are one of the fastest-reproducing mammals on the planet. | 0:37:20 | 0:37:26 | |
Let's take R equals 3.1. | 0:37:26 | 0:37:30 | |
The lemmings don't stabilise now, but ping-pong between two different values. | 0:37:30 | 0:37:35 | |
So the population is high, then low, and back to high again, low again. | 0:37:35 | 0:37:40 | |
But when the growth rate reaches a value just over 3.57 | 0:37:40 | 0:37:44 | |
then something incredibly unexpected happens. | 0:37:44 | 0:37:49 | |
Rather than levelling off at a fixed number, | 0:37:49 | 0:37:52 | |
or fluctuating between two values, their population erupts into chaos. | 0:37:52 | 0:37:58 | |
A plague of almost biblical proportions one year can plummet to near extinction the next. | 0:37:58 | 0:38:04 | |
It's almost impossible to predict how many lemmings you're going to have. | 0:38:04 | 0:38:09 | |
In fact, there doesn't seem to be any pattern to this at all. | 0:38:09 | 0:38:12 | |
And of course, this is exactly what's seen in reality. | 0:38:12 | 0:38:16 | |
Unpredictable boom-and-bust lemming populations. | 0:38:16 | 0:38:20 | |
Lemmings are one of the few creatures on Earth that breed so quickly | 0:38:21 | 0:38:25 | |
their growth rate can sometimes exceed this tipping point. | 0:38:25 | 0:38:29 | |
It's such an odd phenomenon that mass suicide seems like a plausible answer. | 0:38:32 | 0:38:37 | |
But the real explanation comes from the Code. | 0:38:37 | 0:38:41 | |
From this equation. | 0:38:41 | 0:38:42 | |
The problem is we can never know exactly how many lemmings are born or how many die. | 0:38:48 | 0:38:53 | |
And just the smallest difference in the growth rate R, produces a totally different answer. | 0:38:53 | 0:39:00 | |
And this is true of all equations that model chaos. | 0:39:00 | 0:39:04 | |
Although they can explain how something happens, | 0:39:04 | 0:39:08 | |
they're almost useless at predicting the future. | 0:39:08 | 0:39:11 | |
I can use an equation to calculate where this ball will land, | 0:39:19 | 0:39:22 | |
because even if I'm slightly out in any of my measurements, | 0:39:22 | 0:39:25 | |
it will only make a small difference to the final result. | 0:39:25 | 0:39:28 | |
The ball will be released from the ramp at 49.1 degrees. | 0:39:28 | 0:39:34 | |
But if this ball behaved according to the laws of chaos, | 0:39:34 | 0:39:37 | |
the tiniest shift in the ball's position | 0:39:37 | 0:39:40 | |
or the angle of release could dramatically alter its trajectory. | 0:39:40 | 0:39:45 | |
I'd have no idea whether it would just simply fall harmlessly | 0:39:47 | 0:39:50 | |
off the end of the ramp. | 0:39:50 | 0:39:52 | |
Or be sent into orbit. | 0:39:55 | 0:39:58 | |
I'd have no idea where to put my deckchair. | 0:40:02 | 0:40:06 | |
It turns out that much of the world is chaotic, | 0:40:08 | 0:40:12 | |
making it almost impossible to predict. | 0:40:12 | 0:40:15 | |
But that doesn't stop us trying. | 0:40:17 | 0:40:20 | |
Knowing whether the sun is going to shine | 0:40:24 | 0:40:27 | |
or the heavens are going to open, is a British obsession. | 0:40:27 | 0:40:30 | |
But trying to plan our lives around the vagaries of the weather | 0:40:30 | 0:40:34 | |
seems almost futile. | 0:40:34 | 0:40:36 | |
Even though we have precise equations that can describe how clashing air masses interact | 0:40:42 | 0:40:48 | |
to create clouds, wind and rainfall, | 0:40:48 | 0:40:50 | |
