| Recorded at | April 04, 2014 |
|---|---|
| Event | TEDxBinghamtonUniversity |
| Duration (min:sec) | 16:52 |
| Video Type | TEDx Talk |
| Words per minute | 192.7 fast |
| Readability (FK) | 46.92 difficult |
| Speaker | Hannah Fry |
| Country | United Kingdom |
| Occupation | mathematician, Q13590141, orator, author, Q901, lecturer |
| Description | British mathematician and TV presenter (1984-) |
Official TED page for this talk
Synopsis
Finding the right mate is no cakewalk -- but is it even mathematically likely? In a charming talk, mathematician Hannah Fry shows patterns in how we look for love, and gives her top three tips (verified by math!) for finding that special someone.
| 1 | 00:03 | Today I want to talk to you about the mathematics of love. | ||
| 2 | 00:08 | Now, I think that we can all agree | ||
| 3 | 00:09 | that mathematicians are famously excellent at finding love. | ||
| 4 | 00:13 | (Laughter) | ||
| 5 | 00:14 | But it's not just because of our dashing personalities, | ||
| 6 | 00:17 | superior conversational skills and excellent pencil cases. | ||
| 7 | 00:23 | It's also because we've actually done an awful lot of work into the maths | ||
| 8 | 00:26 | of how to find the perfect partner. | ||
| 9 | 00:29 | Now, in my favorite paper on the subject, which is entitled, | ||
| 10 | 00:33 | "Why I Don't Have a Girlfriend" -- | ||
| 11 | 00:34 | (Laughter) | ||
| 12 | 00:36 | Peter Backus tries to rate his chances of finding love. | ||
| 13 | 00:40 | Now, Peter's not a very greedy man. | ||
| 14 | 00:42 | Of all of the available women in the UK, | ||
| 15 | 00:44 | all Peter's looking for is somebody who lives near him, | ||
| 16 | 00:48 | somebody in the right age range, | ||
| 17 | 00:49 | somebody with a university degree, | ||
| 18 | 00:52 | somebody he's likely to get on well with, | ||
| 19 | 00:54 | somebody who's likely to be attractive, | ||
| 20 | 00:56 | somebody who's likely to find him attractive. | ||
| 21 | 00:59 | (Laughter) | ||
| 22 | 01:02 | And comes up with an estimate of 26 women in the whole of the UK. | ||
| 23 | 01:07 | (Laughter) | ||
| 24 | 01:08 | It's not looking very good, is it Peter? | ||
| 25 | 01:10 | Now, just to put that into perspective, | ||
| 26 | 01:12 | that's about 400 times fewer than the best estimates | ||
| 27 | 01:15 | of how many intelligent extraterrestrial life forms there are. | ||
| 28 | 01:19 | And it also gives Peter a 1 in 285,000 chance | ||
| 29 | 01:24 | of bumping into any one of these special ladies | ||
| 30 | 01:26 | on a given night out. | ||
| 31 | 01:28 | I'd like to think that's why mathematicians | ||
| 32 | 01:30 | don't really bother going on nights out anymore. | ||
| 33 | 01:34 | The thing is that I personally don't subscribe | ||
| 34 | 01:36 | to such a pessimistic view. | ||
| 35 | 01:38 | Because I know, just as well as all of you do, | ||
| 36 | 01:40 | that love doesn't really work like that. | ||
| 37 | 01:43 | Human emotion isn't neatly ordered and rational and easily predictable. | ||
| 38 | 01:48 | But I also know that that doesn't mean | ||
| 39 | 01:50 | that mathematics hasn't got something that it can offer us, | ||
| 40 | 01:53 | because, love, as with most of life, is full of patterns | ||
| 41 | 01:57 | and mathematics is, ultimately, all about the study of patterns. | ||
| 42 | 02:02 | Patterns from predicting the weather to the fluctuations in the stock market, | ||
| 43 | 02:06 | to the movement of the planets or the growth of cities. | ||
| 44 | 02:09 | And if we're being honest, none of those things | ||
| 45 | 02:11 | are exactly neatly ordered and easily predictable, either. | ||
| 46 | 02:15 | Because I believe that mathematics is so powerful that it has the potential | ||
| 47 | 02:21 | to offer us a new way of looking at almost anything. | ||
| 48 | 02:24 | Even something as mysterious as love. | ||
| 49 | 02:27 | And so, to try to persuade you | ||
