0:00:04 > 0:00:07For as long as human beings have walked upon earth,
0:00:07 > 0:00:09we've tried to make sense of our world
0:00:09 > 0:00:12and predict what the future will bring.
0:00:18 > 0:00:20Yet today,
0:00:20 > 0:00:23our lives seem more complicated and unpredictable than ever.
0:00:25 > 0:00:27And half the population of the planet
0:00:27 > 0:00:30now live in busy, sprawling cities.
0:00:31 > 0:00:35Every day throws up thousands of different encounters.
0:00:35 > 0:00:39A mass of interactions and forces that seem beyond our control.
0:00:39 > 0:00:41WOMAN LAUGHS
0:00:41 > 0:00:45It's hard to see how any of this could be connected.
0:00:45 > 0:00:46BABY CRIES
0:00:47 > 0:00:51Yet when we start to look closely at all this complexity,
0:00:51 > 0:00:54surprising patterns begin to emerge.
0:00:59 > 0:01:03It's these patterns that I believe point to an underlying Code
0:01:03 > 0:01:06at the very heart of existence
0:01:06 > 0:01:10that controls not only our world and everything in it, but even us.
0:01:46 > 0:01:51As a mathematician, I'm fascinated by the patterns we see all around us.
0:01:55 > 0:01:59Patterns that reflect the hidden connections between everything.
0:02:01 > 0:02:04From the movement of rush hour crowds...
0:02:07 > 0:02:10..to the shifting shape of a flock of starlings.
0:02:13 > 0:02:17The cacophony of a billion Internet searches...
0:02:17 > 0:02:19and the vagaries of the weather.
0:02:19 > 0:02:21THUNDER ROLLS
0:02:26 > 0:02:28CHEERING
0:02:31 > 0:02:34Together, these patterns and connections make up the Code.
0:02:36 > 0:02:41A model of our world that describes not only how it works,
0:02:41 > 0:02:44but can also predict what our future holds.
0:03:00 > 0:03:05Around 500 years ago, a ship was caught in a terrible storm.
0:03:05 > 0:03:06As rain lashed the decks
0:03:06 > 0:03:09and gale force winds tore through the rigging,
0:03:09 > 0:03:11the ship began to take on water.
0:03:11 > 0:03:16The captain had no choice but to run his ship aground and wait for help.
0:03:19 > 0:03:23But help never arrived, and the natives were hostile.
0:03:26 > 0:03:30After eight long months, and with his crew facing certain starvation,
0:03:30 > 0:03:33the captain came up with an ingenious plan.
0:03:33 > 0:03:37He summoned the local chief and told him his God was angry.
0:03:38 > 0:03:42So angry, in fact, that if they didn't bring supplies within three days,
0:03:42 > 0:03:44God would swallow the moon.
0:03:50 > 0:03:56And sure enough, as the moon rose on the third night, it had already begun to disappear.
0:04:04 > 0:04:08Terrified, the locals ran from all directions towards the ship,
0:04:08 > 0:04:09laden with provisions.
0:04:16 > 0:04:19The year was 1504, and the captain?
0:04:19 > 0:04:21Christopher Columbus.
0:04:21 > 0:04:25And the reason he was apparently able to command the heavens
0:04:25 > 0:04:28was because he had something like this.
0:04:31 > 0:04:32It's a set of lunar tables.
0:04:32 > 0:04:37And each one of these numbers represents a lunar eclipse.
0:04:37 > 0:04:43Today's date is June 15th, and it says that in about five hours' time
0:04:43 > 0:04:47the same thing is going to happen to the moon here in Cyprus.
0:04:53 > 0:04:57During a lunar eclipse, the earth passes between the sun and the moon,
0:04:57 > 0:05:00casting its shadow across the lunar surface.
0:05:09 > 0:05:10And there it goes.
0:05:10 > 0:05:15The moon has been swallowed up by the shadow of the Earth.
0:05:15 > 0:05:19But the amazing thing is actually the moon doesn't completely disappear, cos...
0:05:19 > 0:05:24there's a kind of... red, ghostly moon up there.
0:05:24 > 0:05:30And that's because the light from the sun is being refracted around the Earth.
0:05:31 > 0:05:33Really quite spooky.
0:05:38 > 0:05:44I can imagine how terrified the islanders would have been when they saw that 500 years ago.
0:05:44 > 0:05:49And the only explanation for them would have been that the gods really were angry with them.
0:05:57 > 0:06:03We now know that the movement of the planets is incredibly predictable.
0:06:03 > 0:06:08By understanding the Code, we can model their orbits far back into the past.
0:06:08 > 0:06:12And see thousands of years into the future.
0:06:21 > 0:06:25It's thanks to the Code that we're no longer frightened by an eclipse.
0:06:25 > 0:06:28In fact, the Code is such a powerful thing
0:06:28 > 0:06:32that I'm even prepared to entrust my life to it.
0:06:54 > 0:06:58This strange contraption is five and a half metres high.
0:06:59 > 0:07:02Using the force of gravity,
0:07:02 > 0:07:07a 30-kilogram ball will hurtle down the ramp and fire off the end.
0:07:07 > 0:07:13And when it does, I will be sitting directly in its path.
0:07:13 > 0:07:16If I get my sums wrong, I'll be killed outright.
0:07:23 > 0:07:26To calculate how far the ball's going to go,
0:07:26 > 0:07:29I need some key measurements about the ramp.
0:07:31 > 0:07:35Little h is 0.98 metres.
0:07:35 > 0:07:41The angle is 49.1 degree.
0:07:41 > 0:07:43So gravity, I know, on the Earth...
0:07:43 > 0:07:49is 9.8 metres per second squared.
0:07:49 > 0:07:53Interestingly, you don't have to know the weight of the ball, the mass of the ball.
0:07:53 > 0:07:57That's not relevant to how far the thing's going to go.
0:07:57 > 0:08:02Two times gravity, times the height, 5.5,
0:08:02 > 0:08:06multiplied by the speed, divided by 49.1,
0:08:06 > 0:08:08take the cosine...
0:08:08 > 0:08:12That will give me a distance of 9.95 metres.
0:08:12 > 0:08:17But we've got air resistance, there's friction on the... the ramp as well.
0:08:19 > 0:08:22What about the wind today? 9.16.
0:08:22 > 0:08:28OK, so the predicted distance is going to be 5.6 metres.
0:08:31 > 0:08:35That's where I think the ball is going to land.
0:08:38 > 0:08:44Which means if I set up my deckchair here, I should be able to watch the whole thing in complete safety.
0:08:44 > 0:08:47OK, release the ball.
0:08:55 > 0:08:58And that is the power of the Code.
0:09:02 > 0:09:04We can do this again and again and again...
0:09:08 > 0:09:12..and the numbers mean the ball is going to land in the same place each time.
0:09:18 > 0:09:23If everything in the world behaved according to equations that give definite answers,
0:09:23 > 0:09:26we'd be able to predict the future with absolute certainty.
0:09:28 > 0:09:32But unfortunately things aren't quite that simple.
0:09:41 > 0:09:44The natural world often appears so complex
0:09:44 > 0:09:48it's hard to imagine we could write equations to describe it.