it doesn't really help us very much with our predictions. | 0:40:50 | 0:40:56 | |
THUNDER CRASHES | 0:40:56 | 0:40:59 | |
That's because we can never know the exact speed of every air particle. | 0:40:59 | 0:41:05 | |
The precise temperature at every point in space, | 0:41:05 | 0:41:08 | |
or the pressure across the whole planet. | 0:41:08 | 0:41:11 | |
And just a small variation in any one of these | 0:41:11 | 0:41:14 | |
can produce a vastly different forecast. | 0:41:14 | 0:41:16 | |
This is a map of how the weather looks right now. | 0:41:22 | 0:41:26 | |
The blue lines represent cold fronts and the red lines represent warm fronts. | 0:41:26 | 0:41:31 | |
In order to make a prediction | 0:41:31 | 0:41:33 | |
what we do is to take the mathematical equations for the weather | 0:41:33 | 0:41:36 | |
and create a model. | 0:41:36 | 0:41:38 | |
Now the trouble is, I can't know the precise atmospheric conditions, | 0:41:38 | 0:41:43 | |
so I take as much data as possible. | 0:41:43 | 0:41:45 | |
Then I make small variations in the data and run the model again | 0:41:45 | 0:41:50 | |
and again and again and what I get is different predictions according to those slight variations. | 0:41:50 | 0:41:56 | |
So for the weather tomorrow, the predictions are petty similar. | 0:41:56 | 0:42:00 | |
We've got a lot of blue lines together predicting a cold front. | 0:42:00 | 0:42:05 | |
A lot of red lines together predicting a warm front. | 0:42:05 | 0:42:08 | |
But look what happens when I look a little bit further ahead. | 0:42:08 | 0:42:11 | |
So two days, three days ahead... | 0:42:11 | 0:42:15 | |
so you can see these different predictions are beginning to spread out. | 0:42:15 | 0:42:20 | |
You can still see some sort of pattern in the weather | 0:42:20 | 0:42:23 | |
but if I move a week ahead... | 0:42:23 | 0:42:26 | |
..and I couldn't hazard a guess as to what the weather's going to be. | 0:42:27 | 0:42:31 | |
There are red and blue lines all over the place. | 0:42:31 | 0:42:33 | |
One prediction says it's going to be hot, the other says cold, | 0:42:33 | 0:42:37 | |
and if I go ten days ahead, | 0:42:37 | 0:42:40 | |
it just looks like a scrambled mess of spaghetti. | 0:42:40 | 0:42:44 | |
There's absolutely no way to make any prediction that far in advance. | 0:42:44 | 0:42:49 | |
And that's why beyond just a few days, | 0:42:49 | 0:42:52 | |
the weather forecast can be so spectacularly wrong. | 0:42:52 | 0:42:55 | |
Once we understand that the atmosphere is chaotic, | 0:43:00 | 0:43:03 | |
we can appreciate that the smallest change in the initial conditions | 0:43:03 | 0:43:08 | |
can dramatically alter what will happen. | 0:43:08 | 0:43:11 | |
The movement of just one molecule of air can be magnified over time | 0:43:14 | 0:43:18 | |
to have a huge effect on the weather as a whole. | 0:43:18 | 0:43:21 | |
We refer to this phenomenon as the "butterfly effect". | 0:43:24 | 0:43:28 | |
The idea that something as small as the flap of a butterfly's wings | 0:43:28 | 0:43:33 | |
might create changes in the atmosphere | 0:43:33 | 0:43:36 | |
that could ultimately lead to a tornado on the other side of the world. | 0:43:36 | 0:43:40 | |
CRASHING THUNDER | 0:43:40 | 0:43:43 | |
As a crowd, the patterns we make are incredibly predictable. | 0:43:58 | 0:44:03 | |
Even as individuals our actions are controlled by the Code. | 0:44:04 | 0:44:09 | |
And by untangling chaotic systems like the weather, | 0:44:13 | 0:44:16 | |