| 50 | 02:29 | of how totally amazing, excellent and relevant mathematics is, | ||
| 51 | 02:34 | I want to give you my top three mathematically verifiable tips for love. | ||
| 52 | 02:41 | (Laughter) | ||
| 53 | 02:43 | OK, so Top Tip #1: | ||
| 54 | 02:45 | How to win at online dating. | ||
| 55 | 02:49 | So my favorite online dating website is OkCupid, | ||
| 56 | 02:52 | not least because it was started by a group of mathematicians. | ||
| 57 | 02:56 | Now, because they're mathematicians, | ||
| 58 | 02:58 | they have been collecting data | ||
| 59 | 02:59 | on everybody who uses their site for almost a decade. | ||
| 60 | 03:03 | And they've been trying to search for patterns | ||
| 61 | 03:05 | in the way that we talk about ourselves | ||
| 62 | 03:07 | and the way that we interact with each other | ||
| 63 | 03:09 | on an online dating website. | ||
| 64 | 03:10 | And they've come up with some seriously interesting findings. | ||
| 65 | 03:13 | But my particular favorite | ||
| 66 | 03:15 | is that it turns out that on an online dating website, | ||
| 67 | 03:18 | how attractive you are does not dictate how popular you are, | ||
| 68 | 03:24 | and actually, having people think that you're ugly | ||
| 69 | 03:28 | can work to your advantage. | ||
| 70 | 03:30 | (Laughter) | ||
| 71 | 03:31 | Let me show you how this works. | ||
| 72 | 03:33 | In a thankfully voluntary section of OkCupid, | ||
| 73 | 03:37 | you are allowed to rate how attractive you think people are | ||
| 74 | 03:40 | on a scale between one and five. | ||
| 75 | 03:42 | Now, if we compare this score, the average score, | ||
| 76 | 03:45 | to how many messages a selection of people receive, | ||
| 77 | 03:49 | you can begin to get a sense | ||
| 78 | 03:50 | of how attractiveness links to popularity on an online dating website. | ||
| 79 | 03:55 | This is the graph the OkCupid guys have come up with. | ||
| 80 | 03:58 | And the important thing to notice is that it's not totally true | ||
| 81 | 04:01 | that the more attractive you are, the more messages you get. | ||
| 82 | 04:04 | But the question arises then of what is it about people up here | ||
| 83 | 04:08 | who are so much more popular than people down here, | ||
| 84 | 04:12 | even though they have the same score of attractiveness? | ||
| 85 | 04:15 | And the reason why is that it's not just straightforward looks that are important. | ||
| 86 | 04:19 | So let me try to illustrate their findings with an example. | ||
| 87 | 04:22 | So if you take someone like Portia de Rossi, for example, | ||
| 88 | 04:26 | everybody agrees that Portia de Rossi is a very beautiful woman. | ||
| 89 | 04:30 | Nobody thinks that she's ugly, but she's not a supermodel, either. | ||
| 90 | 04:34 | If you compare Portia de Rossi to someone like Sarah Jessica Parker, | ||
| 91 | 04:39 | now, a lot of people, myself included, I should say, | ||
| 92 | 04:43 | think that Sarah Jessica Parker is seriously fabulous | ||
| 93 | 04:47 | and possibly one of the most beautiful creatures | ||
| 94 | 04:50 | to have ever have walked on the face of the Earth. | ||
| 95 | 04:52 | But some other people, i.e., most of the Internet ... | ||
| 96 | 04:57 | (Laughter) | ||
| 97 | 04:59 | seem to think that she looks a bit like a horse. | ||
| 98 | 05:01 | (Laughter) | ||
| 99 | 05:04 | Now, I think that if you ask people how attractive they thought | ||
| 100 | 05:08 | Jessica Parker or Portia de Rossi were, | ||
| 101 | 05:10 | and you ask them to give them a score between one and five | ||
| 102 | 05:13 | I reckon that they'd average out to have roughly the same score. | ||
| 103 | 05:16 | But the way that people would vote would be very different. | ||
| 104 | 05:19 | So Portia's scores would all be clustered around the four | ||
| 105 | 05:22 | because everybody agrees that she's very beautiful, | ||
| 106 | 05:24 | whereas Sarah Jessica Parker completely divides opinion. | ||
| 107 | 05:27 | There'd be a huge spread in her scores. | ||