0:09:50 > 0:09:53Even though we might glimpse what we think are patterns,
0:09:53 > 0:09:56they seem almost impossible to understand.
0:09:56 > 0:09:59I've come to witness a mysterious phenomenon
0:09:59 > 0:10:03that happens here in Denmark for a few short weeks every year.
0:10:06 > 0:10:07WINGS FLUTTER
0:10:10 > 0:10:13BIRDS TWITTER
0:10:17 > 0:10:19WINGS FLUTTER
0:10:20 > 0:10:23First few appearing, I think.
0:10:36 > 0:10:38These are starlings,
0:10:38 > 0:10:43making their annual migration between southern Europe and Scandinavia.
0:10:45 > 0:10:50A single flock can contain a million birds or more.
0:10:52 > 0:10:56Their dance obscures the fading evening light,
0:10:56 > 0:10:59giving the formation its eerie name -
0:10:59 > 0:11:01The Black Sun.
0:11:02 > 0:11:06There's another massive group coming in.
0:11:06 > 0:11:07Oh!
0:11:11 > 0:11:13There are thousands of them up there.
0:11:16 > 0:11:19It's not really clear why they do this.
0:11:19 > 0:11:21It's maybe like, kind of, safety in numbers.
0:11:21 > 0:11:24The whole shape looks quite intimidating.
0:11:24 > 0:11:26It looks like one large, black beast,
0:11:26 > 0:11:29frightening off any predators
0:11:29 > 0:11:32that might be looking for a bit of dinner before sunset.
0:11:36 > 0:11:38Look at that.
0:11:38 > 0:11:40Ah.
0:11:40 > 0:11:42It's almost hypnotic.
0:11:47 > 0:11:49It's amazing. There are so many of them,
0:11:49 > 0:11:52it's a wonder they don't smash into each other
0:11:52 > 0:11:55and sort of knock some out of the sky. But they don't seem to.
0:11:55 > 0:11:58Incredible synchronisation.
0:12:00 > 0:12:01Oh!
0:12:05 > 0:12:08You're never quite sure what it's going to do next.
0:12:09 > 0:12:12'It's an almost impossible achievement.
0:12:12 > 0:12:16'How can each bird predict the movements of thousands of others?'
0:12:23 > 0:12:25That's extraordinary?
0:12:33 > 0:12:38As strange as it seems, by reducing each starling to numbers,
0:12:38 > 0:12:41we can model what's happening on a computer.
0:12:45 > 0:12:48We start with a flock of virtual starlings,
0:12:48 > 0:12:52all flying at different speeds and in different directions.
0:12:52 > 0:12:56And then we give them some simple rules.
0:12:56 > 0:13:00The first is for each bird to fly at the same speed.
0:13:00 > 0:13:04The second rule is to stay close to your neighbours.
0:13:06 > 0:13:11And finally, if you see a predator nearby, get out of the way.
0:13:14 > 0:13:17Three simple rules are all it takes to create something
0:13:17 > 0:13:22that looks uncannily like the movement of a real flock of starlings.
0:13:25 > 0:13:26Oh, here they come.
0:13:26 > 0:13:28Oh!
0:13:28 > 0:13:30HE LAUGHS
0:13:31 > 0:13:34In fact, a recent study has shown
0:13:34 > 0:13:38that even in a flock of hundreds of thousands of birds,
0:13:38 > 0:13:42each starling only has to keep track of its seven nearest neighbours.
0:13:51 > 0:13:54And then...they've all gone.
0:13:55 > 0:13:57The sky's clear again.
0:14:02 > 0:14:05Who'd have thought that something so extraordinarily complex
0:14:05 > 0:14:09as a constantly shifting flock of thousands of birds in flight
0:14:09 > 0:14:13can have at its heart such a simple and elegant Code?
0:14:20 > 0:14:22WOMAN LAUGHS
0:14:22 > 0:14:23CHILD LAUGHS
0:14:27 > 0:14:28BABY CRIES
0:14:28 > 0:14:31It seems inconceivable that human beings
0:14:31 > 0:14:36could ever be reduced to a mathematical model like starlings.
0:14:36 > 0:14:38CLOCK TICKS
0:14:50 > 0:14:54But Iain Couzin studies how animals behave in groups,
0:14:54 > 0:14:59and his research has revealed some surprising parallels.
0:14:59 > 0:15:03How can you possibly begin to understand something like this huge mass of people?
0:15:03 > 0:15:06Even when you look at the crowd for a few seconds,
0:15:06 > 0:15:09you realise there's so many complicated factors at play.
0:15:09 > 0:15:12I started my research looking at simple organisms,
0:15:12 > 0:15:14organisms like ant swarms, schooling fish.
0:15:14 > 0:15:17And remarkably, our insights from studying those systems
0:15:17 > 0:15:20led to new insights in studying human crowds.
0:15:20 > 0:15:24But people are much more complicated than a...a fish or an ant.
0:15:24 > 0:15:26Exactly, but that's almost the beauty of this,
0:15:26 > 0:15:29is we're thinking about more interesting things
0:15:29 > 0:15:33when we're walking through crowds than, "How do I avoid that person and that obstacle?"
0:15:33 > 0:15:39You know, we're thinking about what we're going to cook for dinner or what our friends are doing.
0:15:39 > 0:15:41And so, in actual fact, we're almost on auto-pilot,
0:15:41 > 0:15:44and we're actually using very simple rules of interaction
0:15:44 > 0:15:47just like the schooling fish and the swarming ants.
0:15:50 > 0:15:52So can we learn things from the ants?
0:15:52 > 0:15:54We could learn an huge amount from the ants.
0:15:54 > 0:15:56Ants don't suffer from problems such as congestion.
0:15:56 > 0:16:00Because they're not selfish. And I'm afraid to say we are.
0:16:00 > 0:16:02We want to minimise our own travel time,
0:16:02 > 0:16:07but we don't necessarily care whether we do so at the expense of other individuals.
0:16:08 > 0:16:11Of all the animals Iain has studied,
0:16:11 > 0:16:15human beings are, in some ways, the most predictable.
0:16:15 > 0:16:20We walk at an optimum speed of 1.3 metres per second,
0:16:20 > 0:16:24and prefer to walk in straight lines to get to our destination.
0:16:25 > 0:16:28What happens is you will naturally fall
0:16:28 > 0:16:31into the slipstream of someone moving in the same direction as you.
0:16:31 > 0:16:35And so without you even knowing it, you're forming a lane.
0:16:35 > 0:16:40Similarly, pedestrians moving in the other direction will also form lanes,
0:16:40 > 0:16:42very much like the ants do.
0:16:42 > 0:16:45These lanes help us to avoid collisions.
0:16:45 > 0:16:47However, in a large open space,
0:16:47 > 0:16:49like the concourse at Grand Central Station,
0:16:49 > 0:16:52the lanes inevitably cross each other,
0:16:52 > 0:16:53which could lead to congestion.
0:16:55 > 0:16:59But when you put an obstacle - like this information desk -
0:16:59 > 0:17:03in the middle of the crowd, rather than getting in the way,
0:17:03 > 0:17:06it acts like a roundabout
0:17:06 > 0:17:10and increases the flow through the station by as much as 13%.
