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These are the user uploaded subtitles that are being translated: 1 00:00:04,680 --> 00:00:07,400 For as long as human beings have walked upon earth, 2 00:00:07,400 --> 00:00:09,400 we've tried to make sense of our world 3 00:00:09,400 --> 00:00:12,280 and predict what the future will bring. 4 00:00:18,920 --> 00:00:20,080 Yet today, 5 00:00:20,080 --> 00:00:23,920 our lives seem more complicated and unpredictable than ever. 6 00:00:25,600 --> 00:00:27,400 And half the population of the planet 7 00:00:27,400 --> 00:00:30,520 now live in busy, sprawling cities. 8 00:00:31,920 --> 00:00:35,440 Every day throws up thousands of different encounters. 9 00:00:35,440 --> 00:00:39,640 A mass of interactions and forces that seem beyond our control. 10 00:00:39,640 --> 00:00:41,400 WOMAN LAUGHS 11 00:00:41,400 --> 00:00:45,520 It's hard to see how any of this could be connected. 12 00:00:45,520 --> 00:00:46,840 BABY CRIES 13 00:00:47,880 --> 00:00:51,880 Yet when we start to look closely at all this complexity, 14 00:00:51,880 --> 00:00:54,920 surprising patterns begin to emerge. 15 00:00:59,840 --> 00:01:03,800 It's these patterns that I believe point to an underlying Code 16 00:01:03,800 --> 00:01:06,080 at the very heart of existence 17 00:01:06,080 --> 00:01:10,480 that controls not only our world and everything in it, but even us. 18 00:01:46,640 --> 00:01:51,920 As a mathematician, I'm fascinated by the patterns we see all around us. 19 00:01:55,680 --> 00:01:59,480 Patterns that reflect the hidden connections between everything. 20 00:02:01,240 --> 00:02:04,720 From the movement of rush hour crowds... 21 00:02:07,320 --> 00:02:10,600 ..to the shifting shape of a flock of starlings. 22 00:02:13,400 --> 00:02:17,040 The cacophony of a billion Internet searches... 23 00:02:17,040 --> 00:02:19,760 and the vagaries of the weather. 24 00:02:19,760 --> 00:02:21,800 THUNDER ROLLS 25 00:02:26,920 --> 00:02:28,880 CHEERING 26 00:02:31,080 --> 00:02:34,640 Together, these patterns and connections make up the Code. 27 00:02:36,920 --> 00:02:41,400 A model of our world that describes not only how it works, 28 00:02:41,400 --> 00:02:44,760 but can also predict what our future holds. 29 00:03:00,520 --> 00:03:05,040 Around 500 years ago, a ship was caught in a terrible storm. 30 00:03:05,040 --> 00:03:06,600 As rain lashed the decks 31 00:03:06,600 --> 00:03:09,400 and gale force winds tore through the rigging, 32 00:03:09,400 --> 00:03:11,280 the ship began to take on water. 33 00:03:11,280 --> 00:03:16,480 The captain had no choice but to run his ship aground and wait for help. 34 00:03:19,440 --> 00:03:23,480 But help never arrived, and the natives were hostile. 35 00:03:26,000 --> 00:03:30,120 After eight long months, and with his crew facing certain starvation, 36 00:03:30,120 --> 00:03:33,520 the captain came up with an ingenious plan. 37 00:03:33,520 --> 00:03:37,240 He summoned the local chief and told him his God was angry. 38 00:03:38,240 --> 00:03:42,480 So angry, in fact, that if they didn't bring supplies within three days, 39 00:03:42,480 --> 00:03:44,760 God would swallow the moon. 40 00:03:50,600 --> 00:03:56,400 And sure enough, as the moon rose on the third night, it had already begun to disappear. 41 00:04:04,360 --> 00:04:08,360 Terrified, the locals ran from all directions towards the ship, 42 00:04:08,360 --> 00:04:09,960 laden with provisions. 43 00:04:16,680 --> 00:04:19,920 The year was 1504, and the captain? 44 00:04:19,920 --> 00:04:21,880 Christopher Columbus. 45 00:04:21,880 --> 00:04:25,360 And the reason he was apparently able to command the heavens 46 00:04:25,360 --> 00:04:28,560 was because he had something like this. 47 00:04:31,200 --> 00:04:32,680 It's a set of lunar tables. 48 00:04:32,680 --> 00:04:37,120 And each one of these numbers represents a lunar eclipse. 49 00:04:37,120 --> 00:04:43,000 Today's date is June 15th, and it says that in about five hours' time 50 00:04:43,000 --> 00:04:47,240 the same thing is going to happen to the moon here in Cyprus. 51 00:04:53,040 --> 00:04:57,600 During a lunar eclipse, the earth passes between the sun and the moon, 52 00:04:57,600 --> 00:05:00,760 casting its shadow across the lunar surface. 53 00:05:09,000 --> 00:05:10,760 And there it goes. 54 00:05:10,760 --> 00:05:15,120 The moon has been swallowed up by the shadow of the Earth. 55 00:05:15,120 --> 00:05:19,840 But the amazing thing is actually the moon doesn't completely disappear, cos... 56 00:05:19,840 --> 00:05:24,960 there's a kind of... red, ghostly moon up there. 57 00:05:24,960 --> 00:05:30,200 And that's because the light from the sun is being refracted around the Earth. 58 00:05:31,840 --> 00:05:33,440 Really quite spooky. 59 00:05:38,440 --> 00:05:44,680 I can imagine how terrified the islanders would have been when they saw that 500 years ago. 60 00:05:44,680 --> 00:05:49,600 And the only explanation for them would have been that the gods really were angry with them. 61 00:05:57,800 --> 00:06:03,160 We now know that the movement of the planets is incredibly predictable. 62 00:06:03,160 --> 00:06:08,720 By understanding the Code, we can model their orbits far back into the past. 63 00:06:08,720 --> 00:06:12,160 And see thousands of years into the future. 64 00:06:21,080 --> 00:06:25,240 It's thanks to the Code that we're no longer frightened by an eclipse. 65 00:06:25,240 --> 00:06:28,640 In fact, the Code is such a powerful thing 66 00:06:28,640 --> 00:06:32,280 that I'm even prepared to entrust my life to it. 67 00:06:54,120 --> 00:06:58,840 This strange contraption is five and a half metres high. 68 00:06:59,840 --> 00:07:02,120 Using the force of gravity, 69 00:07:02,120 --> 00:07:07,800 a 30-kilogram ball will hurtle down the ramp and fire off the end. 70 00:07:07,800 --> 00:07:13,000 And when it does, I will be sitting directly in its path. 71 00:07:13,000 --> 00:07:16,960 If I get my sums wrong, I'll be killed outright. 72 00:07:23,520 --> 00:07:26,400 To calculate how far the ball's going to go, 73 00:07:26,400 --> 00:07:29,400 I need some key measurements about the ramp. 74 00:07:31,240 --> 00:07:35,720 Little h is 0.98 metres. 75 00:07:35,720 --> 00:07:41,360 The angle is 49.1 degree. 76 00:07:41,360 --> 00:07:43,640 So gravity, I know, on the Earth... 77 00:07:43,640 --> 00:07:49,040 is 9.8 metres per second squared. 78 00:07:49,040 --> 00:07:53,080 Interestingly, you don't have to know the weight of the ball, the mass of the ball. 79 00:07:53,080 --> 00:07:57,240 That's not relevant to how far the thing's going to go. 80 00:07:57,240 --> 00:08:02,080 Two times gravity, times the height, 5.5, 81 00:08:02,080 --> 00:08:06,120 multiplied by the speed, divided by 49.1, 82 00:08:06,120 --> 00:08:08,040 take the cosine... 83 00:08:08,040 --> 00:08:12,480 That will give me a distance of 9.95 metres. 84 00:08:12,480 --> 00:08:17,600 But we've got air resistance, there's friction on the... the ramp as well. 85 00:08:19,840 --> 00:08:22,920 What about the wind today? 9.16. 86 00:08:22,920 --> 00:08:28,520 OK, so the predicted distance is going to be 5.6 metres. 87 00:08:31,520 --> 00:08:35,440 That's where I think the ball is going to land. 88 00:08:38,000 --> 00:08:44,600 Which means if I set up my deckchair here, I should be able to watch the whole thing in complete safety. 89 00:08:44,600 --> 00:08:47,240 OK, release the ball. 90 00:08:55,360 --> 00:08:58,880 And that is the power of the Code. 91 00:09:02,000 --> 00:09:04,720 We can do this again and again and again... 92 00:09:08,000 --> 00:09:12,920 ..and the numbers mean the ball is going to land in the same place each time. 93 00:09:18,480 --> 00:09:23,280 If everything in the world behaved according to equations that give definite answers, 94 00:09:23,280 --> 00:09:26,160 we'd be able to predict the future with absolute certainty. 95 00:09:28,840 --> 00:09:32,560 But unfortunately things aren't quite that simple. 