| 108 | 05:29 | And actually it's this spread that counts. | ||
| 109 | 05:31 | It's this spread that makes you more popular | ||
| 110 | 05:33 | on an online Internet dating website. | ||
| 111 | 05:36 | So what that means then | ||
| 112 | 05:37 | is that if some people think that you're attractive, | ||
| 113 | 05:39 | you're actually better off | ||
| 114 | 05:41 | having some other people think that you're a massive minger. | ||
| 115 | 05:46 | That's much better than everybody just thinking | ||
| 116 | 05:49 | that you're the cute girl next door. | ||
| 117 | 05:51 | Now, I think this begins to make a bit more sense | ||
| 118 | 05:53 | when you think in terms of the people who are sending these messages. | ||
| 119 | 05:56 | So let's say that you think somebody's attractive, | ||
| 120 | 05:59 | but you suspect that other people won't necessarily be that interested. | ||
| 121 | 06:03 | That means there's less competition for you | ||
| 122 | 06:05 | and it's an extra incentive for you to get in touch. | ||
| 123 | 06:08 | Whereas compare that to if you think somebody is attractive | ||
| 124 | 06:11 | but you suspect that everybody is going to think they're attractive. | ||
| 125 | 06:14 | Well, why would you bother humiliating yourself, let's be honest? | ||
| 126 | 06:18 | But here's where the really interesting part comes. | ||
| 127 | 06:20 | Because when people choose the pictures that they use on an online dating website, | ||
| 128 | 06:24 | they often try to minimize the things | ||
| 129 | 06:26 | that they think some people will find unattractive. | ||
| 130 | 06:30 | The classic example is people who are, perhaps, a little bit overweight | ||
| 131 | 06:34 | deliberately choosing a very cropped photo, | ||
| 132 | 06:36 | (Laughter) | ||
| 133 | 06:38 | or bald men, for example, | ||
| 134 | 06:39 | deliberately choosing pictures where they're wearing hats. | ||
| 135 | 06:42 | But actually this is the opposite of what you should do | ||
| 136 | 06:45 | if you want to be successful. | ||
| 137 | 06:46 | You should really, instead, | ||
| 138 | 06:48 | play up to whatever it is that makes you different, | ||
| 139 | 06:51 | even if you think that some people will find it unattractive. | ||
| 140 | 06:55 | Because the people who fancy you are just going to fancy you anyway, | ||
| 141 | 06:58 | and the unimportant losers who don't, well, they only play up to your advantage. | ||
| 142 | 07:03 | OK, Top Tip #2: How to pick the perfect partner. | ||
| 143 | 07:05 | So let's imagine then that you're a roaring success | ||
| 144 | 07:08 | on the dating scene. | ||
| 145 | 07:10 | But the question arises of how do you then convert that success | ||
| 146 | 07:14 | into longer-term happiness, | ||
| 147 | 07:17 | and in particular, how do you decide when is the right time to settle down? | ||
| 148 | 07:22 | Now generally, it's not advisable to just cash in | ||
| 149 | 07:25 | and marry the first person who comes along and shows you any interest at all. | ||
| 150 | 07:29 | But, equally, you don't really want to leave it too long | ||
| 151 | 07:32 | if you want to maximize your chance of long-term happiness. | ||
| 152 | 07:35 | As my favorite author, Jane Austen, puts it, | ||
| 153 | 07:38 | "An unmarried woman of seven and twenty | ||
| 154 | 07:41 | can never hope to feel or inspire affection again." | ||
| 155 | 07:44 | (Laughter) | ||
| 156 | 07:47 | Thanks a lot, Jane. | ||
| 157 | 07:48 | What do you know about love? | ||
| 158 | 07:49 | (Laughter) | ||
| 159 | 07:50 | So the question is then, | ||
| 160 | 07:52 | how do you know when is the right time to settle down, | ||
| 161 | 07:55 | given all the people that you can date in your lifetime? | ||
| 162 | 07:58 | Thankfully, there's a rather delicious bit of mathematics that we can use | ||
| 163 | 08:01 | to help us out here, called optimal stopping theory. | ||
| 164 | 08:04 | So let's imagine, then, | ||