0:17:17 > 0:17:21These rules are so effective at predicting what we'll do,
0:17:21 > 0:17:25they can even be used to simulate crowds of people.
0:17:27 > 0:17:31Each individual is actually described by a set of numbers
0:17:31 > 0:17:34as they move through an environment.
0:17:34 > 0:17:37Exactly. We're capturing the average type of behaviour of pedestrians.
0:17:37 > 0:17:41We're capturing these simple and local rules that people use within crowds
0:17:41 > 0:17:44to then make predictions as to how the whole crowd
0:17:44 > 0:17:47is going to flow through different environments.
0:17:48 > 0:17:51We can use this underlying Code of the crowd
0:17:51 > 0:17:56to design buildings that are more efficient and safer.
0:17:57 > 0:18:01Simulations like these are able to accurately predict
0:18:01 > 0:18:03how quickly a building can be evacuated,
0:18:03 > 0:18:06even before it has been built.
0:18:13 > 0:18:17As a crowd, people are incredibly predictable.
0:18:17 > 0:18:21There are simple rules that we follow without being aware of it.
0:18:24 > 0:18:27But most of the time, we don't live on autopilot.
0:18:28 > 0:18:34And when the crowd disperses, so too do the rules of group behaviour.
0:18:34 > 0:18:35SIREN BLARES
0:18:35 > 0:18:41As individuals with our own free will, we're much harder to predict.
0:18:41 > 0:18:42Or so we think.
0:18:49 > 0:18:52Before we gets started, I would like to mention the rules.
0:18:52 > 0:18:53They are very simple.
0:18:53 > 0:18:56There are three throws and there are only three throws.
0:18:56 > 0:19:00We use a three-prime shoot, which means you go one, two, three
0:19:00 > 0:19:03and you release your throw on four.
0:19:04 > 0:19:06A throw of rock is a closed fist.
0:19:06 > 0:19:10You can throw it any way you want as long as it is a closed fist.
0:19:10 > 0:19:11Your paper must be horizontal.
0:19:11 > 0:19:13Your scissors must be vertical.
0:19:13 > 0:19:15That will be foul.
0:19:18 > 0:19:22The game of rock, paper, scissors is known all over the world.
0:19:24 > 0:19:27And some people take it very seriously.
0:19:27 > 0:19:30For those of you who don't know, and there should be very few,
0:19:30 > 0:19:32the throw of paper covers the throw of rock.
0:19:32 > 0:19:35The throw of scissors cuts the throw of paper,
0:19:35 > 0:19:37and the throw of rock crushes the throw of scissors.
0:19:42 > 0:19:45In Philadelphia, the Rock, Paper, Scissors League
0:19:45 > 0:19:47competes four times a week.
0:19:48 > 0:19:51The people in this room are fighting
0:19:51 > 0:19:54to go to the world championship in Las Vegas
0:19:54 > 0:19:57and the chance to win 10,000.
0:20:03 > 0:20:06Sweetji in the lead. Rock versus scissors for Sweetji.
0:20:06 > 0:20:09You're on the verge of elimination, Drew Bag.
0:20:09 > 0:20:12Third and final set, winner moves on.
0:20:12 > 0:20:13THE CROWD CHANTS AND CLAPS
0:20:13 > 0:20:15Rock versus scissors.
0:20:15 > 0:20:18And what a match, to take us down to the final four.
0:20:20 > 0:20:24The intriguing thing about this game is that it should be impossible
0:20:24 > 0:20:27to predict what your opponent's going to do next.
0:20:28 > 0:20:32In rock, paper, scissors, they're all pretty much equivalent.
0:20:32 > 0:20:36So each throw beats one and loses to another,
0:20:36 > 0:20:38so essentially it's a game of even odds.
0:20:38 > 0:20:40A bit like a flip of a coin.
0:20:42 > 0:20:44But if the game is entirely random,
0:20:44 > 0:20:47every player would be evenly matched.
0:20:47 > 0:20:51And yet some people win time and time again.
0:20:51 > 0:20:52It is match point, Sweetji.
0:20:52 > 0:20:55B-Pac has no points here in round number two.
0:20:55 > 0:20:58He will need two straight throws.
0:20:58 > 0:20:59Can he get through number one?
0:20:59 > 0:21:01No. Sweetji!
0:21:01 > 0:21:04So now our final match of the night.
0:21:04 > 0:21:08Sweetji, you're going to play dOGulas.
0:21:08 > 0:21:12The more we play, the more we're influenced by our past throws.
0:21:12 > 0:21:13Begin.
0:21:13 > 0:21:17And that creates patterns that can be exploited to win the game.
0:21:17 > 0:21:21Sweetji came fifth in the league last year,
0:21:21 > 0:21:24and this season looks set to do even better.
0:21:28 > 0:21:30dOGulas!
0:21:30 > 0:21:32Rock crushes scissors.
0:21:32 > 0:21:35Sweetji still has point...
0:21:35 > 0:21:36Rock crushes scissors!
0:21:36 > 0:21:37SHE SCREAMS
0:21:37 > 0:21:42Sweetji, Philadelphia Rock, Paper, Scissors City League Champion here at the Raven Lounge.
0:21:42 > 0:21:44- Congratulations.- Thank you.
0:21:44 > 0:21:47So that was five consecutive wins.
0:21:47 > 0:21:52- What was the key to your success, do you think?- I try to read people.
0:21:52 > 0:21:55- Yeah, you do, yeah?- Or at least try to think what they're thinking.
0:21:55 > 0:21:58- You're looking for their patterns then?- Yeah, a little bit like...
0:21:58 > 0:22:02Their patterns, and they'll be trying to learn mine and go against that.
0:22:06 > 0:22:09Rock, paper, scissors reveals a fundamental truth
0:22:09 > 0:22:11about human nature.
0:22:13 > 0:22:15We are so addicted to patterns
0:22:15 > 0:22:17that we let them seep into almost everything we do.
0:22:21 > 0:22:23And these patterns are the key
0:22:23 > 0:22:26to predicting many aspects of our behaviour.
0:22:26 > 0:22:28Even the darkest parts of our nature.
0:22:32 > 0:22:35SCREAMS
0:22:35 > 0:22:38Deceased. Female, five foot two.
0:22:38 > 0:22:42Complexion, dark. Eyes, brown. Hair, brown.
0:22:44 > 0:22:48When you see this much activity in such a small geographic area
0:22:48 > 0:22:49in such a tight time frame,
0:22:49 > 0:22:52that's a warning bell that something's going on,
0:22:52 > 0:22:53we have a predator operating.
0:22:55 > 0:23:00Kim Rossmo has 20 years' experience as a Detective Inspector.
0:23:00 > 0:23:04He specialises in hunting down serial killers.
0:23:06 > 0:23:08The victim's body was found here in the corner
0:23:08 > 0:23:12by a police officer that came in shortly after the crime had occurred.
0:23:12 > 0:23:14The prime crime scene would be...