96 00:09:41,600 --> 00:09:44,840 The natural world often appears so complex 97 00:09:44,840 --> 00:09:48,400 it's hard to imagine we could write equations to describe it. 98 00:09:50,160 --> 00:09:53,720 Even though we might glimpse what we think are patterns, 99 00:09:53,720 --> 00:09:56,760 they seem almost impossible to understand. 100 00:09:56,760 --> 00:09:59,320 I've come to witness a mysterious phenomenon 101 00:09:59,320 --> 00:10:03,200 that happens here in Denmark for a few short weeks every year. 102 00:10:06,360 --> 00:10:07,720 WINGS FLUTTER 103 00:10:10,960 --> 00:10:13,320 BIRDS TWITTER 104 00:10:17,880 --> 00:10:19,040 WINGS FLUTTER 105 00:10:20,920 --> 00:10:23,120 First few appearing, I think. 106 00:10:36,440 --> 00:10:38,600 These are starlings, 107 00:10:38,600 --> 00:10:43,600 making their annual migration between southern Europe and Scandinavia. 108 00:10:45,280 --> 00:10:50,000 A single flock can contain a million birds or more. 109 00:10:52,800 --> 00:10:56,400 Their dance obscures the fading evening light, 110 00:10:56,400 --> 00:10:59,680 giving the formation its eerie name - 111 00:10:59,680 --> 00:11:01,160 The Black Sun. 112 00:11:02,800 --> 00:11:06,080 There's another massive group coming in. 113 00:11:06,080 --> 00:11:07,360 Oh! 114 00:11:11,320 --> 00:11:13,840 There are thousands of them up there. 115 00:11:16,840 --> 00:11:19,200 It's not really clear why they do this. 116 00:11:19,200 --> 00:11:21,880 It's maybe like, kind of, safety in numbers. 117 00:11:21,880 --> 00:11:24,040 The whole shape looks quite intimidating. 118 00:11:24,040 --> 00:11:26,840 It looks like one large, black beast, 119 00:11:26,840 --> 00:11:29,240 frightening off any predators 120 00:11:29,240 --> 00:11:32,960 that might be looking for a bit of dinner before sunset. 121 00:11:36,840 --> 00:11:38,160 Look at that. 122 00:11:38,160 --> 00:11:40,440 Ah. 123 00:11:40,440 --> 00:11:42,000 It's almost hypnotic. 124 00:11:47,360 --> 00:11:49,800 It's amazing. There are so many of them, 125 00:11:49,800 --> 00:11:52,480 it's a wonder they don't smash into each other 126 00:11:52,480 --> 00:11:55,440 and sort of knock some out of the sky. But they don't seem to. 127 00:11:55,440 --> 00:11:58,440 Incredible synchronisation. 128 00:12:00,320 --> 00:12:01,400 Oh! 129 00:12:05,000 --> 00:12:08,600 You're never quite sure what it's going to do next. 130 00:12:09,640 --> 00:12:12,680 'It's an almost impossible achievement. 131 00:12:12,680 --> 00:12:16,200 'How can each bird predict the movements of thousands of others?' 132 00:12:23,720 --> 00:12:25,360 That's extraordinary? 133 00:12:33,520 --> 00:12:38,200 As strange as it seems, by reducing each starling to numbers, 134 00:12:38,200 --> 00:12:41,200 we can model what's happening on a computer. 135 00:12:45,200 --> 00:12:48,000 We start with a flock of virtual starlings, 136 00:12:48,000 --> 00:12:52,400 all flying at different speeds and in different directions. 137 00:12:52,400 --> 00:12:56,240 And then we give them some simple rules. 138 00:12:56,240 --> 00:13:00,880 The first is for each bird to fly at the same speed. 139 00:13:00,880 --> 00:13:04,360 The second rule is to stay close to your neighbours. 140 00:13:06,520 --> 00:13:11,120 And finally, if you see a predator nearby, get out of the way. 141 00:13:14,120 --> 00:13:17,760 Three simple rules are all it takes to create something 142 00:13:17,760 --> 00:13:22,360 that looks uncannily like the movement of a real flock of starlings. 143 00:13:25,360 --> 00:13:26,880 Oh, here they come. 144 00:13:26,880 --> 00:13:28,920 Oh! 145 00:13:28,920 --> 00:13:30,320 HE LAUGHS 146 00:13:31,640 --> 00:13:34,320 In fact, a recent study has shown 147 00:13:34,320 --> 00:13:38,040 that even in a flock of hundreds of thousands of birds, 148 00:13:38,040 --> 00:13:42,840 each starling only has to keep track of its seven nearest neighbours. 149 00:13:51,840 --> 00:13:54,280 And then...they've all gone. 150 00:13:55,440 --> 00:13:57,000 The sky's clear again. 151 00:14:02,560 --> 00:14:05,760 Who'd have thought that something so extraordinarily complex 152 00:14:05,760 --> 00:14:09,760 as a constantly shifting flock of thousands of birds in flight 153 00:14:09,760 --> 00:14:13,280 can have at its heart such a simple and elegant Code? 154 00:14:20,280 --> 00:14:22,240 WOMAN LAUGHS 155 00:14:22,240 --> 00:14:23,480 CHILD LAUGHS 156 00:14:27,080 --> 00:14:28,720 BABY CRIES 157 00:14:28,720 --> 00:14:31,720 It seems inconceivable that human beings 158 00:14:31,720 --> 00:14:36,160 could ever be reduced to a mathematical model like starlings. 159 00:14:36,160 --> 00:14:38,520 CLOCK TICKS 160 00:14:50,520 --> 00:14:54,360 But Iain Couzin studies how animals behave in groups, 161 00:14:54,360 --> 00:14:59,320 and his research has revealed some surprising parallels. 162 00:14:59,320 --> 00:15:03,760 How can you possibly begin to understand something like this huge mass of people? 163 00:15:03,760 --> 00:15:06,080 Even when you look at the crowd for a few seconds, 164 00:15:06,080 --> 00:15:09,560 you realise there's so many complicated factors at play. 165 00:15:09,560 --> 00:15:12,160 I started my research looking at simple organisms, 166 00:15:12,160 --> 00:15:14,520 organisms like ant swarms, schooling fish. 167 00:15:14,520 --> 00:15:17,800 And remarkably, our insights from studying those systems 168 00:15:17,800 --> 00:15:20,760 led to new insights in studying human crowds. 169 00:15:20,760 --> 00:15:24,160 But people are much more complicated than a...a fish or an ant. 170 00:15:24,160 --> 00:15:26,960 Exactly, but that's almost the beauty of this, 171 00:15:26,960 --> 00:15:29,480 is we're thinking about more interesting things 172 00:15:29,480 --> 00:15:33,760 when we're walking through crowds than, "How do I avoid that person and that obstacle?" 173 00:15:33,760 --> 00:15:39,000 You know, we're thinking about what we're going to cook for dinner or what our friends are doing. 174 00:15:39,000 --> 00:15:41,560 And so, in actual fact, we're almost on auto-pilot, 175 00:15:41,560 --> 00:15:44,800 and we're actually using very simple rules of interaction 176 00:15:44,800 --> 00:15:47,680 just like the schooling fish and the swarming ants. 177 00:15:50,240 --> 00:15:52,160 So can we learn things from the ants? 178 00:15:52,160 --> 00:15:54,240 We could learn an huge amount from the ants. 179 00:15:54,240 --> 00:15:56,840 Ants don't suffer from problems such as congestion. 180 00:15:56,840 --> 00:16:00,280 Because they're not selfish. And I'm afraid to say we are. 181 00:16:00,280 --> 00:16:02,840 We want to minimise our own travel time, 182 00:16:02,840 --> 00:16:07,120 but we don't necessarily care whether we do so at the expense of other individuals. 183 00:16:08,760 --> 00:16:11,800 Of all the animals Iain has studied, 184 00:16:11,800 --> 00:16:15,160 human beings are, in some ways, the most predictable. 185 00:16:15,160 --> 00:16:20,320 We walk at an optimum speed of 1.3 metres per second, 186 00:16:20,320 --> 00:16:24,520 and prefer to walk in straight lines to get to our destination. 187 00:16:25,840 --> 00:16:28,280 What happens is you will naturally fall 188 00:16:28,280 --> 00:16:31,600 into the slipstream of someone moving in the same direction as you. 189 00:16:31,600 --> 00:16:35,040 And so without you even knowing it, you're forming a lane. 190 00:16:35,040 --> 00:16:40,240 Similarly, pedestrians moving in the other direction will also form lanes, 191 00:16:40,240 --> 00:16:42,200 very much like the ants do. 192 00:16:42,200 --> 00:16:45,280 These lanes help us to avoid collisions. 193 00:16:45,280 --> 00:16:47,080 However, in a large open space, 194 00:16:47,080 --> 00:16:49,880 like the concourse at Grand Central Station, 195 00:16:49,880 --> 00:16:52,160 the lanes inevitably cross each other, 196 00:16:52,160 --> 00:16:53,800 which could lead to congestion. 197 00:16:55,800 --> 00:16:59,320 But when you put an obstacle - like this information desk - 198 00:16:59,320 --> 00:17:03,200 in the middle of the crowd, rather than getting in the way, 199 00:17:03,200 --> 00:17:06,000 it acts like a roundabout 200 00:17:06,000 --> 00:17:10,160 and increases the flow through the station by as much as 13%. 