| 165 | 08:06 | that you start dating when you're 15 | ||
| 166 | 08:08 | and ideally, you'd like to be married by the time that you're 35. | ||
| 167 | 08:12 | And there's a number of people | ||
| 168 | 08:14 | that you could potentially date across your lifetime, | ||
| 169 | 08:16 | and they'll be at varying levels of goodness. | ||
| 170 | 08:18 | Now the rules are that once you cash in and get married, | ||
| 171 | 08:21 | you can't look ahead to see what you could have had, | ||
| 172 | 08:23 | and equally, you can't go back and change your mind. | ||
| 173 | 08:26 | In my experience at least, | ||
| 174 | 08:28 | I find that typically people don't much like being recalled | ||
| 175 | 08:30 | years after being passed up for somebody else, or that's just me. | ||
| 176 | 08:36 | So the math says then that what you should do | ||
| 177 | 08:39 | in the first 37 percent of your dating window, | ||
| 178 | 08:42 | you should just reject everybody as serious marriage potential. | ||
| 179 | 08:46 | (Laughter) | ||
| 180 | 08:48 | And then, you should pick the next person that comes along | ||
| 181 | 08:52 | that is better than everybody that you've seen before. | ||
| 182 | 08:55 | So here's the example. | ||
| 183 | 08:56 | Now if you do this, it can be mathematically proven, in fact, | ||
| 184 | 08:59 | that this is the best possible way | ||
| 185 | 09:01 | of maximizing your chances of finding the perfect partner. | ||
| 186 | 09:06 | Now unfortunately, I have to tell you that this method does come with some risks. | ||
| 187 | 09:11 | For instance, imagine if your perfect partner appeared | ||
| 188 | 09:16 | during your first 37 percent. | ||
| 189 | 09:19 | Now, unfortunately, you'd have to reject them. | ||
| 190 | 09:21 | (Laughter) | ||
| 191 | 09:25 | Now, if you're following the maths, | ||
| 192 | 09:27 | I'm afraid no one else comes along | ||
| 193 | 09:28 | that's better than anyone you've seen before, | ||
| 194 | 09:31 | so you have to go on rejecting everyone and die alone. | ||
| 195 | 09:35 | (Laughter) | ||
| 196 | 09:37 | Probably surrounded by cats ... | ||
| 197 | 09:39 | (Laughter) | ||
| 198 | 09:40 | nibbling at your remains. | ||
| 199 | 09:42 | OK, another risk is, let's imagine, instead, | ||
| 200 | 09:46 | that the first people that you dated in your first 37 percent | ||
| 201 | 09:49 | are just incredibly dull, boring, terrible people. | ||
| 202 | 09:53 | That's OK, because you're in your rejection phase, | ||
| 203 | 09:56 | so that's fine, you can reject them. | ||
| 204 | 09:58 | But then imagine the next person to come along | ||
| 205 | 10:01 | is just marginally less boring, dull and terrible ... | ||
| 206 | 10:04 | (Laughter) | ||
| 207 | 10:05 | than everybody that you've seen before. | ||
| 208 | 10:07 | Now, if you are following the maths, I'm afraid you have to marry them ... | ||
| 209 | 10:11 | (Laughter) | ||
| 210 | 10:12 | and end up in a relationship which is, frankly, suboptimal. | ||
| 211 | 10:15 | Sorry about that. | ||
| 212 | 10:16 | But I do think that there's an opportunity here for Hallmark to cash in on | ||
| 213 | 10:20 | and really cater for this market. | ||
| 214 | 10:21 | A Valentine's Day card like this. | ||
| 215 | 10:23 | (Laughter) | ||
| 216 | 10:24 | "My darling husband, you are marginally less terrible | ||
| 217 | 10:27 | than the first 37 percent of people I dated." | ||
| 218 | 10:29 | (Laughter) | ||
| 219 | 10:31 | It's actually more romantic than I normally manage. | ||
| 220 | 10:34 | (Laughter) | ||
| 221 | 10:36 | OK, so this method doesn't give you a 100 percent success rate, | ||
| 222 | 10:40 | but there's no other possible strategy that can do any better. | ||
| 223 | 10:44 | And actually, in the wild, there are certain types of fish | ||
| 224 | 10:47 | which follow and employ this exact strategy. | ||
| 225 | 10:50 | So they reject every possible suitor that turns up | ||
| 226 | 10:53 | in the first 37 percent of the mating season, | ||