0:23:15 > 0:23:17But Rossmo is no ordinary cop,
0:23:17 > 0:23:20because he's got a PhD
0:23:20 > 0:23:25and uses mathematics to understand the patterns criminals leave behind.
0:23:29 > 0:23:32There's a logic in how the offender hunted for the victim
0:23:32 > 0:23:35and the location where he committed the crime.
0:23:35 > 0:23:38If we can decode that and if we can understand that pattern,
0:23:38 > 0:23:41we can use that information to help us focus a criminal investigation.
0:23:44 > 0:23:51The reason it's so hard to catch serial killers is because there's often no link to their crimes.
0:23:51 > 0:23:53They kill random strangers
0:23:53 > 0:23:56in locations they have no obvious connection to.
0:23:56 > 0:23:59It's very common in the investigation of a serial murder case
0:23:59 > 0:24:03to have hundreds, thousands, even tens of thousands of suspects.
0:24:03 > 0:24:05It's a needle-in-a-haystack problem.
0:24:07 > 0:24:09Where do you start?
0:24:10 > 0:24:15In 1888, the most notorious serial killer of all, Jack the Ripper,
0:24:15 > 0:24:18killed five women in London's East End.
0:24:19 > 0:24:25Since then, countless people have tried to solve the mystery of the Ripper's identity.
0:24:25 > 0:24:28But Rossmo thinks he could have tracked him down
0:24:28 > 0:24:30without seeing a scrap of evidence.
0:24:31 > 0:24:35Because he's worked out where Jack the Ripper most likely lived.
0:24:35 > 0:24:38Based only on the location of the crimes.
0:24:38 > 0:24:44Flower and Dean Street should have been the epicentre of their search.
0:24:44 > 0:24:47And all he used to do it is an equation.
0:24:51 > 0:24:52Inherently, we're all lazy,
0:24:52 > 0:24:55and criminals just as much as anyone else.
0:24:55 > 0:24:59They want to accomplish their goals close to home rather than further away,
0:24:59 > 0:25:02because it involves too much effort, too much time, too much travel.
0:25:04 > 0:25:06The first half of Rossmo's equation
0:25:06 > 0:25:09models what's known as the least-effort principle.
0:25:09 > 0:25:12It means that the crime locations
0:25:12 > 0:25:17are statistically more likely the nearer they are to where the offender lives.
0:25:17 > 0:25:20If you have a choice of going to the corner store for a loaf of bread
0:25:20 > 0:25:23or one that's seven miles down the road, you'll pick the corner store.
0:25:23 > 0:25:27It seems a bit gruesome to apply the same thing to a serial killer
0:25:27 > 0:25:29as to going and buying a loaf of bread or milk.
0:25:29 > 0:25:34Well, actually, if we can get over the horrible nature of these crimes
0:25:34 > 0:25:38and recognise that these are human beings like the rest of us,
0:25:38 > 0:25:41we can, because we understand ourselves,
0:25:41 > 0:25:44maybe bet some understanding of these individuals.
0:25:46 > 0:25:48The second half of the equation
0:25:48 > 0:25:51describes something called the buffer zone.
0:25:51 > 0:25:54Criminals avoid committing crimes too close to home,
0:25:54 > 0:25:56for fear of drawing attention to themselves.
0:25:58 > 0:26:02It's the interaction of these two behaviours that allows Rossmo
0:26:02 > 0:26:05to calculate the most probable location of the criminal.
0:26:05 > 0:26:09These individuals have to not only obtain their target -
0:26:09 > 0:26:10or capture a victim -
0:26:10 > 0:26:15but avoid apprehension by the police and identification by witnesses.
0:26:18 > 0:26:21The technique, known as geographic profiling,
0:26:21 > 0:26:25is now used by police all over the world.
0:26:30 > 0:26:34Police are examining the possibility that a small explosion
0:26:34 > 0:26:36near a branch of Barclays Bank in West London
0:26:36 > 0:26:38was the work of an extortionist.
0:26:38 > 0:26:40Police believe the demand
0:26:40 > 0:26:42came from the blackmailer known as Mardi Gra.
0:26:44 > 0:26:48In the late '90s, Rossmo was called in by Scotland Yard
0:26:48 > 0:26:52to help catch the notorious Mardi Gra bomber,
0:26:52 > 0:26:55who for three years waged a campaign of terror
0:26:55 > 0:26:58against banks and supermarkets.
0:26:58 > 0:27:01A 17-year-old man is recovering in hospital after being injured
0:27:01 > 0:27:04in an explosion at a Sainsbury's store in South London.
0:27:04 > 0:27:06'Police are advising the public to be vigilant.
0:27:06 > 0:27:10'In truth, they can only wait to see what Mardi Gra does next.'
0:27:16 > 0:27:19How many bombs did he let off during that time?
0:27:19 > 0:27:23Total, 36 known linked offences.
0:27:23 > 0:27:26So you can see, they range from the north of Cambridge,
0:27:26 > 0:27:29all the way down to the strait of Dover.
0:27:29 > 0:27:31But most of them are in Greater London.
0:27:31 > 0:27:35So this is a map showing the locations of all the bombs that were set off?
0:27:35 > 0:27:39- That's right.- There's certainly a concentration on London,
0:27:39 > 0:27:42but it looks pretty randomly scattered.
0:27:42 > 0:27:45So now you're feeding those locations into the equation?
0:27:45 > 0:27:49Right. And what we have here now is the geo-profile.
0:27:49 > 0:27:52And that's going to show us the most likely location
0:27:52 > 0:27:54where the offender lived.
0:27:54 > 0:27:58With dark orange being the most likely or the most probable.
0:27:58 > 0:28:02So we can see that the major focus is around the Chiswick area.
0:28:02 > 0:28:05In fact, in the report we prepared for Scotland Yard,
0:28:05 > 0:28:07we even prioritised postcodes for that.
0:28:07 > 0:28:09And how successful was it in this case?
0:28:09 > 0:28:12Well, let me show you the locations...
0:28:12 > 0:28:15of the two brothers, Edgar and Ronald Pearce.
0:28:15 > 0:28:19- Right, that is really in the hot zone, isn't it?- Yes.
0:28:19 > 0:28:21Edgar's home is in the top 0.8%
0:28:21 > 0:28:24of the area of the crimes in Greater London.
0:28:24 > 0:28:27- So less than 1%. - That's extraordinary.
0:28:31 > 0:28:35Edgar Pearce had demanded £10,000 a day from Barclays.
0:28:35 > 0:28:39And when he and his brother tried to collect it from a cash point in Chiswick,
0:28:39 > 0:28:41the police were waiting.
0:28:41 > 0:28:44Two bothers in their 60s were remanded in custody by magistrates
0:28:44 > 0:28:47in connection with the so-called Mardi Gra bombings.
0:28:47 > 0:28:51Ronald and Edgar Pearce, both from Chiswick in West London,
0:28:51 > 0:28:54each face three conspiracy charges.
0:28:54 > 0:28:58Based on the apparently random location of 36 bombs,
0:28:58 > 0:29:04Rossmo's geographic profile narrowed the location of the Mardi Gra bomber
0:29:04 > 0:29:07from 300 square miles to a postcode in Chiswick.