201 00:17:17,960 --> 00:17:21,120 These rules are so effective at predicting what we'll do, 202 00:17:21,120 --> 00:17:25,480 they can even be used to simulate crowds of people. 203 00:17:27,600 --> 00:17:31,920 Each individual is actually described by a set of numbers 204 00:17:31,920 --> 00:17:34,000 as they move through an environment. 205 00:17:34,000 --> 00:17:37,840 Exactly. We're capturing the average type of behaviour of pedestrians. 206 00:17:37,840 --> 00:17:41,880 We're capturing these simple and local rules that people use within crowds 207 00:17:41,880 --> 00:17:44,600 to then make predictions as to how the whole crowd 208 00:17:44,600 --> 00:17:47,120 is going to flow through different environments. 209 00:17:48,760 --> 00:17:51,840 We can use this underlying Code of the crowd 210 00:17:51,840 --> 00:17:56,040 to design buildings that are more efficient and safer. 211 00:17:57,440 --> 00:18:01,040 Simulations like these are able to accurately predict 212 00:18:01,040 --> 00:18:03,920 how quickly a building can be evacuated, 213 00:18:03,920 --> 00:18:06,360 even before it has been built. 214 00:18:13,640 --> 00:18:17,680 As a crowd, people are incredibly predictable. 215 00:18:17,680 --> 00:18:21,560 There are simple rules that we follow without being aware of it. 216 00:18:24,400 --> 00:18:27,280 But most of the time, we don't live on autopilot. 217 00:18:28,840 --> 00:18:34,080 And when the crowd disperses, so too do the rules of group behaviour. 218 00:18:34,080 --> 00:18:35,640 SIREN BLARES 219 00:18:35,640 --> 00:18:41,040 As individuals with our own free will, we're much harder to predict. 220 00:18:41,040 --> 00:18:42,480 Or so we think. 221 00:18:49,280 --> 00:18:52,160 Before we gets started, I would like to mention the rules. 222 00:18:52,160 --> 00:18:53,360 They are very simple. 223 00:18:53,360 --> 00:18:56,320 There are three throws and there are only three throws. 224 00:18:56,320 --> 00:19:00,560 We use a three-prime shoot, which means you go one, two, three 225 00:19:00,560 --> 00:19:03,400 and you release your throw on four. 226 00:19:04,520 --> 00:19:06,840 A throw of rock is a closed fist. 227 00:19:06,840 --> 00:19:10,120 You can throw it any way you want as long as it is a closed fist. 228 00:19:10,120 --> 00:19:11,720 Your paper must be horizontal. 229 00:19:11,720 --> 00:19:13,760 Your scissors must be vertical. 230 00:19:13,760 --> 00:19:15,920 That will be foul. 231 00:19:18,200 --> 00:19:22,320 The game of rock, paper, scissors is known all over the world. 232 00:19:24,120 --> 00:19:27,160 And some people take it very seriously. 233 00:19:27,160 --> 00:19:30,080 For those of you who don't know, and there should be very few, 234 00:19:30,080 --> 00:19:32,840 the throw of paper covers the throw of rock. 235 00:19:32,840 --> 00:19:35,160 The throw of scissors cuts the throw of paper, 236 00:19:35,160 --> 00:19:37,760 and the throw of rock crushes the throw of scissors. 237 00:19:42,400 --> 00:19:45,280 In Philadelphia, the Rock, Paper, Scissors League 238 00:19:45,280 --> 00:19:47,320 competes four times a week. 239 00:19:48,760 --> 00:19:51,240 The people in this room are fighting 240 00:19:51,240 --> 00:19:54,240 to go to the world championship in Las Vegas 241 00:19:54,240 --> 00:19:57,400 and the chance to win 10,000. 242 00:20:03,400 --> 00:20:06,480 Sweetji in the lead. Rock versus scissors for Sweetji. 243 00:20:06,480 --> 00:20:09,800 You're on the verge of elimination, Drew Bag. 244 00:20:09,800 --> 00:20:12,240 Third and final set, winner moves on. 245 00:20:12,240 --> 00:20:13,720 THE CROWD CHANTS AND CLAPS 246 00:20:13,720 --> 00:20:15,640 Rock versus scissors. 247 00:20:15,640 --> 00:20:18,480 And what a match, to take us down to the final four. 248 00:20:20,280 --> 00:20:24,600 The intriguing thing about this game is that it should be impossible 249 00:20:24,600 --> 00:20:27,800 to predict what your opponent's going to do next. 250 00:20:28,960 --> 00:20:32,120 In rock, paper, scissors, they're all pretty much equivalent. 251 00:20:32,120 --> 00:20:36,080 So each throw beats one and loses to another, 252 00:20:36,080 --> 00:20:38,640 so essentially it's a game of even odds. 253 00:20:38,640 --> 00:20:40,320 A bit like a flip of a coin. 254 00:20:42,360 --> 00:20:44,640 But if the game is entirely random, 255 00:20:44,640 --> 00:20:47,560 every player would be evenly matched. 256 00:20:47,560 --> 00:20:51,120 And yet some people win time and time again. 257 00:20:51,120 --> 00:20:52,840 It is match point, Sweetji. 258 00:20:52,840 --> 00:20:55,880 B-Pac has no points here in round number two. 259 00:20:55,880 --> 00:20:58,040 He will need two straight throws. 260 00:20:58,040 --> 00:20:59,600 Can he get through number one? 261 00:20:59,600 --> 00:21:01,560 No. Sweetji! 262 00:21:01,560 --> 00:21:04,640 So now our final match of the night. 263 00:21:04,640 --> 00:21:08,280 Sweetji, you're going to play dOGulas. 264 00:21:08,280 --> 00:21:12,720 The more we play, the more we're influenced by our past throws. 265 00:21:12,720 --> 00:21:13,920 Begin. 266 00:21:13,920 --> 00:21:17,480 And that creates patterns that can be exploited to win the game. 267 00:21:17,480 --> 00:21:21,200 Sweetji came fifth in the league last year, 268 00:21:21,200 --> 00:21:24,920 and this season looks set to do even better. 269 00:21:28,440 --> 00:21:30,480 dOGulas! 270 00:21:30,480 --> 00:21:32,040 Rock crushes scissors. 271 00:21:32,040 --> 00:21:35,080 Sweetji still has point... 272 00:21:35,080 --> 00:21:36,720 Rock crushes scissors! 273 00:21:36,720 --> 00:21:37,760 SHE SCREAMS 274 00:21:37,760 --> 00:21:42,440 Sweetji, Philadelphia Rock, Paper, Scissors City League Champion here at the Raven Lounge. 275 00:21:42,440 --> 00:21:44,400 Congratulations. Thank you. 276 00:21:44,400 --> 00:21:47,480 So that was five consecutive wins. 277 00:21:47,480 --> 00:21:52,000 What was the key to your success, do you think? I try to read people. 278 00:21:52,000 --> 00:21:55,240 Yeah, you do, yeah? Or at least try to think what they're thinking. 279 00:21:55,240 --> 00:21:58,480 You're looking for their patterns then? Yeah, a little bit like... 280 00:21:58,480 --> 00:22:02,280 Their patterns, and they'll be trying to learn mine and go against that. 281 00:22:06,520 --> 00:22:09,720 Rock, paper, scissors reveals a fundamental truth 282 00:22:09,720 --> 00:22:11,120 about human nature. 283 00:22:13,040 --> 00:22:15,400 We are so addicted to patterns 284 00:22:15,400 --> 00:22:17,920 that we let them seep into almost everything we do. 285 00:22:21,480 --> 00:22:23,400 And these patterns are the key 286 00:22:23,400 --> 00:22:26,320 to predicting many aspects of our behaviour. 287 00:22:26,320 --> 00:22:28,320 Even the darkest parts of our nature. 288 00:22:32,800 --> 00:22:35,840 SCREAMS 289 00:22:35,840 --> 00:22:38,600 Deceased. Female, five foot two. 290 00:22:38,600 --> 00:22:42,200 Complexion, dark. Eyes, brown. Hair, brown. 291 00:22:44,640 --> 00:22:48,240 When you see this much activity in such a small geographic area 292 00:22:48,240 --> 00:22:49,800 in such a tight time frame, 293 00:22:49,800 --> 00:22:52,200 that's a warning bell that something's going on, 294 00:22:52,200 --> 00:22:53,760 we have a predator operating. 295 00:22:55,280 --> 00:23:00,760 Kim Rossmo has 20 years' experience as a Detective Inspector. 296 00:23:00,760 --> 00:23:04,440 He specialises in hunting down serial killers. 297 00:23:06,200 --> 00:23:08,680 The victim's body was found here in the corner 298 00:23:08,680 --> 00:23:12,760 by a police officer that came in shortly after the crime had occurred. 299 00:23:12,760 --> 00:23:14,760 The prime crime scene would be... 300 00:23:15,960 --> 00:23:17,800 But Rossmo is no ordinary cop, 301 00:23:17,800 --> 00:23:20,640 because he's got a PhD 302 00:23:20,640 --> 00:23:25,440 and uses mathematics to understand the patterns criminals leave behind. 