| 227 | 10:56 | and then they pick the next fish that comes along after that window | ||
| 228 | 10:59 | that's, I don't know, bigger and burlier | ||
| 229 | 11:02 | than all of the fish that they've seen before. | ||
| 230 | 11:04 | I also think that subconsciously, humans, we do sort of do this anyway. | ||
| 231 | 11:09 | We give ourselves a little bit of time to play the field, | ||
| 232 | 11:12 | get a feel for the marketplace or whatever when we're young. | ||
| 233 | 11:16 | And then we only start looking seriously at potential marriage candidates | ||
| 234 | 11:20 | once we hit our mid-to-late 20s. | ||
| 235 | 11:22 | I think this is conclusive proof, if ever it were needed, | ||
| 236 | 11:26 | that everybody's brains are prewired to be just a little bit mathematical. | ||
| 237 | 11:30 | OK, so that was Top Tip #2. | ||
| 238 | 11:32 | Now, Top Tip #3: How to avoid divorce. | ||
| 239 | 11:36 | OK, so let's imagine then that you picked your perfect partner | ||
| 240 | 11:39 | and you're settling into a lifelong relationship with them. | ||
| 241 | 11:43 | Now, I like to think that everybody would ideally like to avoid divorce, | ||
| 242 | 11:47 | apart from, I don't know, Piers Morgan's wife, maybe? | ||
| 243 | 11:51 | (Laughter) | ||
| 244 | 11:53 | But it's a sad fact of modern life | ||
| 245 | 11:55 | that one in two marriages in the States ends in divorce, | ||
| 246 | 11:58 | with the rest of the world not being far behind. | ||
| 247 | 12:02 | Now, you can be forgiven, perhaps | ||
| 248 | 12:04 | for thinking that the arguments that precede a marital breakup | ||
| 249 | 12:08 | are not an ideal candidate for mathematical investigation. | ||
| 250 | 12:12 | For one thing, it's very hard to know | ||
| 251 | 12:14 | what you should be measuring or what you should be quantifying. | ||
| 252 | 12:17 | But this didn't stop a psychologist, John Gottman, who did exactly that. | ||
| 253 | 12:23 | Gottman observed hundreds of couples having a conversation | ||
| 254 | 12:28 | and recorded, well, everything you can think of. | ||
| 255 | 12:31 | So he recorded what was said in the conversation, | ||
| 256 | 12:33 | he recorded their skin conductivity, | ||
| 257 | 12:35 | he recorded their facial expressions, | ||
| 258 | 12:37 | their heart rates, their blood pressure, | ||
| 259 | 12:39 | basically everything apart from whether or not the wife was actually always right, | ||
| 260 | 12:46 | which incidentally she totally is. | ||
| 261 | 12:49 | But what Gottman and his team found | ||
| 262 | 12:52 | was that one of the most important predictors | ||
| 263 | 12:55 | for whether or not a couple is going to get divorced | ||
| 264 | 12:57 | was how positive or negative each partner was being in the conversation. | ||
| 265 | 13:02 | Now, couples that were very low-risk | ||
| 266 | 13:04 | scored a lot more positive points on Gottman's scale than negative. | ||
| 267 | 13:08 | Whereas bad relationships, | ||
| 268 | 13:11 | by which I mean, probably going to get divorced, | ||
| 269 | 13:14 | they found themselves getting into a spiral of negativity. | ||
| 270 | 13:18 | Now just by using these very simple ideas, | ||
| 271 | 13:20 | Gottman and his group were able to predict | ||
| 272 | 13:23 | whether a given couple was going to get divorced | ||
| 273 | 13:26 | with a 90 percent accuracy. | ||
| 274 | 13:29 | But it wasn't until he teamed up with a mathematician, James Murray, | ||
| 275 | 13:32 | that they really started to understand | ||
| 276 | 13:34 | what causes these negativity spirals and how they occur. | ||
| 277 | 13:38 | And the results that they found, | ||
| 278 | 13:40 | I think, are just incredibly impressively simple and interesting. | ||
| 279 | 13:45 | So these equations predict how the wife or husband is going to respond | ||
| 280 | 13:49 | in their next turn of the conversation, | ||
| 281 | 13:51 | how positive or negative they're going to be. | ||