0:29:10 > 0:29:13Although his bother, Ronald, was acquitted,
0:29:13 > 0:29:18Edgar Pearce pleaded guilty and was jailed for 21 years.
0:29:18 > 0:29:22So do you think the bomber was aware that he was creating these patterns?
0:29:22 > 0:29:25No, he wasn't. But it's very difficult for humans
0:29:25 > 0:29:28to engage in completely random behaviour.
0:29:34 > 0:29:38Very few of us are aware of the patterns we leave behind.
0:29:38 > 0:29:40WOMAN LAUGHS
0:29:40 > 0:29:43From the way we move in a crowd...
0:29:46 > 0:29:48..to the choices we make in a game...
0:29:48 > 0:29:50Paper covers rock!
0:29:50 > 0:29:53The victim's body was found here...
0:29:53 > 0:29:55..or even how we commit murder.
0:29:55 > 0:29:59In reality, these crimes are not random...
0:29:59 > 0:30:01None of it is random.
0:30:01 > 0:30:05It's all part of the Code.
0:30:05 > 0:30:08There are always tell-tale patterns.
0:30:08 > 0:30:11And if we're able to decode them,
0:30:11 > 0:30:15we can use those patterns to model our behaviour.
0:30:15 > 0:30:19And this leads to the intriguing possibility
0:30:19 > 0:30:25that if we can reduce human beings to numbers, we might be able to predict our future
0:30:25 > 0:30:30in the same way as we can predict the movement of the planets or the trajectory of a ball.
0:30:39 > 0:30:44But the course of our lives never seems to run entirely smoothly,
0:30:44 > 0:30:49and the future rarely turns out exactly as we'd planned.
0:30:49 > 0:30:53I may have a good idea what I'm going to be doing tomorrow, or even next week,
0:30:53 > 0:30:58but as the weeks turn into months and months to years, our future becomes less certain.
0:31:02 > 0:31:06Every decision we make, every situation we encounter,
0:31:06 > 0:31:11every person we meet, sends our life down a different path.
0:31:12 > 0:31:17As you watch each stick floating off downstream, there's no sure way of predicting their fate.
0:31:17 > 0:31:22I might be able to hazard a guess where a stick will be in two minutes.
0:31:22 > 0:31:25But what about two hours? Two days?
0:31:25 > 0:31:29'..Turn into years, our future becomes far less certain.'
0:31:29 > 0:31:35Life sometimes seems so unpredictable that we think of it as being random.
0:31:35 > 0:31:38But in fact it isn't random at all.
0:31:38 > 0:31:40Simply a sequence of cause and effect.
0:31:40 > 0:31:44A freak accident.
0:31:44 > 0:31:45I'm so sorry.
0:31:45 > 0:31:47A slight delay.
0:31:47 > 0:31:49A missed bus.
0:31:49 > 0:31:51A broken promise.
0:31:51 > 0:31:58There are millions of factors that intervene to affect our journey through life,
0:31:58 > 0:32:04and the tiniest shift in any one of these can completely change its future course.
0:32:05 > 0:32:07The white one's caught in a dam, but the red one's fast.
0:32:19 > 0:32:23I think this'd be a good finishing line.
0:32:23 > 0:32:27And here comes the white. It's way ahead of the red.
0:32:28 > 0:32:32And white's the winner.
0:32:32 > 0:32:33Right, let's give it another go.
0:32:33 > 0:32:40The truth is, our lives are controlled by the strangest code of all...
0:32:40 > 0:32:42the code of chaos.
0:32:46 > 0:32:49Our lives aren't random, they're chaotic,
0:32:49 > 0:32:54a tangled web of cause and effect in which insignificant moments
0:32:54 > 0:32:59can escalate into events that change our lives forever.
0:32:59 > 0:33:05Any difference, no matter how small, can have a huge effect on the outcome.
0:33:05 > 0:33:09It's this incredible sensitivity to even the slightest change
0:33:09 > 0:33:12which is one of the defining features of chaos.
0:33:18 > 0:33:23Because chaotic systems appear so random, it's often difficult to see a pattern.
0:33:25 > 0:33:31And that has led us to sometimes misinterpret our world in a spectacular manner.
0:33:37 > 0:33:40'In this land of many mysteries, it's a strange fact
0:33:40 > 0:33:44'that large legends seem to collect around the smallest creatures.
0:33:44 > 0:33:49'One of these is a mousy little rodent called the lemming.
0:33:49 > 0:33:53'Here's an actual living legend, for it's said of this tiny animal
0:33:53 > 0:33:57''that it commits mass suicide by rushing into the sea in droves.
0:33:57 > 0:34:00This film from 1958
0:34:00 > 0:34:03set out to explain the wildly fluctuating population
0:34:03 > 0:34:05of these tiny rodents.
0:34:11 > 0:34:19'Ahead lies the Arctic shore, and beyond, the sea. And still the little animals surge forward.
0:34:21 > 0:34:25'Their frenzy takes them tumbling down the terraced cliffs,
0:34:25 > 0:34:29'creating tiny avalanches of sliding soil and rocks.'
0:34:33 > 0:34:35The legend of suicidal lemmings
0:34:35 > 0:34:39was the accepted explanation for why the Arctic can be overrun with them one year
0:34:39 > 0:34:41and completely empty the next.
0:34:41 > 0:34:45'They reach the final precipice.
0:34:45 > 0:34:48'This is the last chance to turn back.
0:34:52 > 0:34:56'Yet over they go, casting themselves bodily out into space.'
0:35:01 > 0:35:07This film popularised the belief that lemmings are stupid, reckless and suicidal.
0:35:07 > 0:35:10The very word "lemming" has come to mean as much.
0:35:15 > 0:35:18The trouble is, though, it isn't true.
0:35:18 > 0:35:22In fact, it's been claimed that the whole thing was faked.
0:35:26 > 0:35:30The film-makers apparently flew in hundreds of captive-bred lemmings
0:35:30 > 0:35:33and drove them over the cliffs and out to sea.
0:35:37 > 0:35:40'Soon the Arctic Sea is dotted with tiny bobbing bodies.
0:35:43 > 0:35:48'And so is acted out the legend of mass suicide.'
0:35:48 > 0:35:53Now, as appalling as this sounds, the reason for the alleged lemming abuse stems not so much
0:35:53 > 0:35:55from ignoring the moral code,
0:35:55 > 0:35:58but rather an ignorance of the mathematical one.
0:35:58 > 0:36:04What no-one knew at the time was that the incredible fluctuation
0:36:04 > 0:36:08in lemming numbers has nothing to do with mass suicide.
0:36:09 > 0:36:12It's all because of chaos.
0:36:12 > 0:36:15And there's a simple equation at its heart.
0:36:17 > 0:36:22So, if I want to know how many lemmings there'll be next year,
0:36:22 > 0:36:26what I need to do is take this year's population, "P",
0:36:26 > 0:36:29and multiply that by the growth rate "R".
0:36:29 > 0:36:31But not all lemmings will survive,
0:36:31 > 0:36:36so there's a bit of the equation which tells me how many lemmings will die during the year.