we've uncovered evidence of the Code in what we once thought of | 0:44:16 | 0:44:21 | |
as impossibly complex. | 0:44:21 | 0:44:24 | |
When we look at things from a different angle, | 0:44:26 | 0:44:28 | |
surprising patterns emerge. | 0:44:28 | 0:44:31 | |
Patterns that can reveal defining truths about ourselves and our future. | 0:44:33 | 0:44:39 | |
In 1906, an unfortunate cow laid down its life | 0:44:47 | 0:44:51 | |
for a place in mathematical history. | 0:44:51 | 0:44:54 | |
One. | 0:44:54 | 0:44:55 | |
Ten. | 0:44:55 | 0:44:57 | |
264. | 0:44:57 | 0:44:59 | |
417. | 0:44:59 | 0:45:01 | |
'The cow was the subject of a guess-the-weight competition at a village fare. | 0:45:01 | 0:45:06 | |
'The lucky person who came closest | 0:45:06 | 0:45:09 | |
'would win the slaughtered animal's meat.' | 0:45:09 | 0:45:12 | |
1,020. | 0:45:13 | 0:45:14 | |
2,137. | 0:45:16 | 0:45:17 | |
'The amazing thing was nobody guessed correctly.' | 0:45:17 | 0:45:20 | |
..570. | 0:45:20 | 0:45:21 | |
'And yet everybody got it right.' | 0:45:21 | 0:45:24 | |
4,510. | 0:45:26 | 0:45:28 | |
To show you how they did it, | 0:45:30 | 0:45:32 | |
I'm not going to use a cow, I'm going to use a jar of jelly beans. | 0:45:32 | 0:45:36 | |
450? | 0:45:40 | 0:45:41 | |
800? | 0:45:41 | 0:45:43 | |
12,000. | 0:45:43 | 0:45:44 | |
7,000. | 0:45:44 | 0:45:45 | |
How many jellybeans do you think there are in this jar? | 0:45:45 | 0:45:48 | |
Um, | 0:45:48 | 0:45:50 | |
50... | 0:45:50 | 0:45:52 | |
80 thousand. | 0:45:52 | 0:45:54 | |
80 thousand? | 0:45:54 | 0:45:55 | |
No, actually 50,000. | 0:45:55 | 0:45:58 | |
50,000. OK, yeah. | 0:45:58 | 0:46:00 | |
It's incredibly difficult for anyone to guess how many jellybeans there are. | 0:46:04 | 0:46:09 | |
I asked 160 people and most were way off the mark. | 0:46:10 | 0:46:15 | |
Everything from 400 right up to 50,000 beans. | 0:46:15 | 0:46:20 | |
In fact only four people got anywhere near the correct answer of 4,510. | 0:46:20 | 0:46:27 | |
Plus 1,500, plus 3,217, plus 83... . | 0:46:27 | 0:46:36 | |
If I add all the answers together and take the average, I get the combined guess of the entire group. | 0:46:36 | 0:46:43 | |
Plus, 4,000, plus 5,000, | 0:46:43 | 0:46:46 | |
463, | 0:46:46 | 0:46:48 | |
Plus 853, plus 1,000, | 0:46:48 | 0:46:52 | |
plus 5,000... | 0:46:52 | 0:46:55 | |
Which gives a grand total | 0:46:55 | 0:46:58 | |
of 722,383.5. | 0:46:58 | 0:47:04 | |
Somebody thought there was half a bean in there. | 0:47:04 | 0:47:06 | |
Now there are 160 guesses made, so let's see how close they are collectively. | 0:47:06 | 0:47:13 | |
Wow, that's extraordinary. | 0:47:13 | 0:47:16 | |
You remember there were 4,510. | 0:47:16 | 0:47:20 | |
The average guess to the nearest bean is 4,515. | 0:47:20 | 0:47:26 | |
I thought it would be close, but I didn't think it would be THAT close. | 0:47:26 | 0:47:30 | |
That is ridiculous. | 0:47:30 | 0:47:31 | |
Though we had guesses that were all over the place, up in 30,000s right down in the 400s, | 0:47:31 | 0:47:36 | |
collectively we get something which is just 0.1% away | 0:47:36 | 0:47:40 | |
from the real number of beans in there. | 0:47:40 | 0:47:43 | |
So as individuals the guesses are just that, guesses. | 0:47:43 | 0:47:47 | |
But when you take them collectively they become something else entirely. | 0:47:47 | 0:47:52 | |
-5,000. -1,450. -9,200. | 0:47:52 | 0:47:57 | |