303 00:23:29,720 --> 00:23:32,560 There's a logic in how the offender hunted for the victim 304 00:23:32,560 --> 00:23:35,000 and the location where he committed the crime. 305 00:23:35,000 --> 00:23:38,120 If we can decode that and if we can understand that pattern, 306 00:23:38,120 --> 00:23:41,680 we can use that information to help us focus a criminal investigation. 307 00:23:44,200 --> 00:23:51,240 The reason it's so hard to catch serial killers is because there's often no link to their crimes. 308 00:23:51,240 --> 00:23:53,400 They kill random strangers 309 00:23:53,400 --> 00:23:56,920 in locations they have no obvious connection to. 310 00:23:56,920 --> 00:23:59,960 It's very common in the investigation of a serial murder case 311 00:23:59,960 --> 00:24:03,520 to have hundreds, thousands, even tens of thousands of suspects. 312 00:24:03,520 --> 00:24:05,520 It's a needle-in-a-haystack problem. 313 00:24:07,600 --> 00:24:09,160 Where do you start? 314 00:24:10,320 --> 00:24:15,680 In 1888, the most notorious serial killer of all, Jack the Ripper, 315 00:24:15,680 --> 00:24:18,840 killed five women in London's East End. 316 00:24:19,920 --> 00:24:25,720 Since then, countless people have tried to solve the mystery of the Ripper's identity. 317 00:24:25,720 --> 00:24:28,360 But Rossmo thinks he could have tracked him down 318 00:24:28,360 --> 00:24:30,200 without seeing a scrap of evidence. 319 00:24:31,560 --> 00:24:35,280 Because he's worked out where Jack the Ripper most likely lived. 320 00:24:35,280 --> 00:24:38,800 Based only on the location of the crimes. 321 00:24:38,800 --> 00:24:44,400 Flower and Dean Street should have been the epicentre of their search. 322 00:24:44,400 --> 00:24:47,640 And all he used to do it is an equation. 323 00:24:51,200 --> 00:24:52,680 Inherently, we're all lazy, 324 00:24:52,680 --> 00:24:55,560 and criminals just as much as anyone else. 325 00:24:55,560 --> 00:24:59,200 They want to accomplish their goals close to home rather than further away, 326 00:24:59,200 --> 00:25:02,760 because it involves too much effort, too much time, too much travel. 327 00:25:04,360 --> 00:25:06,400 The first half of Rossmo's equation 328 00:25:06,400 --> 00:25:09,920 models what's known as the least-effort principle. 329 00:25:09,920 --> 00:25:12,040 It means that the crime locations 330 00:25:12,040 --> 00:25:17,080 are statistically more likely the nearer they are to where the offender lives. 331 00:25:17,080 --> 00:25:20,240 If you have a choice of going to the corner store for a loaf of bread 332 00:25:20,240 --> 00:25:23,680 or one that's seven miles down the road, you'll pick the corner store. 333 00:25:23,680 --> 00:25:27,160 It seems a bit gruesome to apply the same thing to a serial killer 334 00:25:27,160 --> 00:25:29,960 as to going and buying a loaf of bread or milk. 335 00:25:29,960 --> 00:25:34,960 Well, actually, if we can get over the horrible nature of these crimes 336 00:25:34,960 --> 00:25:38,560 and recognise that these are human beings like the rest of us, 337 00:25:38,560 --> 00:25:41,640 we can, because we understand ourselves, 338 00:25:41,640 --> 00:25:44,320 maybe bet some understanding of these individuals. 339 00:25:46,080 --> 00:25:48,160 The second half of the equation 340 00:25:48,160 --> 00:25:51,080 describes something called the buffer zone. 341 00:25:51,080 --> 00:25:54,440 Criminals avoid committing crimes too close to home, 342 00:25:54,440 --> 00:25:56,800 for fear of drawing attention to themselves. 343 00:25:58,320 --> 00:26:02,000 It's the interaction of these two behaviours that allows Rossmo 344 00:26:02,000 --> 00:26:05,760 to calculate the most probable location of the criminal. 345 00:26:05,760 --> 00:26:09,280 These individuals have to not only obtain their target - 346 00:26:09,280 --> 00:26:10,480 or capture a victim - 347 00:26:10,480 --> 00:26:15,000 but avoid apprehension by the police and identification by witnesses. 348 00:26:18,720 --> 00:26:21,840 The technique, known as geographic profiling, 349 00:26:21,840 --> 00:26:25,000 is now used by police all over the world. 350 00:26:30,720 --> 00:26:34,160 Police are examining the possibility that a small explosion 351 00:26:34,160 --> 00:26:36,960 near a branch of Barclays Bank in West London 352 00:26:36,960 --> 00:26:38,960 was the work of an extortionist. 353 00:26:38,960 --> 00:26:40,280 Police believe the demand 354 00:26:40,280 --> 00:26:42,680 came from the blackmailer known as Mardi Gra. 355 00:26:44,960 --> 00:26:48,600 In the late '90s, Rossmo was called in by Scotland Yard 356 00:26:48,600 --> 00:26:52,400 to help catch the notorious Mardi Gra bomber, 357 00:26:52,400 --> 00:26:55,600 who for three years waged a campaign of terror 358 00:26:55,600 --> 00:26:58,000 against banks and supermarkets. 359 00:26:58,000 --> 00:27:01,160 A 17-year-old man is recovering in hospital after being injured 360 00:27:01,160 --> 00:27:04,000 in an explosion at a Sainsbury's store in South London. 361 00:27:04,000 --> 00:27:06,400 'Police are advising the public to be vigilant. 362 00:27:06,400 --> 00:27:10,440 'In truth, they can only wait to see what Mardi Gra does next.' 363 00:27:16,440 --> 00:27:19,400 How many bombs did he let off during that time? 364 00:27:19,400 --> 00:27:23,480 Total, 36 known linked offences. 365 00:27:23,480 --> 00:27:26,800 So you can see, they range from the north of Cambridge, 366 00:27:26,800 --> 00:27:29,080 all the way down to the strait of Dover. 367 00:27:29,080 --> 00:27:31,800 But most of them are in Greater London. 368 00:27:31,800 --> 00:27:35,920 So this is a map showing the locations of all the bombs that were set off? 369 00:27:35,920 --> 00:27:39,240 That's right. There's certainly a concentration on London, 370 00:27:39,240 --> 00:27:42,200 but it looks pretty randomly scattered. 371 00:27:42,200 --> 00:27:45,640 So now you're feeding those locations into the equation? 372 00:27:45,640 --> 00:27:49,400 Right. And what we have here now is the geo-profile. 373 00:27:49,400 --> 00:27:52,840 And that's going to show us the most likely location 374 00:27:52,840 --> 00:27:54,200 where the offender lived. 375 00:27:54,200 --> 00:27:58,040 With dark orange being the most likely or the most probable. 376 00:27:58,040 --> 00:28:02,040 So we can see that the major focus is around the Chiswick area. 377 00:28:02,040 --> 00:28:05,080 In fact, in the report we prepared for Scotland Yard, 378 00:28:05,080 --> 00:28:07,800 we even prioritised postcodes for that. 379 00:28:07,800 --> 00:28:09,880 And how successful was it in this case? 380 00:28:09,880 --> 00:28:12,840 Well, let me show you the locations... 381 00:28:12,840 --> 00:28:15,800 of the two brothers, Edgar and Ronald Pearce. 382 00:28:15,800 --> 00:28:19,320 Right, that is really in the hot zone, isn't it? Yes. 383 00:28:19,320 --> 00:28:21,600 Edgar's home is in the top 0.8% 384 00:28:21,600 --> 00:28:24,880 of the area of the crimes in Greater London. 385 00:28:24,880 --> 00:28:27,280 So less than 1%. That's extraordinary. 386 00:28:31,320 --> 00:28:35,480 Edgar Pearce had demanded £10,000 a day from Barclays. 387 00:28:35,480 --> 00:28:39,560 And when he and his brother tried to collect it from a cash point in Chiswick, 388 00:28:39,560 --> 00:28:41,360 the police were waiting. 389 00:28:41,360 --> 00:28:44,520 Two bothers in their 60s were remanded in custody by magistrates 390 00:28:44,520 --> 00:28:47,760 in connection with the so-called Mardi Gra bombings. 391 00:28:47,760 --> 00:28:51,000 Ronald and Edgar Pearce, both from Chiswick in West London, 392 00:28:51,000 --> 00:28:54,800 each face three conspiracy charges. 393 00:28:54,800 --> 00:28:58,720 Based on the apparently random location of 36 bombs, 394 00:28:58,720 --> 00:29:04,000 Rossmo's geographic profile narrowed the location of the Mardi Gra bomber 395 00:29:04,000 --> 00:29:07,960 from 300 square miles to a postcode in Chiswick. 396 00:29:10,800 --> 00:29:13,040 Although his bother, Ronald, was acquitted, 397 00:29:13,040 --> 00:29:18,160 Edgar Pearce pleaded guilty and was jailed for 21 years. 