0:36:36 > 0:36:39So that's R times P times P.
0:36:39 > 0:36:42So we can rewrite this equation
0:36:42 > 0:36:47as the growth rate R times P times one minus P.
0:36:47 > 0:36:50Now, this equation isn't specific to lemmings,
0:36:50 > 0:36:52it actually applies to any animal population.
0:36:52 > 0:36:57And the interesting part of the equation is this number R, the growth rate.
0:36:57 > 0:37:00Because when we choose different values for R,
0:37:00 > 0:37:03we get a very different behaviour for the population growth.
0:37:04 > 0:37:08The growth rate determines how quickly a population expands.
0:37:08 > 0:37:12For most species of mammal, this is usually below 2.
0:37:12 > 0:37:16With a growth rate in this range, the equation predicts
0:37:16 > 0:37:20that a population will rise until it stabilises at a fixed value.
0:37:20 > 0:37:26But it turns out lemmings are one of the fastest-reproducing mammals on the planet.
0:37:26 > 0:37:30Let's take R equals 3.1.
0:37:30 > 0:37:35The lemmings don't stabilise now, but ping-pong between two different values.
0:37:35 > 0:37:40So the population is high, then low, and back to high again, low again.
0:37:40 > 0:37:44But when the growth rate reaches a value just over 3.57
0:37:44 > 0:37:49then something incredibly unexpected happens.
0:37:49 > 0:37:52Rather than levelling off at a fixed number,
0:37:52 > 0:37:58or fluctuating between two values, their population erupts into chaos.
0:37:58 > 0:38:04A plague of almost biblical proportions one year can plummet to near extinction the next.
0:38:04 > 0:38:09It's almost impossible to predict how many lemmings you're going to have.
0:38:09 > 0:38:12In fact, there doesn't seem to be any pattern to this at all.
0:38:12 > 0:38:16And of course, this is exactly what's seen in reality.
0:38:16 > 0:38:20Unpredictable boom-and-bust lemming populations.
0:38:21 > 0:38:25Lemmings are one of the few creatures on Earth that breed so quickly
0:38:25 > 0:38:29their growth rate can sometimes exceed this tipping point.
0:38:32 > 0:38:37It's such an odd phenomenon that mass suicide seems like a plausible answer.
0:38:37 > 0:38:41But the real explanation comes from the Code.
0:38:41 > 0:38:42From this equation.
0:38:48 > 0:38:53The problem is we can never know exactly how many lemmings are born or how many die.
0:38:53 > 0:39:00And just the smallest difference in the growth rate R, produces a totally different answer.
0:39:00 > 0:39:04And this is true of all equations that model chaos.
0:39:04 > 0:39:08Although they can explain how something happens,
0:39:08 > 0:39:11they're almost useless at predicting the future.
0:39:19 > 0:39:22I can use an equation to calculate where this ball will land,
0:39:22 > 0:39:25because even if I'm slightly out in any of my measurements,
0:39:25 > 0:39:28it will only make a small difference to the final result.
0:39:28 > 0:39:34The ball will be released from the ramp at 49.1 degrees.
0:39:34 > 0:39:37But if this ball behaved according to the laws of chaos,
0:39:37 > 0:39:40the tiniest shift in the ball's position
0:39:40 > 0:39:45or the angle of release could dramatically alter its trajectory.
0:39:47 > 0:39:50I'd have no idea whether it would just simply fall harmlessly
0:39:50 > 0:39:52off the end of the ramp.
0:39:55 > 0:39:58Or be sent into orbit.
0:40:02 > 0:40:06I'd have no idea where to put my deckchair.
0:40:08 > 0:40:12It turns out that much of the world is chaotic,
0:40:12 > 0:40:15making it almost impossible to predict.
0:40:17 > 0:40:20But that doesn't stop us trying.
0:40:24 > 0:40:27Knowing whether the sun is going to shine
0:40:27 > 0:40:30or the heavens are going to open, is a British obsession.
0:40:30 > 0:40:34But trying to plan our lives around the vagaries of the weather
0:40:34 > 0:40:36seems almost futile.
0:40:42 > 0:40:48Even though we have precise equations that can describe how clashing air masses interact
0:40:48 > 0:40:50to create clouds, wind and rainfall,
0:40:50 > 0:40:56it doesn't really help us very much with our predictions.
0:40:56 > 0:40:59THUNDER CRASHES
0:40:59 > 0:41:05That's because we can never know the exact speed of every air particle.
0:41:05 > 0:41:08The precise temperature at every point in space,
0:41:08 > 0:41:11or the pressure across the whole planet.
0:41:11 > 0:41:14And just a small variation in any one of these
0:41:14 > 0:41:16can produce a vastly different forecast.
0:41:22 > 0:41:26This is a map of how the weather looks right now.
0:41:26 > 0:41:31The blue lines represent cold fronts and the red lines represent warm fronts.
0:41:31 > 0:41:33In order to make a prediction
0:41:33 > 0:41:36what we do is to take the mathematical equations for the weather
0:41:36 > 0:41:38and create a model.
0:41:38 > 0:41:43Now the trouble is, I can't know the precise atmospheric conditions,
0:41:43 > 0:41:45so I take as much data as possible.
0:41:45 > 0:41:50Then I make small variations in the data and run the model again
0:41:50 > 0:41:56and again and again and what I get is different predictions according to those slight variations.
0:41:56 > 0:42:00So for the weather tomorrow, the predictions are petty similar.
0:42:00 > 0:42:05We've got a lot of blue lines together predicting a cold front.
0:42:05 > 0:42:08A lot of red lines together predicting a warm front.
0:42:08 > 0:42:11But look what happens when I look a little bit further ahead.
0:42:11 > 0:42:15So two days, three days ahead...
0:42:15 > 0:42:20so you can see these different predictions are beginning to spread out.
0:42:20 > 0:42:23You can still see some sort of pattern in the weather
0:42:23 > 0:42:26but if I move a week ahead...
0:42:27 > 0:42:31..and I couldn't hazard a guess as to what the weather's going to be.
0:42:31 > 0:42:33There are red and blue lines all over the place.
0:42:33 > 0:42:37One prediction says it's going to be hot, the other says cold,
0:42:37 > 0:42:40and if I go ten days ahead,
0:42:40 > 0:42:44it just looks like a scrambled mess of spaghetti.
0:42:44 > 0:42:49There's absolutely no way to make any prediction that far in advance.
0:42:49 > 0:42:52And that's why beyond just a few days,
0:42:52 > 0:42:55the weather forecast can be so spectacularly wrong.
0:43:00 > 0:43:03Once we understand that the atmosphere is chaotic,
0:43:03 > 0:43:08we can appreciate that the smallest change in the initial conditions
0:43:08 > 0:43:11can dramatically alter what will happen.
0:43:14 > 0:43:18The movement of just one molecule of air can be magnified over time
0:43:18 > 0:43:21to have a huge effect on the weather as a whole.
0:43:24 > 0:43:28We refer to this phenomenon as the "butterfly effect".
0:43:28 > 0:43:33The idea that something as small as the flap of a butterfly's wings
0:43:33 > 0:43:36might create changes in the atmosphere
0:43:36 > 0:43:40that could ultimately lead to a tornado on the other side of the world.