What tends to happen is that more or less as many people | 0:47:57 | 0:48:01 | |
will underestimate the number of jellybeans as overestimate it. | 0:48:01 | 0:48:05 | |
-1,763... -6,000. | 0:48:05 | 0:48:08 | |
A few people will be way off the mark either way, but that doesn't matter. | 0:48:08 | 0:48:13 | |
Provided you ask enough people, the errors should cancel each other out. | 0:48:13 | 0:48:19 | |
-1,000. -1,275. -700? | 0:48:19 | 0:48:24 | |
The accuracy of the group is far greater than the individual. | 0:48:24 | 0:48:28 | |
We call it "the wisdom of the crowd". | 0:48:28 | 0:48:31 | |
160 people is a powerful tool for working out how many jellybeans there are in the jar. | 0:48:31 | 0:48:40 | |
But imagine what you could do with a crowd of millions. | 0:48:40 | 0:48:44 | |
That's exactly what they use here at Google. | 0:48:47 | 0:48:50 | |
With access to over two billion web searches a day, | 0:48:52 | 0:48:55 | |
Google have found a way of tapping into the wisdom of the biggest crowd on Earth. | 0:48:55 | 0:49:00 | |
And by doing so, | 0:49:00 | 0:49:02 | |
they've been able to reveal the forces that control our lives, | 0:49:02 | 0:49:07 | |
and harness them to make predictions about us. | 0:49:07 | 0:49:11 | |
'Think of the things that people search for on a daily basis. | 0:49:11 | 0:49:14 | |
'Think of the things that YOU search for on a daily basis.' | 0:49:14 | 0:49:17 | |
I searched for cities in Mexico and films in Hackney today. | 0:49:17 | 0:49:23 | |
Lots of people may be searching for the similar... | 0:49:23 | 0:49:26 | |
a similar thing, movies in Hackney, for example. | 0:49:26 | 0:49:29 | |
And if you look at that query over the past three years, | 0:49:29 | 0:49:33 | |
um, what the pattern of searches for that term looks like. | 0:49:33 | 0:49:36 | |
Google had a hunch they could use all our searches | 0:49:40 | 0:49:43 | |
to make predictions about our lives. | 0:49:43 | 0:49:46 | |
They wanted to see if they could match the pattern of certain searches | 0:49:47 | 0:49:51 | |
with events in the real world. | 0:49:51 | 0:49:53 | |
Google began by seeing if they could predict outbreaks of flu. | 0:49:55 | 0:49:59 | |
So flu has a nice seasonal pattern | 0:50:01 | 0:50:04 | |
and because it has that pattern every year over many years, | 0:50:04 | 0:50:09 | |
we're able to...to take that trend | 0:50:09 | 0:50:12 | |
and say which search queries match that pattern. | 0:50:12 | 0:50:16 | |
So we built a database that included over 50 million different search terms. | 0:50:16 | 0:50:21 | |
50 million? | 0:50:21 | 0:50:22 | |
-Yes. -Wow, yeah. | 0:50:22 | 0:50:24 | |
We didn't only include things that may be related to flu. | 0:50:24 | 0:50:27 | |
We included things like Britney Spears or... | 0:50:27 | 0:50:29 | |
Everything people search for would be included. | 0:50:29 | 0:50:32 | |
When Google looked back over the past five years of data, | 0:50:35 | 0:50:38 | |
there were certain search terms whose popularity exactly matched | 0:50:38 | 0:50:43 | |
the pattern of flu cases. | 0:50:43 | 0:50:45 | |
So people were searching for things like "symptoms" | 0:50:45 | 0:50:49 | |
or "medications" or "sore throat". | 0:50:49 | 0:50:52 | |
There are other things like complications. | 0:50:52 | 0:50:56 | |
So you're saying that the sort of number of search terms for flu-related things | 0:50:56 | 0:51:01 | |
