Recorded at | April 04, 2014 |
---|---|
Event | TEDxBinghamtonUniversity |
Duration (min:sec) | 16:48 |
Video Type | TEDx Talk |
Words per minute | 193.37 fast |
Readability (FK) | 46.92 difficult |
Speaker | Hannah Fry |
Country | United Kingdom |
Occupation | mathematician, presenter, orator, author, scientist, lecturer |
Description | British mathematician and TV presenter |
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 | 00:12 | Today I want to talk to you about the mathematics of love. | ||
2 | 00:17 | Now, I think that we can all agree | ||
3 | 00:18 | that mathematicians are famously excellent at finding love. | ||
4 | 00:22 | (Laughter) | ||
5 | 00:23 | But it's not just because of our dashing personalities, | ||
6 | 00:26 | superior conversational skills and excellent pencil cases. | ||
7 | 00:32 | It's also because we've actually done an awful lot of work into the maths | ||
8 | 00:35 | of how to find the perfect partner. | ||
9 | 00:38 | Now, in my favorite paper on the subject, which is entitled, | ||
10 | 00:42 | "Why I Don't Have a Girlfriend" -- | ||
11 | 00:43 | (Laughter) | ||
12 | 00:45 | Peter Backus tries to rate his chances of finding love. | ||
13 | 00:49 | Now, Peter's not a very greedy man. | ||
14 | 00:51 | Of all of the available women in the UK, | ||
15 | 00:53 | all Peter's looking for is somebody who lives near him, | ||
16 | 00:57 | somebody in the right age range, | ||
17 | 00:58 | somebody with a university degree, | ||
18 | 01:01 | somebody he's likely to get on well with, | ||
19 | 01:03 | somebody who's likely to be attractive, | ||
20 | 01:05 | somebody who's likely to find him attractive. | ||
21 | 01:08 | (Laughter) | ||
22 | 01:11 | And comes up with an estimate of 26 women in the whole of the UK. | ||
23 | 01:16 | (Laughter) | ||
24 | 01:17 | It's not looking very good, is it Peter? | ||
25 | 01:19 | Now, just to put that into perspective, | ||
26 | 01:21 | that's about 400 times fewer than the best estimates | ||
27 | 01:24 | of how many intelligent extraterrestrial life forms there are. | ||
28 | 01:28 | And it also gives Peter a 1 in 285,000 chance | ||
29 | 01:33 | of bumping into any one of these special ladies | ||
30 | 01:35 | on a given night out. | ||
31 | 01:37 | I'd like to think that's why mathematicians | ||
32 | 01:39 | don't really bother going on nights out anymore. | ||
33 | 01:43 | The thing is that I personally don't subscribe | ||
34 | 01:45 | to such a pessimistic view. | ||
35 | 01:47 | Because I know, just as well as all of you do, | ||
36 | 01:49 | that love doesn't really work like that. | ||
37 | 01:52 | Human emotion isn't neatly ordered and rational and easily predictable. | ||
38 | 01:57 | But I also know that that doesn't mean | ||
39 | 01:59 | that mathematics hasn't got something that it can offer us, | ||
40 | 02:02 | because, love, as with most of life, is full of patterns | ||
41 | 02:06 | and mathematics is, ultimately, all about the study of patterns. | ||
42 | 02:11 | Patterns from predicting the weather to the fluctuations in the stock market, | ||
43 | 02:15 | to the movement of the planets or the growth of cities. | ||
44 | 02:18 | And if we're being honest, none of those things | ||
45 | 02:20 | are exactly neatly ordered and easily predictable, either. | ||
46 | 02:24 | Because I believe that mathematics is so powerful that it has the potential | ||
47 | 02:30 | to offer us a new way of looking at almost anything. | ||
48 | 02:33 | Even something as mysterious as love. | ||
49 | 02:36 | And so, to try to persuade you | ||
50 | 02:38 | of how totally amazing, excellent and relevant mathematics is, | ||
51 | 02:43 | I want to give you my top three mathematically verifiable tips for love. | ||
52 | 02:50 | (Laughter) | ||
53 | 02:52 | OK, so Top Tip #1: | ||
54 | 02:54 | How to win at online dating. | ||
55 | 02:58 | So my favorite online dating website is OkCupid, | ||
56 | 03:01 | not least because it was started by a group of mathematicians. | ||
57 | 03:05 | Now, because they're mathematicians, | ||
58 | 03:07 | they have been collecting data | ||
59 | 03:08 | on everybody who uses their site for almost a decade. | ||
60 | 03:12 | And they've been trying to search for patterns | ||
61 | 03:14 | in the way that we talk about ourselves | ||
62 | 03:16 | and the way that we interact with each other | ||
63 | 03:18 | on an online dating website. | ||
64 | 03:19 | And they've come up with some seriously interesting findings. | ||
65 | 03:22 | But my particular favorite | ||
66 | 03:24 | is that it turns out that on an online dating website, | ||
67 | 03:27 | how attractive you are does not dictate how popular you are, | ||
68 | 03:33 | and actually, having people think that you're ugly | ||