398 00:29:18,160 --> 00:29:22,320 So do you think the bomber was aware that he was creating these patterns? 399 00:29:22,320 --> 00:29:25,880 No, he wasn't. But it's very difficult for humans 400 00:29:25,880 --> 00:29:28,760 to engage in completely random behaviour. 401 00:29:34,640 --> 00:29:38,760 Very few of us are aware of the patterns we leave behind. 402 00:29:38,760 --> 00:29:40,760 WOMAN LAUGHS 403 00:29:40,760 --> 00:29:43,560 From the way we move in a crowd... 404 00:29:46,120 --> 00:29:48,200 ..to the choices we make in a game... 405 00:29:48,200 --> 00:29:50,240 Paper covers rock! 406 00:29:50,240 --> 00:29:53,320 The victim's body was found here... 407 00:29:53,320 --> 00:29:55,960 ..or even how we commit murder. 408 00:29:55,960 --> 00:29:59,400 In reality, these crimes are not random... 409 00:29:59,400 --> 00:30:01,400 None of it is random. 410 00:30:01,400 --> 00:30:05,560 It's all part of the Code. 411 00:30:05,560 --> 00:30:08,200 There are always tell-tale patterns. 412 00:30:08,200 --> 00:30:11,640 And if we're able to decode them, 413 00:30:11,640 --> 00:30:15,120 we can use those patterns to model our behaviour. 414 00:30:15,120 --> 00:30:19,520 And this leads to the intriguing possibility 415 00:30:19,520 --> 00:30:25,800 that if we can reduce human beings to numbers, we might be able to predict our future 416 00:30:25,800 --> 00:30:30,760 in the same way as we can predict the movement of the planets or the trajectory of a ball. 417 00:30:39,800 --> 00:30:44,480 But the course of our lives never seems to run entirely smoothly, 418 00:30:44,480 --> 00:30:49,800 and the future rarely turns out exactly as we'd planned. 419 00:30:49,800 --> 00:30:53,760 I may have a good idea what I'm going to be doing tomorrow, or even next week, 420 00:30:53,760 --> 00:30:58,560 but as the weeks turn into months and months to years, our future becomes less certain. 421 00:31:02,160 --> 00:31:06,600 Every decision we make, every situation we encounter, 422 00:31:06,600 --> 00:31:11,440 every person we meet, sends our life down a different path. 423 00:31:12,440 --> 00:31:17,760 As you watch each stick floating off downstream, there's no sure way of predicting their fate. 424 00:31:17,760 --> 00:31:22,760 I might be able to hazard a guess where a stick will be in two minutes. 425 00:31:22,760 --> 00:31:25,760 But what about two hours? Two days? 426 00:31:25,760 --> 00:31:29,000 '..Turn into years, our future becomes far less certain.' 427 00:31:29,000 --> 00:31:35,240 Life sometimes seems so unpredictable that we think of it as being random. 428 00:31:35,240 --> 00:31:38,560 But in fact it isn't random at all. 429 00:31:38,560 --> 00:31:40,880 Simply a sequence of cause and effect. 430 00:31:40,880 --> 00:31:44,320 A freak accident. 431 00:31:44,320 --> 00:31:45,800 I'm so sorry. 432 00:31:45,800 --> 00:31:47,480 A slight delay. 433 00:31:47,480 --> 00:31:49,240 A missed bus. 434 00:31:49,240 --> 00:31:51,880 A broken promise. 435 00:31:51,880 --> 00:31:58,960 There are millions of factors that intervene to affect our journey through life, 436 00:31:58,960 --> 00:32:04,000 and the tiniest shift in any one of these can completely change its future course. 437 00:32:05,000 --> 00:32:07,880 The white one's caught in a dam, but the red one's fast. 438 00:32:19,560 --> 00:32:23,280 I think this'd be a good finishing line. 439 00:32:23,280 --> 00:32:27,480 And here comes the white. It's way ahead of the red. 440 00:32:28,040 --> 00:32:32,080 And white's the winner. 441 00:32:32,080 --> 00:32:33,760 Right, let's give it another go. 442 00:32:33,760 --> 00:32:40,560 The truth is, our lives are controlled by the strangest code of all... 443 00:32:40,560 --> 00:32:42,760 the code of chaos. 444 00:32:46,000 --> 00:32:49,520 Our lives aren't random, they're chaotic, 445 00:32:49,520 --> 00:32:54,920 a tangled web of cause and effect in which insignificant moments 446 00:32:54,920 --> 00:32:59,600 can escalate into events that change our lives forever. 447 00:32:59,600 --> 00:33:05,680 Any difference, no matter how small, can have a huge effect on the outcome. 448 00:33:05,680 --> 00:33:09,640 It's this incredible sensitivity to even the slightest change 449 00:33:09,640 --> 00:33:12,320 which is one of the defining features of chaos. 450 00:33:18,320 --> 00:33:23,800 Because chaotic systems appear so random, it's often difficult to see a pattern. 451 00:33:25,400 --> 00:33:31,920 And that has led us to sometimes misinterpret our world in a spectacular manner. 452 00:33:37,640 --> 00:33:40,920 'In this land of many mysteries, it's a strange fact 453 00:33:40,920 --> 00:33:44,720 'that large legends seem to collect around the smallest creatures. 454 00:33:44,720 --> 00:33:49,320 'One of these is a mousy little rodent called the lemming. 455 00:33:49,320 --> 00:33:53,200 'Here's an actual living legend, for it's said of this tiny animal 456 00:33:53,200 --> 00:33:57,360 ''that it commits mass suicide by rushing into the sea in droves. 457 00:33:57,360 --> 00:34:00,280 This film from 1958 458 00:34:00,280 --> 00:34:03,200 set out to explain the wildly fluctuating population 459 00:34:03,200 --> 00:34:05,040 of these tiny rodents. 460 00:34:11,560 --> 00:34:19,000 'Ahead lies the Arctic shore, and beyond, the sea. And still the little animals surge forward. 461 00:34:21,840 --> 00:34:25,680 'Their frenzy takes them tumbling down the terraced cliffs, 462 00:34:25,680 --> 00:34:29,400 'creating tiny avalanches of sliding soil and rocks.' 463 00:34:33,120 --> 00:34:35,000 The legend of suicidal lemmings 464 00:34:35,000 --> 00:34:39,800 was the accepted explanation for why the Arctic can be overrun with them one year 465 00:34:39,800 --> 00:34:41,480 and completely empty the next. 466 00:34:41,480 --> 00:34:45,520 'They reach the final precipice. 467 00:34:45,520 --> 00:34:48,240 'This is the last chance to turn back. 468 00:34:52,520 --> 00:34:56,720 'Yet over they go, casting themselves bodily out into space.' 469 00:35:01,240 --> 00:35:07,560 This film popularised the belief that lemmings are stupid, reckless and suicidal. 470 00:35:07,560 --> 00:35:10,280 The very word "lemming" has come to mean as much. 471 00:35:15,400 --> 00:35:18,520 The trouble is, though, it isn't true. 472 00:35:18,520 --> 00:35:22,160 In fact, it's been claimed that the whole thing was faked. 473 00:35:26,240 --> 00:35:30,840 The film-makers apparently flew in hundreds of captive-bred lemmings 474 00:35:30,840 --> 00:35:33,840 and drove them over the cliffs and out to sea. 475 00:35:37,080 --> 00:35:40,200 'Soon the Arctic Sea is dotted with tiny bobbing bodies. 476 00:35:43,800 --> 00:35:48,800 'And so is acted out the legend of mass suicide.' 477 00:35:48,800 --> 00:35:53,960 Now, as appalling as this sounds, the reason for the alleged lemming abuse stems not so much 478 00:35:53,960 --> 00:35:55,840 from ignoring the moral code, 479 00:35:55,840 --> 00:35:58,600 but rather an ignorance of the mathematical one. 480 00:35:58,600 --> 00:36:04,640 What no-one knew at the time was that the incredible fluctuation 481 00:36:04,640 --> 00:36:08,760 in lemming numbers has nothing to do with mass suicide. 482 00:36:09,960 --> 00:36:12,640 It's all because of chaos. 483 00:36:12,640 --> 00:36:15,880 And there's a simple equation at its heart. 484 00:36:17,800 --> 00:36:22,720 So, if I want to know how many lemmings there'll be next year, 485 00:36:22,720 --> 00:36:26,480 what I need to do is take this year's population, "P", 486 00:36:26,480 --> 00:36:29,400 and multiply that by the growth rate "R". 487 00:36:29,400 --> 00:36:31,840 But not all lemmings will survive, 488 00:36:31,840 --> 00:36:36,480 so there's a bit of the equation which tells me how many lemmings will die during the year. 489 00:36:36,480 --> 00:36:39,160 So that's R times P times P. 490 00:36:39,160 --> 00:36:42,440 So we can rewrite this equation 491 00:36:42,440 --> 00:36:47,320 as the growth rate R times P times one minus P. 492 00:36:47,320 --> 00:36:50,040 Now, this equation isn't specific to lemmings, 493 00:36:50,040 --> 00:36:52,680 it actually applies to any animal population. 