-almost exactly mirrors the actual cases of flu that we see in the population? -That's true. | 0:51:01 | 0:51:07 | |
It is an indicator of flu activity just based on lots of people searching for these terms. | 0:51:07 | 0:51:12 | |
We were amazed by this finding. | 0:51:12 | 0:51:14 | |
As soon as they see this pattern of search terms | 0:51:19 | 0:51:22 | |
Google can predict there will be an outbreak of flu. | 0:51:22 | 0:51:26 | |
Often before people had even gone to the doctor. | 0:51:26 | 0:51:29 | |
This is the extraordinary power of the Code. | 0:51:31 | 0:51:36 | |
But it's just the tip of the iceberg. | 0:51:38 | 0:51:42 | |
The searches we make can be used to predict | 0:51:42 | 0:51:44 | |
where we'll go on holiday. | 0:51:44 | 0:51:46 | |
What model of car we're going to buy. | 0:51:46 | 0:51:48 | |
Or how we're going to vote, | 0:51:48 | 0:51:50 | |
often before we know ourselves. | 0:51:50 | 0:51:53 | |
It's even been possible to forecast the movement of the Stock Market | 0:51:55 | 0:51:59 | |
from the number of negative words used on Twitter. | 0:51:59 | 0:52:02 | |
Analysing such vast amounts of data, doesn't just allow us to make predictions. | 0:52:06 | 0:52:12 | |
It can also tell us something fundamental about ourselves. | 0:52:12 | 0:52:17 | |
You look out at a city like this and it looks like, you know, some arbitrary jumbled mess. | 0:52:20 | 0:52:26 | |
Yet the city IS people. | 0:52:26 | 0:52:30 | |
It's not the buildings and the streets. | 0:52:30 | 0:52:32 | |
They're the stage upon which the real actors | 0:52:32 | 0:52:36 | |
are playing out the story of civilisation. | 0:52:36 | 0:52:39 | |
Geoffrey West is a physicist who's spent his life trying to see meaningful patterns in the universe. | 0:52:42 | 0:52:48 | |
And how he's turned his attention to the dynamics of human life in cities. | 0:52:48 | 0:52:55 | |
So you can see there's all kinds of infrastructure here. | 0:52:59 | 0:53:03 | |
There's the obvious, the roads, the electrical lines, the sewer lines. | 0:53:03 | 0:53:09 | |
They're an extraordinary network that is sustaining New York City. | 0:53:09 | 0:53:13 | |
You know, coming at it as a physicist, | 0:53:13 | 0:53:16 | |
I had this hunch that there is an underlying code to all this. | 0:53:16 | 0:53:21 | |
West amassed data about cities all over the world. | 0:53:24 | 0:53:29 | |
And the patterns he found mean that for any given population size, | 0:53:29 | 0:53:33 | |
he can predict the amount of roads, | 0:53:33 | 0:53:35 | |
electrical wiring or office space that city has. | 0:53:35 | 0:53:40 | |
But he also discovered something much more surprising. | 0:53:43 | 0:53:46 | |
One of the most interesting results we discovered was that, um... | 0:53:50 | 0:53:56 | |
Wages scale in a very systematic way | 0:53:56 | 0:54:00 | |
and the rule that came out was that if you doubled the size of the city, | 0:54:00 | 0:54:04 | |
you get this marvellous 15% increase in the wages. | 0:54:04 | 0:54:08 | |
-If you live in a large city, you're going to earn more? -Yes. | 0:54:08 | 0:54:13 | |
So what, if there are two mathematicians | 0:54:13 | 0:54:15 | |
in two different cities - one twice the size - doing the same job, | 0:54:15 | 0:54:19 | |
-one will have a bigger income? -On the average, that is what the data say. | 0:54:19 | 0:54:24 | |
-Was that a surprise to you to see that? -A huge surprise. | 0:54:24 | 0:54:26 | |
I thought there was something wrong with the data. | 0:54:26 | 0:54:29 | |