69 | 03:37 | can work to your advantage. | ||
70 | 03:39 | (Laughter) | ||
71 | 03:40 | Let me show you how this works. | ||
72 | 03:42 | In a thankfully voluntary section of OkCupid, | ||
73 | 03:46 | you are allowed to rate how attractive you think people are | ||
74 | 03:49 | on a scale between one and five. | ||
75 | 03:51 | Now, if we compare this score, the average score, | ||
76 | 03:54 | to how many messages a selection of people receive, | ||
77 | 03:58 | you can begin to get a sense | ||
78 | 03:59 | of how attractiveness links to popularity on an online dating website. | ||
79 | 04:04 | This is the graph the OkCupid guys have come up with. | ||
80 | 04:07 | And the important thing to notice is that it's not totally true | ||
81 | 04:10 | that the more attractive you are, the more messages you get. | ||
82 | 04:13 | But the question arises then of what is it about people up here | ||
83 | 04:17 | who are so much more popular than people down here, | ||
84 | 04:21 | even though they have the same score of attractiveness? | ||
85 | 04:24 | And the reason why is that it's not just straightforward looks that are important. | ||
86 | 04:28 | So let me try to illustrate their findings with an example. | ||
87 | 04:31 | So if you take someone like Portia de Rossi, for example, | ||
88 | 04:35 | everybody agrees that Portia de Rossi is a very beautiful woman. | ||
89 | 04:39 | Nobody thinks that she's ugly, but she's not a supermodel, either. | ||
90 | 04:43 | If you compare Portia de Rossi to someone like Sarah Jessica Parker, | ||
91 | 04:48 | now, a lot of people, myself included, I should say, | ||
92 | 04:52 | think that Sarah Jessica Parker is seriously fabulous | ||
93 | 04:56 | and possibly one of the most beautiful creatures | ||
94 | 04:59 | to have ever have walked on the face of the Earth. | ||
95 | 05:01 | But some other people, i.e., most of the Internet ... | ||
96 | 05:06 | (Laughter) | ||
97 | 05:08 | seem to think that she looks a bit like a horse. | ||
98 | 05:10 | (Laughter) | ||
99 | 05:13 | Now, I think that if you ask people how attractive they thought | ||
100 | 05:17 | Jessica Parker or Portia de Rossi were, | ||
101 | 05:19 | and you ask them to give them a score between one and five | ||
102 | 05:22 | I reckon that they'd average out to have roughly the same score. | ||
103 | 05:25 | But the way that people would vote would be very different. | ||
104 | 05:28 | So Portia's scores would all be clustered around the four | ||
105 | 05:31 | because everybody agrees that she's very beautiful, | ||
106 | 05:33 | whereas Sarah Jessica Parker completely divides opinion. | ||
107 | 05:36 | There'd be a huge spread in her scores. | ||
108 | 05:38 | And actually it's this spread that counts. | ||
109 | 05:40 | It's this spread that makes you more popular | ||
110 | 05:42 | on an online Internet dating website. | ||
111 | 05:45 | So what that means then | ||
112 | 05:46 | is that if some people think that you're attractive, | ||
113 | 05:48 | you're actually better off | ||
114 | 05:50 | having some other people think that you're a massive minger. | ||
115 | 05:55 | That's much better than everybody just thinking | ||
116 | 05:58 | that you're the cute girl next door. | ||
117 | 06:00 | Now, I think this begins to make a bit more sense | ||
118 | 06:02 | when you think in terms of the people who are sending these messages. | ||
119 | 06:05 | So let's say that you think somebody's attractive, | ||
120 | 06:08 | but you suspect that other people won't necessarily be that interested. | ||
121 | 06:12 | That means there's less competition for you | ||
122 | 06:14 | and it's an extra incentive for you to get in touch. | ||
123 | 06:17 | Whereas compare that to if you think somebody is attractive | ||
124 | 06:20 | but you suspect that everybody is going to think they're attractive. | ||
125 | 06:23 | Well, why would you bother humiliating yourself, let's be honest? | ||
126 | 06:27 | But here's where the really interesting part comes. | ||
127 | 06:29 | Because when people choose the pictures that they use on an online dating website, | ||
128 | 06:33 | they often try to minimize the things | ||
129 | 06:35 | that they think some people will find unattractive. | ||
130 | 06:39 | The classic example is people who are, perhaps, a little bit overweight | ||
131 | 06:43 | deliberately choosing a very cropped photo, | ||
132 | 06:45 | (Laughter) | ||
133 | 06:47 | or bald men, for example, | ||
134 | 06:48 | deliberately choosing pictures where they're wearing hats. | ||
135 | 06:51 | But actually this is the opposite of what you should do | ||
136 | 06:54 | if you want to be successful. | ||