494 00:36:52,680 --> 00:36:57,360 And the interesting part of the equation is this number R, the growth rate. 495 00:36:57,360 --> 00:37:00,400 Because when we choose different values for R, 496 00:37:00,400 --> 00:37:03,680 we get a very different behaviour for the population growth. 497 00:37:04,600 --> 00:37:08,840 The growth rate determines how quickly a population expands. 498 00:37:08,840 --> 00:37:12,800 For most species of mammal, this is usually below 2. 499 00:37:12,800 --> 00:37:16,640 With a growth rate in this range, the equation predicts 500 00:37:16,640 --> 00:37:20,760 that a population will rise until it stabilises at a fixed value. 501 00:37:20,760 --> 00:37:26,640 But it turns out lemmings are one of the fastest-reproducing mammals on the planet. 502 00:37:26,640 --> 00:37:30,360 Let's take R equals 3.1. 503 00:37:30,360 --> 00:37:35,480 The lemmings don't stabilise now, but ping-pong between two different values. 504 00:37:35,480 --> 00:37:40,080 So the population is high, then low, and back to high again, low again. 505 00:37:40,080 --> 00:37:44,640 But when the growth rate reaches a value just over 3.57 506 00:37:44,640 --> 00:37:49,080 then something incredibly unexpected happens. 507 00:37:49,080 --> 00:37:52,080 Rather than levelling off at a fixed number, 508 00:37:52,080 --> 00:37:58,680 or fluctuating between two values, their population erupts into chaos. 509 00:37:58,680 --> 00:38:04,800 A plague of almost biblical proportions one year can plummet to near extinction the next. 510 00:38:04,800 --> 00:38:09,080 It's almost impossible to predict how many lemmings you're going to have. 511 00:38:09,080 --> 00:38:12,280 In fact, there doesn't seem to be any pattern to this at all. 512 00:38:12,280 --> 00:38:16,520 And of course, this is exactly what's seen in reality. 513 00:38:16,520 --> 00:38:20,280 Unpredictable boom-and-bust lemming populations. 514 00:38:21,800 --> 00:38:25,640 Lemmings are one of the few creatures on Earth that breed so quickly 515 00:38:25,640 --> 00:38:29,120 their growth rate can sometimes exceed this tipping point. 516 00:38:32,600 --> 00:38:37,760 It's such an odd phenomenon that mass suicide seems like a plausible answer. 517 00:38:37,760 --> 00:38:41,320 But the real explanation comes from the Code. 518 00:38:41,320 --> 00:38:42,920 From this equation. 519 00:38:48,080 --> 00:38:53,640 The problem is we can never know exactly how many lemmings are born or how many die. 520 00:38:53,640 --> 00:39:00,080 And just the smallest difference in the growth rate R, produces a totally different answer. 521 00:39:00,080 --> 00:39:04,360 And this is true of all equations that model chaos. 522 00:39:04,360 --> 00:39:08,240 Although they can explain how something happens, 523 00:39:08,240 --> 00:39:11,440 they're almost useless at predicting the future. 524 00:39:19,320 --> 00:39:22,640 I can use an equation to calculate where this ball will land, 525 00:39:22,640 --> 00:39:25,960 because even if I'm slightly out in any of my measurements, 526 00:39:25,960 --> 00:39:28,960 it will only make a small difference to the final result. 527 00:39:28,960 --> 00:39:34,280 The ball will be released from the ramp at 49.1 degrees. 528 00:39:34,280 --> 00:39:37,640 But if this ball behaved according to the laws of chaos, 529 00:39:37,640 --> 00:39:40,360 the tiniest shift in the ball's position 530 00:39:40,360 --> 00:39:45,720 or the angle of release could dramatically alter its trajectory. 531 00:39:47,520 --> 00:39:50,760 I'd have no idea whether it would just simply fall harmlessly 532 00:39:50,760 --> 00:39:52,040 off the end of the ramp. 533 00:39:55,040 --> 00:39:58,080 Or be sent into orbit. 534 00:40:02,520 --> 00:40:06,480 I'd have no idea where to put my deckchair. 535 00:40:08,240 --> 00:40:12,320 It turns out that much of the world is chaotic, 536 00:40:12,320 --> 00:40:15,920 making it almost impossible to predict. 537 00:40:17,760 --> 00:40:20,920 But that doesn't stop us trying. 538 00:40:24,880 --> 00:40:27,360 Knowing whether the sun is going to shine 539 00:40:27,360 --> 00:40:30,720 or the heavens are going to open, is a British obsession. 540 00:40:30,720 --> 00:40:34,840 But trying to plan our lives around the vagaries of the weather 541 00:40:34,840 --> 00:40:36,360 seems almost futile. 542 00:40:42,920 --> 00:40:48,360 Even though we have precise equations that can describe how clashing air masses interact 543 00:40:48,360 --> 00:40:50,840 to create clouds, wind and rainfall, 544 00:40:50,840 --> 00:40:56,520 it doesn't really help us very much with our predictions. 545 00:40:56,520 --> 00:40:59,920 THUNDER CRASHES 546 00:40:59,920 --> 00:41:05,520 That's because we can never know the exact speed of every air particle. 547 00:41:05,520 --> 00:41:08,320 The precise temperature at every point in space, 548 00:41:08,320 --> 00:41:11,280 or the pressure across the whole planet. 549 00:41:11,280 --> 00:41:14,160 And just a small variation in any one of these 550 00:41:14,160 --> 00:41:16,880 can produce a vastly different forecast. 551 00:41:22,400 --> 00:41:26,040 This is a map of how the weather looks right now. 552 00:41:26,040 --> 00:41:31,600 The blue lines represent cold fronts and the red lines represent warm fronts. 553 00:41:31,600 --> 00:41:33,400 In order to make a prediction 554 00:41:33,400 --> 00:41:36,880 what we do is to take the mathematical equations for the weather 555 00:41:36,880 --> 00:41:38,760 and create a model. 556 00:41:38,760 --> 00:41:43,200 Now the trouble is, I can't know the precise atmospheric conditions, 557 00:41:43,200 --> 00:41:45,640 so I take as much data as possible. 558 00:41:45,640 --> 00:41:50,760 Then I make small variations in the data and run the model again 559 00:41:50,760 --> 00:41:56,680 and again and again and what I get is different predictions according to those slight variations. 560 00:41:56,680 --> 00:42:00,800 So for the weather tomorrow, the predictions are petty similar. 561 00:42:00,800 --> 00:42:05,000 We've got a lot of blue lines together predicting a cold front. 562 00:42:05,000 --> 00:42:08,080 A lot of red lines together predicting a warm front. 563 00:42:08,080 --> 00:42:11,840 But look what happens when I look a little bit further ahead. 564 00:42:11,840 --> 00:42:15,560 So two days, three days ahead... 565 00:42:15,560 --> 00:42:20,280 so you can see these different predictions are beginning to spread out. 566 00:42:20,280 --> 00:42:23,280 You can still see some sort of pattern in the weather 567 00:42:23,280 --> 00:42:26,400 but if I move a week ahead... 568 00:42:27,480 --> 00:42:31,040 ..and I couldn't hazard a guess as to what the weather's going to be. 569 00:42:31,040 --> 00:42:33,800 There are red and blue lines all over the place. 570 00:42:33,800 --> 00:42:37,000 One prediction says it's going to be hot, the other says cold, 571 00:42:37,000 --> 00:42:40,800 and if I go ten days ahead, 572 00:42:40,800 --> 00:42:44,120 it just looks like a scrambled mess of spaghetti. 573 00:42:44,120 --> 00:42:49,840 There's absolutely no way to make any prediction that far in advance. 574 00:42:49,840 --> 00:42:52,280 And that's why beyond just a few days, 575 00:42:52,280 --> 00:42:55,680 the weather forecast can be so spectacularly wrong. 576 00:43:00,400 --> 00:43:03,720 Once we understand that the atmosphere is chaotic, 577 00:43:03,720 --> 00:43:08,440 we can appreciate that the smallest change in the initial conditions 578 00:43:08,440 --> 00:43:11,360 can dramatically alter what will happen. 579 00:43:14,480 --> 00:43:18,720 The movement of just one molecule of air can be magnified over time 580 00:43:18,720 --> 00:43:21,680 to have a huge effect on the weather as a whole. 581 00:43:24,560 --> 00:43:28,000 We refer to this phenomenon as the "butterfly effect". 