| 282 | 13:53 | And these equations depend on | ||
| 283 | 13:55 | the mood of the person when they're on their own, | ||
| 284 | 13:57 | the mood of the person when they're with their partner, | ||
| 285 | 14:00 | but most importantly, they depend on | ||
| 286 | 14:01 | how much the husband and wife influence one another. | ||
| 287 | 14:05 | Now, I think it's important to point out at this stage, | ||
| 288 | 14:07 | that these exact equations have also been shown | ||
| 289 | 14:11 | to be perfectly able at describing | ||
| 290 | 14:13 | what happens between two countries in an arms race. | ||
| 291 | 14:18 | (Laughter) | ||
| 292 | 14:21 | So that an arguing couple spiraling into negativity | ||
| 293 | 14:24 | and teetering on the brink of divorce | ||
| 294 | 14:26 | is actually mathematically equivalent to the beginning of a nuclear war. | ||
| 295 | 14:31 | (Laughter) | ||
| 296 | 14:34 | But the really important term in this equation | ||
| 297 | 14:36 | is the influence that people have on one another, | ||
| 298 | 14:38 | and in particular, something called "the negativity threshold." | ||
| 299 | 14:42 | Now, the negativity threshold, | ||
| 300 | 14:43 | you can think of as how annoying the husband can be | ||
| 301 | 14:48 | before the wife starts to get really pissed off, and vice versa. | ||
| 302 | 14:52 | Now, I always thought that good marriages were about compromise and understanding | ||
| 303 | 14:57 | and allowing the person to have the space to be themselves. | ||
| 304 | 15:00 | So I would have thought that perhaps the most successful relationships | ||
| 305 | 15:03 | were ones where there was a really high negativity threshold. | ||
| 306 | 15:06 | Where couples let things go | ||
| 307 | 15:08 | and only brought things up if they really were a big deal. | ||
| 308 | 15:11 | But actually, the mathematics and subsequent findings by the team | ||
| 309 | 15:15 | have shown the exact opposite is true. | ||
| 310 | 15:18 | The best couples, or the most successful couples, | ||
| 311 | 15:21 | are the ones with a really low negativity threshold. | ||
| 312 | 15:24 | These are the couples that don't let anything go unnoticed | ||
| 313 | 15:28 | and allow each other some room to complain. | ||
| 314 | 15:31 | These are the couples that are continually trying to repair their own relationship, | ||
| 315 | 15:36 | that have a much more positive outlook on their marriage. | ||
| 316 | 15:39 | Couples that don't let things go | ||
| 317 | 15:41 | and couples that don't let trivial things end up being a really big deal. | ||
| 318 | 15:47 | Now of course, it takes a bit more than just a low negativity threshold | ||
| 319 | 15:52 | and not compromising to have a successful relationship. | ||
| 320 | 15:57 | But I think that it's quite interesting | ||
| 321 | 15:59 | to know that there is really mathematical evidence | ||
| 322 | 16:02 | to say that you should never let the sun go down on your anger. | ||
| 323 | 16:05 | So those are my top three tips | ||
| 324 | 16:07 | of how maths can help you with love and relationships. | ||
| 325 | 16:10 | But I hope, that aside from their use as tips, | ||
| 326 | 16:12 | they also give you a little bit of insight into the power of mathematics. | ||
| 327 | 16:16 | Because for me, equations and symbols aren't just a thing. | ||
| 328 | 16:21 | They're a voice that speaks out about the incredible richness of nature | ||
| 329 | 16:26 | and the startling simplicity | ||
| 330 | 16:27 | in the patterns that twist and turn and warp and evolve all around us, | ||
| 331 | 16:32 | from how the world works to how we behave. | ||
| 332 | 16:35 | So I hope that perhaps, for just a couple of you, | ||
| 333 | 16:37 | a little bit of insight into the mathematics of love | ||
| 334 | 16:40 | can persuade you to have a little bit more love for mathematics. | ||
| 335 | 16:43 | Thank you. | ||
| 336 | 16:44 | (Applause) |