0:43:40 > 0:43:43CRASHING THUNDER
0:43:58 > 0:44:03As a crowd, the patterns we make are incredibly predictable.
0:44:04 > 0:44:09Even as individuals our actions are controlled by the Code.
0:44:13 > 0:44:16And by untangling chaotic systems like the weather,
0:44:16 > 0:44:21we've uncovered evidence of the Code in what we once thought of
0:44:21 > 0:44:24as impossibly complex.
0:44:26 > 0:44:28When we look at things from a different angle,
0:44:28 > 0:44:31surprising patterns emerge.
0:44:33 > 0:44:39Patterns that can reveal defining truths about ourselves and our future.
0:44:47 > 0:44:51In 1906, an unfortunate cow laid down its life
0:44:51 > 0:44:54for a place in mathematical history.
0:44:54 > 0:44:55One.
0:44:55 > 0:44:57Ten.
0:44:57 > 0:44:59264.
0:44:59 > 0:45:01417.
0:45:01 > 0:45:06'The cow was the subject of a guess-the-weight competition at a village fare.
0:45:06 > 0:45:09'The lucky person who came closest
0:45:09 > 0:45:12'would win the slaughtered animal's meat.'
0:45:13 > 0:45:141,020.
0:45:16 > 0:45:172,137.
0:45:17 > 0:45:20'The amazing thing was nobody guessed correctly.'
0:45:20 > 0:45:21..570.
0:45:21 > 0:45:24'And yet everybody got it right.'
0:45:26 > 0:45:284,510.
0:45:30 > 0:45:32To show you how they did it,
0:45:32 > 0:45:36I'm not going to use a cow, I'm going to use a jar of jelly beans.
0:45:40 > 0:45:41450?
0:45:41 > 0:45:43800?
0:45:43 > 0:45:4412,000.
0:45:44 > 0:45:457,000.
0:45:45 > 0:45:48How many jellybeans do you think there are in this jar?
0:45:48 > 0:45:50Um,
0:45:50 > 0:45:5250...
0:45:52 > 0:45:5480 thousand.
0:45:54 > 0:45:5580 thousand?
0:45:55 > 0:45:58No, actually 50,000.
0:45:58 > 0:46:0050,000. OK, yeah.
0:46:04 > 0:46:09It's incredibly difficult for anyone to guess how many jellybeans there are.
0:46:10 > 0:46:15I asked 160 people and most were way off the mark.
0:46:15 > 0:46:20Everything from 400 right up to 50,000 beans.
0:46:20 > 0:46:27In fact only four people got anywhere near the correct answer of 4,510.
0:46:27 > 0:46:36Plus 1,500, plus 3,217, plus 83... .
0:46:36 > 0:46:43If I add all the answers together and take the average, I get the combined guess of the entire group.
0:46:43 > 0:46:46Plus, 4,000, plus 5,000,
0:46:46 > 0:46:48463,
0:46:48 > 0:46:52Plus 853, plus 1,000,
0:46:52 > 0:46:55plus 5,000...
0:46:55 > 0:46:58Which gives a grand total
0:46:58 > 0:47:04of 722,383.5.
0:47:04 > 0:47:06Somebody thought there was half a bean in there.
0:47:06 > 0:47:13Now there are 160 guesses made, so let's see how close they are collectively.
0:47:13 > 0:47:16Wow, that's extraordinary.
0:47:16 > 0:47:20You remember there were 4,510.
0:47:20 > 0:47:26The average guess to the nearest bean is 4,515.
0:47:26 > 0:47:30I thought it would be close, but I didn't think it would be THAT close.
0:47:30 > 0:47:31That is ridiculous.
0:47:31 > 0:47:36Though we had guesses that were all over the place, up in 30,000s right down in the 400s,
0:47:36 > 0:47:40collectively we get something which is just 0.1% away
0:47:40 > 0:47:43from the real number of beans in there.
0:47:43 > 0:47:47So as individuals the guesses are just that, guesses.
0:47:47 > 0:47:52But when you take them collectively they become something else entirely.
0:47:52 > 0:47:57- 5,000.- 1,450.- 9,200.
0:47:57 > 0:48:01What tends to happen is that more or less as many people
0:48:01 > 0:48:05will underestimate the number of jellybeans as overestimate it.
0:48:05 > 0:48:08- 1,763...- 6,000.
0:48:08 > 0:48:13A few people will be way off the mark either way, but that doesn't matter.
0:48:13 > 0:48:19Provided you ask enough people, the errors should cancel each other out.
0:48:19 > 0:48:24- 1,000.- 1,275.- 700?
0:48:24 > 0:48:28The accuracy of the group is far greater than the individual.
0:48:28 > 0:48:31We call it "the wisdom of the crowd".
0:48:31 > 0:48:40160 people is a powerful tool for working out how many jellybeans there are in the jar.
0:48:40 > 0:48:44But imagine what you could do with a crowd of millions.
0:48:47 > 0:48:50That's exactly what they use here at Google.
0:48:52 > 0:48:55With access to over two billion web searches a day,
0:48:55 > 0:49:00Google have found a way of tapping into the wisdom of the biggest crowd on Earth.
0:49:00 > 0:49:02And by doing so,
0:49:02 > 0:49:07they've been able to reveal the forces that control our lives,
0:49:07 > 0:49:11and harness them to make predictions about us.
0:49:11 > 0:49:14'Think of the things that people search for on a daily basis.
0:49:14 > 0:49:17'Think of the things that YOU search for on a daily basis.'
0:49:17 > 0:49:23I searched for cities in Mexico and films in Hackney today.
0:49:23 > 0:49:26Lots of people may be searching for the similar...
0:49:26 > 0:49:29a similar thing, movies in Hackney, for example.
0:49:29 > 0:49:33And if you look at that query over the past three years,
0:49:33 > 0:49:36um, what the pattern of searches for that term looks like.
0:49:40 > 0:49:43Google had a hunch they could use all our searches
0:49:43 > 0:49:46to make predictions about our lives.
0:49:47 > 0:49:51They wanted to see if they could match the pattern of certain searches
0:49:51 > 0:49:53with events in the real world.
0:49:55 > 0:49:59Google began by seeing if they could predict outbreaks of flu.
0:50:01 > 0:50:04So flu has a nice seasonal pattern
0:50:04 > 0:50:09and because it has that pattern every year over many years,
0:50:09 > 0:50:12we're able to...to take that trend
0:50:12 > 0:50:16and say which search queries match that pattern.
0:50:16 > 0:50:21So we built a database that included over 50 million different search terms.
0:50:21 > 0:50:2250 million?
0:50:22 > 0:50:24- Yes.- Wow, yeah.
0:50:24 > 0:50:27We didn't only include things that may be related to flu.
0:50:27 > 0:50:29We included things like Britney Spears or...
0:50:29 > 0:50:32Everything people search for would be included.
0:50:35 > 0:50:38When Google looked back over the past five years of data,
0:50:38 > 0:50:43there were certain search terms whose popularity exactly matched
0:50:43 > 0:50:45the pattern of flu cases.