582 00:43:28,000 --> 00:43:33,840 The idea that something as small as the flap of a butterfly's wings 583 00:43:33,840 --> 00:43:36,000 might create changes in the atmosphere 584 00:43:36,000 --> 00:43:40,760 that could ultimately lead to a tornado on the other side of the world. 585 00:43:40,760 --> 00:43:43,760 CRASHING THUNDER 586 00:43:58,720 --> 00:44:03,000 As a crowd, the patterns we make are incredibly predictable. 587 00:44:04,840 --> 00:44:09,840 Even as individuals our actions are controlled by the Code. 588 00:44:13,960 --> 00:44:16,720 And by untangling chaotic systems like the weather, 589 00:44:16,720 --> 00:44:21,480 we've uncovered evidence of the Code in what we once thought of 590 00:44:21,480 --> 00:44:24,360 as impossibly complex. 591 00:44:26,200 --> 00:44:28,920 When we look at things from a different angle, 592 00:44:28,920 --> 00:44:31,560 surprising patterns emerge. 593 00:44:33,840 --> 00:44:39,800 Patterns that can reveal defining truths about ourselves and our future. 594 00:44:47,760 --> 00:44:51,880 In 1906, an unfortunate cow laid down its life 595 00:44:51,880 --> 00:44:54,400 for a place in mathematical history. 596 00:44:54,400 --> 00:44:55,720 One. 597 00:44:55,720 --> 00:44:57,440 Ten. 598 00:44:57,440 --> 00:44:59,160 264. 599 00:44:59,160 --> 00:45:01,400 417. 600 00:45:01,400 --> 00:45:06,760 'The cow was the subject of a guess-the-weight competition at a village fare. 601 00:45:06,760 --> 00:45:09,120 'The lucky person who came closest 602 00:45:09,120 --> 00:45:12,240 'would win the slaughtered animal's meat.' 603 00:45:13,240 --> 00:45:14,800 1,020. 604 00:45:16,280 --> 00:45:17,480 2,137. 605 00:45:17,480 --> 00:45:20,520 'The amazing thing was nobody guessed correctly.' 606 00:45:20,520 --> 00:45:21,720 ..570. 607 00:45:21,720 --> 00:45:24,720 'And yet everybody got it right.' 608 00:45:26,240 --> 00:45:28,760 4,510. 609 00:45:30,160 --> 00:45:32,160 To show you how they did it, 610 00:45:32,160 --> 00:45:36,800 I'm not going to use a cow, I'm going to use a jar of jelly beans. 611 00:45:40,240 --> 00:45:41,720 450? 612 00:45:41,720 --> 00:45:43,000 800? 613 00:45:43,000 --> 00:45:44,040 12,000. 614 00:45:44,040 --> 00:45:45,640 7,000. 615 00:45:45,640 --> 00:45:48,800 How many jellybeans do you think there are in this jar? 616 00:45:48,800 --> 00:45:50,320 Um, 617 00:45:50,320 --> 00:45:52,840 50... 618 00:45:52,840 --> 00:45:54,440 80 thousand. 619 00:45:54,440 --> 00:45:55,920 80 thousand? 620 00:45:55,920 --> 00:45:58,880 No, actually 50,000. 621 00:45:58,880 --> 00:46:00,600 50,000. OK, yeah. 622 00:46:04,600 --> 00:46:09,080 It's incredibly difficult for anyone to guess how many jellybeans there are. 623 00:46:10,040 --> 00:46:15,000 I asked 160 people and most were way off the mark. 624 00:46:15,000 --> 00:46:20,200 Everything from 400 right up to 50,000 beans. 625 00:46:20,200 --> 00:46:27,080 In fact only four people got anywhere near the correct answer of 4,510. 626 00:46:27,080 --> 00:46:36,840 Plus 1,500, plus 3,217, plus 83... . 627 00:46:36,840 --> 00:46:43,680 If I add all the answers together and take the average, I get the combined guess of the entire group. 628 00:46:43,680 --> 00:46:46,240 Plus, 4,000, plus 5,000, 629 00:46:46,240 --> 00:46:48,800 463, 630 00:46:48,800 --> 00:46:52,520 Plus 853, plus 1,000, 631 00:46:52,520 --> 00:46:55,400 plus 5,000... 632 00:46:55,400 --> 00:46:58,040 Which gives a grand total 633 00:46:58,040 --> 00:47:04,000 of 722,383.5. 634 00:47:04,000 --> 00:47:06,960 Somebody thought there was half a bean in there. 635 00:47:06,960 --> 00:47:13,320 Now there are 160 guesses made, so let's see how close they are collectively. 636 00:47:13,320 --> 00:47:16,600 Wow, that's extraordinary. 637 00:47:16,600 --> 00:47:20,440 You remember there were 4,510. 638 00:47:20,440 --> 00:47:26,680 The average guess to the nearest bean is 4,515. 639 00:47:26,680 --> 00:47:30,280 I thought it would be close, but I didn't think it would be THAT close. 640 00:47:30,280 --> 00:47:31,360 That is ridiculous. 641 00:47:31,360 --> 00:47:36,160 Though we had guesses that were all over the place, up in 30,000s right down in the 400s, 642 00:47:36,160 --> 00:47:40,600 collectively we get something which is just 0.1% away 643 00:47:40,600 --> 00:47:43,800 from the real number of beans in there. 644 00:47:43,800 --> 00:47:47,840 So as individuals the guesses are just that, guesses. 645 00:47:47,840 --> 00:47:52,240 But when you take them collectively they become something else entirely. 646 00:47:52,240 --> 00:47:57,800 5,000. 1,450. 9,200. 647 00:47:57,800 --> 00:48:01,440 What tends to happen is that more or less as many people 648 00:48:01,440 --> 00:48:05,720 will underestimate the number of jellybeans as overestimate it. 649 00:48:05,720 --> 00:48:08,600 1,763... 6,000. 650 00:48:08,600 --> 00:48:13,400 A few people will be way off the mark either way, but that doesn't matter. 651 00:48:13,400 --> 00:48:19,480 Provided you ask enough people, the errors should cancel each other out. 652 00:48:19,480 --> 00:48:24,400 1,000. 1,275. 700? 653 00:48:24,400 --> 00:48:28,640 The accuracy of the group is far greater than the individual. 654 00:48:28,640 --> 00:48:31,880 We call it "the wisdom of the crowd". 655 00:48:31,880 --> 00:48:40,880 160 people is a powerful tool for working out how many jellybeans there are in the jar. 656 00:48:40,880 --> 00:48:44,560 But imagine what you could do with a crowd of millions. 657 00:48:47,080 --> 00:48:50,640 That's exactly what they use here at Google. 658 00:48:52,160 --> 00:48:55,320 With access to over two billion web searches a day, 659 00:48:55,320 --> 00:49:00,240 Google have found a way of tapping into the wisdom of the biggest crowd on Earth. 660 00:49:00,240 --> 00:49:02,480 And by doing so, 661 00:49:02,480 --> 00:49:07,960 they've been able to reveal the forces that control our lives, 662 00:49:07,960 --> 00:49:11,000 and harness them to make predictions about us. 663 00:49:11,000 --> 00:49:14,280 'Think of the things that people search for on a daily basis. 664 00:49:14,280 --> 00:49:17,760 'Think of the things that YOU search for on a daily basis.' 665 00:49:17,760 --> 00:49:23,480 I searched for cities in Mexico and films in Hackney today. 666 00:49:23,480 --> 00:49:26,160 Lots of people may be searching for the similar... 667 00:49:26,160 --> 00:49:29,200 a similar thing, movies in Hackney, for example. 668 00:49:29,200 --> 00:49:33,040 And if you look at that query over the past three years, 669 00:49:33,040 --> 00:49:36,200 um, what the pattern of searches for that term looks like. 670 00:49:40,360 --> 00:49:43,600 Google had a hunch they could use all our searches 671 00:49:43,600 --> 00:49:46,080 to make predictions about our lives. 672 00:49:47,600 --> 00:49:51,720 They wanted to see if they could match the pattern of certain searches 673 00:49:51,720 --> 00:49:53,560 with events in the real world. 674 00:49:55,400 --> 00:49:59,720 Google began by seeing if they could predict outbreaks of flu. 675 00:50:01,440 --> 00:50:04,720 So flu has a nice seasonal pattern 676 00:50:04,720 --> 00:50:09,600 and because it has that pattern every year over many years, 677 00:50:09,600 --> 00:50:12,000 we're able to...to take that trend 678 00:50:12,000 --> 00:50:16,880 and say which search queries match that pattern. 679 00:50:16,880 --> 00:50:21,280 So we built a database that included over 50 million different search terms. 680 00:50:21,280 --> 00:50:22,800 50 million? 681 00:50:22,800 --> 00:50:24,440 Yes. Wow, yeah. 682 00:50:24,440 --> 00:50:27,520 We didn't only include things that may be related to flu. 683 00:50:27,520 --> 00:50:29,920 We included things like Britney Spears or... 684 00:50:29,920 --> 00:50:32,840 Everything people search for would be included. 685 00:50:35,000 --> 00:50:38,680 When Google looked back over the past five years of data, 686 00:50:38,680 --> 00:50:43,400 there were certain search terms whose popularity exactly matched 687 00:50:43,400 --> 00:50:45,240 the pattern of flu cases. 688 00:50:45,240 --> 00:50:49,600 So people were searching for things like "symptoms" 689 00:50:49,600 --> 00:50:52,880 or "medications" or "sore throat". 