And then it was like, "Of course! That's why cities exist." | 0:54:29 | 0:54:35 | |
Incredibly, it's not just people's salaries that increase. | 0:54:40 | 0:54:43 | |
When a city doubles in size, every measure of social and economic activity | 0:54:43 | 0:54:49 | |
goes up by 15% per person. | 0:54:49 | 0:54:52 | |
That's 15% more restaurants to choose from. | 0:54:52 | 0:54:56 | |
15% more art galleries to visit. 15% more shops to go to. | 0:54:56 | 0:55:00 | |
In short, life gets 15% better. | 0:55:01 | 0:55:05 | |
You know it looks like it's a magic formula | 0:55:11 | 0:55:13 | |
that we as social human beings have discovered... | 0:55:13 | 0:55:17 | |
..this 15% bonus, so to speak, is, I believe, the reason | 0:55:20 | 0:55:25 | |
that people are attracted to cities | 0:55:25 | 0:55:28 | |
and why there has been this continuous migration | 0:55:28 | 0:55:32 | |
from the countryside and into the cities. | 0:55:32 | 0:55:35 | |
And at some deeper level, actually drive our civilisation. | 0:55:35 | 0:55:40 | |
According to Geoffrey West, humankind has an ultimate number. | 0:55:43 | 0:55:48 | |
It's this extra 15% | 0:55:48 | 0:55:51 | |
or 1.15. | 0:55:51 | 0:55:53 | |
He believes it's the most important driving force in humanity. | 0:55:53 | 0:55:58 | |
This single number, 1.15, | 0:56:00 | 0:56:03 | |
predicts our future. | 0:56:03 | 0:56:05 | |
It will bring us together in ever expanding cities | 0:56:06 | 0:56:10 | |
and shape our destiny for as long as human beings exist. | 0:56:10 | 0:56:14 | |
Five hundred years ago, when faced with an eclipse, | 0:56:25 | 0:56:29 | |
many of us would have believed it was the work of an angry god. | 0:56:29 | 0:56:33 | |
But as we've unearthed the language of the Code, | 0:56:33 | 0:56:35 | |
we've discovered that the apparent mysteries of our world | 0:56:35 | 0:56:39 | |
can be understood without invoking the supernatural. | 0:56:39 | 0:56:43 | |
And this for me is what's so remarkable. | 0:56:43 | 0:56:46 | |
That despite the incredible complexity of the world we live in, | 0:56:46 | 0:56:50 | |
it can all, ultimately, be explained by numbers. | 0:56:50 | 0:56:54 | |
Just like the orbit of the planets, life too follows a pattern. | 0:56:57 | 0:57:02 | |
And it can all be reduced to cause and effect. | 0:57:04 | 0:57:07 | |
In the end, even the flip of a coin | 0:57:11 | 0:57:13 | |
is determined by how fast it's spinning | 0:57:13 | 0:57:16 | |
and how long it takes to hit the ground. | 0:57:16 | 0:57:18 | |
The ultimate symbol of chance isn't random at all. | 0:57:18 | 0:57:23 | |
It only appears that way. | 0:57:23 | 0:57:26 | |
When we don't understand the Code, | 0:57:28 | 0:57:31 | |
the only way we can make sense of our world is to make up stories. | 0:57:31 | 0:57:36 | |
But the truth is far more extraordinary. | 0:57:37 | 0:57:40 | |
Everything has mathematics at its heart. | 0:57:42 | 0:57:46 | |
When everything is stripped away all that remains is the Code. | 0:57:47 | 0:57:52 | |
Find clues to help you solve the Code's treasure hunt at... | 0:57:57 | 0:58:05 | |
Plus get a free set of mathematical puzzles and a treasure hunt clue | 0:58:05 | 0:58:10 | |
when you follow the links to the Open University. | 0:58:10 | 0:58:14 | |
Or call 0845 366 8026. | 0:58:14 | 0:58:19 | |
Subtitles by Red Bee Media Ltd | 0:58:32 | 0:58:35 | |
E-mail [email protected] | 0:58:35 | 0:58:38 |