0:50:45 > 0:50:49So people were searching for things like "symptoms"
0:50:49 > 0:50:52or "medications" or "sore throat".
0:50:52 > 0:50:56There are other things like complications.
0:50:56 > 0:51:01So you're saying that the sort of number of search terms for flu-related things
0:51:01 > 0:51:07- almost exactly mirrors the actual cases of flu that we see in the population?- That's true.
0:51:07 > 0:51:12It is an indicator of flu activity just based on lots of people searching for these terms.
0:51:12 > 0:51:14We were amazed by this finding.
0:51:19 > 0:51:22As soon as they see this pattern of search terms
0:51:22 > 0:51:26Google can predict there will be an outbreak of flu.
0:51:26 > 0:51:29Often before people had even gone to the doctor.
0:51:31 > 0:51:36This is the extraordinary power of the Code.
0:51:38 > 0:51:42But it's just the tip of the iceberg.
0:51:42 > 0:51:44The searches we make can be used to predict
0:51:44 > 0:51:46where we'll go on holiday.
0:51:46 > 0:51:48What model of car we're going to buy.
0:51:48 > 0:51:50Or how we're going to vote,
0:51:50 > 0:51:53often before we know ourselves.
0:51:55 > 0:51:59It's even been possible to forecast the movement of the Stock Market
0:51:59 > 0:52:02from the number of negative words used on Twitter.
0:52:06 > 0:52:12Analysing such vast amounts of data, doesn't just allow us to make predictions.
0:52:12 > 0:52:17It can also tell us something fundamental about ourselves.
0:52:20 > 0:52:26You look out at a city like this and it looks like, you know, some arbitrary jumbled mess.
0:52:26 > 0:52:30Yet the city IS people.
0:52:30 > 0:52:32It's not the buildings and the streets.
0:52:32 > 0:52:36They're the stage upon which the real actors
0:52:36 > 0:52:39are playing out the story of civilisation.
0:52:42 > 0:52:48Geoffrey West is a physicist who's spent his life trying to see meaningful patterns in the universe.
0:52:48 > 0:52:55And how he's turned his attention to the dynamics of human life in cities.
0:52:59 > 0:53:03So you can see there's all kinds of infrastructure here.
0:53:03 > 0:53:09There's the obvious, the roads, the electrical lines, the sewer lines.
0:53:09 > 0:53:13They're an extraordinary network that is sustaining New York City.
0:53:13 > 0:53:16You know, coming at it as a physicist,
0:53:16 > 0:53:21I had this hunch that there is an underlying code to all this.
0:53:24 > 0:53:29West amassed data about cities all over the world.
0:53:29 > 0:53:33And the patterns he found mean that for any given population size,
0:53:33 > 0:53:35he can predict the amount of roads,
0:53:35 > 0:53:40electrical wiring or office space that city has.
0:53:43 > 0:53:46But he also discovered something much more surprising.
0:53:50 > 0:53:56One of the most interesting results we discovered was that, um...
0:53:56 > 0:54:00Wages scale in a very systematic way
0:54:00 > 0:54:04and the rule that came out was that if you doubled the size of the city,
0:54:04 > 0:54:08you get this marvellous 15% increase in the wages.
0:54:08 > 0:54:13- If you live in a large city, you're going to earn more?- Yes.
0:54:13 > 0:54:15So what, if there are two mathematicians
0:54:15 > 0:54:19in two different cities - one twice the size - doing the same job,
0:54:19 > 0:54:24- one will have a bigger income? - On the average, that is what the data say.
0:54:24 > 0:54:26- Was that a surprise to you to see that?- A huge surprise.
0:54:26 > 0:54:29I thought there was something wrong with the data.
0:54:29 > 0:54:35And then it was like, "Of course! That's why cities exist."
0:54:40 > 0:54:43Incredibly, it's not just people's salaries that increase.
0:54:43 > 0:54:49When a city doubles in size, every measure of social and economic activity
0:54:49 > 0:54:52goes up by 15% per person.
0:54:52 > 0:54:56That's 15% more restaurants to choose from.
0:54:56 > 0:55:0015% more art galleries to visit. 15% more shops to go to.
0:55:01 > 0:55:05In short, life gets 15% better.
0:55:11 > 0:55:13You know it looks like it's a magic formula
0:55:13 > 0:55:17that we as social human beings have discovered...
0:55:20 > 0:55:25..this 15% bonus, so to speak, is, I believe, the reason
0:55:25 > 0:55:28that people are attracted to cities
0:55:28 > 0:55:32and why there has been this continuous migration
0:55:32 > 0:55:35from the countryside and into the cities.
0:55:35 > 0:55:40And at some deeper level, actually drive our civilisation.
0:55:43 > 0:55:48According to Geoffrey West, humankind has an ultimate number.
0:55:48 > 0:55:51It's this extra 15%
0:55:51 > 0:55:53or 1.15.
0:55:53 > 0:55:58He believes it's the most important driving force in humanity.
0:56:00 > 0:56:03This single number, 1.15,
0:56:03 > 0:56:05predicts our future.
0:56:06 > 0:56:10It will bring us together in ever expanding cities
0:56:10 > 0:56:14and shape our destiny for as long as human beings exist.
0:56:25 > 0:56:29Five hundred years ago, when faced with an eclipse,
0:56:29 > 0:56:33many of us would have believed it was the work of an angry god.
0:56:33 > 0:56:35But as we've unearthed the language of the Code,
0:56:35 > 0:56:39we've discovered that the apparent mysteries of our world
0:56:39 > 0:56:43can be understood without invoking the supernatural.
0:56:43 > 0:56:46And this for me is what's so remarkable.
0:56:46 > 0:56:50That despite the incredible complexity of the world we live in,
0:56:50 > 0:56:54it can all, ultimately, be explained by numbers.
0:56:57 > 0:57:02Just like the orbit of the planets, life too follows a pattern.
0:57:04 > 0:57:07And it can all be reduced to cause and effect.
0:57:11 > 0:57:13In the end, even the flip of a coin
0:57:13 > 0:57:16is determined by how fast it's spinning
0:57:16 > 0:57:18and how long it takes to hit the ground.
0:57:18 > 0:57:23The ultimate symbol of chance isn't random at all.
0:57:23 > 0:57:26It only appears that way.
0:57:28 > 0:57:31When we don't understand the Code,
0:57:31 > 0:57:36the only way we can make sense of our world is to make up stories.
0:57:37 > 0:57:40But the truth is far more extraordinary.
0:57:42 > 0:57:46Everything has mathematics at its heart.
0:57:47 > 0:57:52When everything is stripped away all that remains is the Code.
0:57:57 > 0:58:05Find clues to help you solve the Code's treasure hunt at...
0:58:05 > 0:58:10Plus get a free set of mathematical puzzles and a treasure hunt clue
0:58:10 > 0:58:14when you follow the links to the Open University.
0:58:14 > 0:58:19Or call 0845 366 8026.
0:58:32 > 0:58:35Subtitles by Red Bee Media Ltd
0:58:35 > 0:58:38E-mail subtitling@bbc.co.uk