690 00:50:52,880 --> 00:50:56,720 There are other things like complications. 691 00:50:56,720 --> 00:51:01,440 So you're saying that the sort of number of search terms for flu-related things 692 00:51:01,440 --> 00:51:07,000 almost exactly mirrors the actual cases of flu that we see in the population? That's true. 693 00:51:07,000 --> 00:51:12,000 It is an indicator of flu activity just based on lots of people searching for these terms. 694 00:51:12,000 --> 00:51:14,600 We were amazed by this finding. 695 00:51:19,680 --> 00:51:22,680 As soon as they see this pattern of search terms 696 00:51:22,680 --> 00:51:26,000 Google can predict there will be an outbreak of flu. 697 00:51:26,000 --> 00:51:29,520 Often before people had even gone to the doctor. 698 00:51:31,920 --> 00:51:36,280 This is the extraordinary power of the Code. 699 00:51:38,520 --> 00:51:42,200 But it's just the tip of the iceberg. 700 00:51:42,200 --> 00:51:44,480 The searches we make can be used to predict 701 00:51:44,480 --> 00:51:46,000 where we'll go on holiday. 702 00:51:46,000 --> 00:51:48,720 What model of car we're going to buy. 703 00:51:48,720 --> 00:51:50,960 Or how we're going to vote, 704 00:51:50,960 --> 00:51:53,360 often before we know ourselves. 705 00:51:55,360 --> 00:51:59,160 It's even been possible to forecast the movement of the Stock Market 706 00:51:59,160 --> 00:52:02,400 from the number of negative words used on Twitter. 707 00:52:06,600 --> 00:52:12,440 Analysing such vast amounts of data, doesn't just allow us to make predictions. 708 00:52:12,440 --> 00:52:17,000 It can also tell us something fundamental about ourselves. 709 00:52:20,840 --> 00:52:26,440 You look out at a city like this and it looks like, you know, some arbitrary jumbled mess. 710 00:52:26,440 --> 00:52:30,480 Yet the city IS people. 711 00:52:30,480 --> 00:52:32,800 It's not the buildings and the streets. 712 00:52:32,800 --> 00:52:36,240 They're the stage upon which the real actors 713 00:52:36,240 --> 00:52:39,560 are playing out the story of civilisation. 714 00:52:42,640 --> 00:52:48,520 Geoffrey West is a physicist who's spent his life trying to see meaningful patterns in the universe. 715 00:52:48,520 --> 00:52:55,000 And how he's turned his attention to the dynamics of human life in cities. 716 00:52:59,360 --> 00:53:03,800 So you can see there's all kinds of infrastructure here. 717 00:53:03,800 --> 00:53:09,760 There's the obvious, the roads, the electrical lines, the sewer lines. 718 00:53:09,760 --> 00:53:13,680 They're an extraordinary network that is sustaining New York City. 719 00:53:13,680 --> 00:53:16,320 You know, coming at it as a physicist, 720 00:53:16,320 --> 00:53:21,720 I had this hunch that there is an underlying code to all this. 721 00:53:24,480 --> 00:53:29,480 West amassed data about cities all over the world. 722 00:53:29,480 --> 00:53:33,360 And the patterns he found mean that for any given population size, 723 00:53:33,360 --> 00:53:35,480 he can predict the amount of roads, 724 00:53:35,480 --> 00:53:40,160 electrical wiring or office space that city has. 725 00:53:43,000 --> 00:53:46,840 But he also discovered something much more surprising. 726 00:53:50,320 --> 00:53:56,080 One of the most interesting results we discovered was that, um... 727 00:53:56,080 --> 00:54:00,040 Wages scale in a very systematic way 728 00:54:00,040 --> 00:54:04,160 and the rule that came out was that if you doubled the size of the city, 729 00:54:04,160 --> 00:54:08,840 you get this marvellous 15% increase in the wages. 730 00:54:08,840 --> 00:54:13,440 If you live in a large city, you're going to earn more? Yes. 731 00:54:13,440 --> 00:54:15,680 So what, if there are two mathematicians 732 00:54:15,680 --> 00:54:19,280 in two different cities - one twice the size - doing the same job, 733 00:54:19,280 --> 00:54:24,080 one will have a bigger income? On the average, that is what the data say. 734 00:54:24,080 --> 00:54:26,960 Was that a surprise to you to see that? A huge surprise. 735 00:54:26,960 --> 00:54:29,880 I thought there was something wrong with the data. 736 00:54:29,880 --> 00:54:35,560 And then it was like, "Of course! That's why cities exist." 737 00:54:40,240 --> 00:54:43,800 Incredibly, it's not just people's salaries that increase. 738 00:54:43,800 --> 00:54:49,280 When a city doubles in size, every measure of social and economic activity 739 00:54:49,280 --> 00:54:52,000 goes up by 15% per person. 740 00:54:52,000 --> 00:54:56,200 That's 15% more restaurants to choose from. 741 00:54:56,200 --> 00:55:00,880 15% more art galleries to visit. 15% more shops to go to. 742 00:55:01,920 --> 00:55:05,400 In short, life gets 15% better. 743 00:55:11,120 --> 00:55:13,680 You know it looks like it's a magic formula 744 00:55:13,680 --> 00:55:17,280 that we as social human beings have discovered... 745 00:55:20,120 --> 00:55:25,720 ..this 15% bonus, so to speak, is, I believe, the reason 746 00:55:25,720 --> 00:55:28,080 that people are attracted to cities 747 00:55:28,080 --> 00:55:32,200 and why there has been this continuous migration 748 00:55:32,200 --> 00:55:35,760 from the countryside and into the cities. 749 00:55:35,760 --> 00:55:40,720 And at some deeper level, actually drive our civilisation. 750 00:55:43,120 --> 00:55:48,120 According to Geoffrey West, humankind has an ultimate number. 751 00:55:48,120 --> 00:55:51,360 It's this extra 15% 752 00:55:51,360 --> 00:55:53,280 or 1.15. 753 00:55:53,280 --> 00:55:58,600 He believes it's the most important driving force in humanity. 754 00:56:00,680 --> 00:56:03,680 This single number, 1.15, 755 00:56:03,680 --> 00:56:05,680 predicts our future. 756 00:56:06,960 --> 00:56:10,520 It will bring us together in ever expanding cities 757 00:56:10,520 --> 00:56:14,840 and shape our destiny for as long as human beings exist. 758 00:56:25,440 --> 00:56:29,160 Five hundred years ago, when faced with an eclipse, 759 00:56:29,160 --> 00:56:33,320 many of us would have believed it was the work of an angry god. 760 00:56:33,320 --> 00:56:35,920 But as we've unearthed the language of the Code, 761 00:56:35,920 --> 00:56:39,800 we've discovered that the apparent mysteries of our world 762 00:56:39,800 --> 00:56:43,240 can be understood without invoking the supernatural. 763 00:56:43,240 --> 00:56:46,360 And this for me is what's so remarkable. 764 00:56:46,360 --> 00:56:50,120 That despite the incredible complexity of the world we live in, 765 00:56:50,120 --> 00:56:54,640 it can all, ultimately, be explained by numbers. 766 00:56:57,360 --> 00:57:02,080 Just like the orbit of the planets, life too follows a pattern. 767 00:57:04,320 --> 00:57:07,920 And it can all be reduced to cause and effect. 768 00:57:11,040 --> 00:57:13,920 In the end, even the flip of a coin 769 00:57:13,920 --> 00:57:16,520 is determined by how fast it's spinning 770 00:57:16,520 --> 00:57:18,680 and how long it takes to hit the ground. 771 00:57:18,680 --> 00:57:23,640 The ultimate symbol of chance isn't random at all. 772 00:57:23,640 --> 00:57:26,080 It only appears that way. 773 00:57:28,760 --> 00:57:31,920 When we don't understand the Code, 774 00:57:31,920 --> 00:57:36,200 the only way we can make sense of our world is to make up stories. 775 00:57:37,880 --> 00:57:40,840 But the truth is far more extraordinary. 776 00:57:42,440 --> 00:57:46,480 Everything has mathematics at its heart. 777 00:57:47,520 --> 00:57:52,560 When everything is stripped away all that remains is the Code. 778 00:57:57,720 --> 00:58:05,520 Find clues to help you solve the Code's treasure hunt at... 779 00:58:05,520 --> 00:58:10,360 Plus get a free set of mathematical puzzles and a treasure hunt clue 780 00:58:10,360 --> 00:58:14,200 when you follow the links to the Open University. 781 00:58:14,200 --> 00:58:19,760 Or call 0845 366 8026. 782 00:58:32,720 --> 00:58:35,760 Subtitles by Red Bee Media Ltd 783 00:58:35,760 --> 00:58:38,800 E-mail subtitling@bbc.co.uk 68978

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