137 | 06:55 | You should really, instead, | ||
138 | 06:57 | play up to whatever it is that makes you different, | ||
139 | 07:00 | even if you think that some people will find it unattractive. | ||
140 | 07:04 | Because the people who fancy you are just going to fancy you anyway, | ||
141 | 07:07 | and the unimportant losers who don't, well, they only play up to your advantage. | ||
142 | 07:12 | OK, Top Tip #2: How to pick the perfect partner. | ||
143 | 07:14 | So let's imagine then that you're a roaring success | ||
144 | 07:17 | on the dating scene. | ||
145 | 07:19 | But the question arises of how do you then convert that success | ||
146 | 07:23 | into longer-term happiness, | ||
147 | 07:26 | and in particular, how do you decide when is the right time to settle down? | ||
148 | 07:31 | Now generally, it's not advisable to just cash in | ||
149 | 07:34 | and marry the first person who comes along and shows you any interest at all. | ||
150 | 07:38 | But, equally, you don't really want to leave it too long | ||
151 | 07:41 | if you want to maximize your chance of long-term happiness. | ||
152 | 07:44 | As my favorite author, Jane Austen, puts it, | ||
153 | 07:47 | "An unmarried woman of seven and twenty | ||
154 | 07:50 | can never hope to feel or inspire affection again." | ||
155 | 07:53 | (Laughter) | ||
156 | 07:56 | Thanks a lot, Jane. | ||
157 | 07:57 | What do you know about love? | ||
158 | 07:58 | (Laughter) | ||
159 | 07:59 | So the question is then, | ||
160 | 08:01 | how do you know when is the right time to settle down, | ||
161 | 08:04 | given all the people that you can date in your lifetime? | ||
162 | 08:07 | Thankfully, there's a rather delicious bit of mathematics that we can use | ||
163 | 08:10 | to help us out here, called optimal stopping theory. | ||
164 | 08:13 | So let's imagine, then, | ||
165 | 08:15 | that you start dating when you're 15 | ||
166 | 08:17 | and ideally, you'd like to be married by the time that you're 35. | ||
167 | 08:21 | And there's a number of people | ||
168 | 08:23 | that you could potentially date across your lifetime, | ||
169 | 08:25 | and they'll be at varying levels of goodness. | ||
170 | 08:27 | Now the rules are that once you cash in and get married, | ||
171 | 08:30 | you can't look ahead to see what you could have had, | ||
172 | 08:32 | and equally, you can't go back and change your mind. | ||
173 | 08:35 | In my experience at least, | ||
174 | 08:37 | I find that typically people don't much like being recalled | ||
175 | 08:39 | years after being passed up for somebody else, or that's just me. | ||
176 | 08:45 | So the math says then that what you should do | ||
177 | 08:48 | in the first 37 percent of your dating window, | ||
178 | 08:51 | you should just reject everybody as serious marriage potential. | ||
179 | 08:55 | (Laughter) | ||
180 | 08:57 | And then, you should pick the next person that comes along | ||
181 | 09:01 | that is better than everybody that you've seen before. | ||
182 | 09:04 | So here's the example. | ||
183 | 09:05 | Now if you do this, it can be mathematically proven, in fact, | ||
184 | 09:08 | that this is the best possible way | ||
185 | 09:10 | of maximizing your chances of finding the perfect partner. | ||
186 | 09:15 | Now unfortunately, I have to tell you that this method does come with some risks. | ||
187 | 09:20 | For instance, imagine if your perfect partner appeared | ||
188 | 09:25 | during your first 37 percent. | ||
189 | 09:28 | Now, unfortunately, you'd have to reject them. | ||
190 | 09:30 | (Laughter) | ||
191 | 09:34 | Now, if you're following the maths, | ||
192 | 09:36 | I'm afraid no one else comes along | ||
193 | 09:37 | that's better than anyone you've seen before, | ||
194 | 09:40 | so you have to go on rejecting everyone and die alone. | ||
195 | 09:44 | (Laughter) | ||
196 | 09:46 | Probably surrounded by cats ... | ||
197 | 09:48 | (Laughter) | ||
198 | 09:49 | nibbling at your remains. | ||
199 | 09:51 | OK, another risk is, let's imagine, instead, | ||
200 | 09:55 | that the first people that you dated in your first 37 percent | ||
201 | 09:58 | are just incredibly dull, boring, terrible people. | ||
202 | 10:02 | That's OK, because you're in your rejection phase, | ||
203 | 10:05 | so that's fine, you can reject them. | ||
204 | 10:07 | But then imagine the next person to come along | ||
205 | 10:10 | is just marginally less boring, dull and terrible ... | ||
206 | 10:13 | (Laughter) | ||
207 | 10:14 | than everybody that you've seen before. | ||
208 | 10:16 | Now, if you are following the maths, I'm afraid you have to marry them ... | ||
209 | 10:20 | (Laughter) | ||
210 | 10:21 | and end up in a relationship which is, frankly, suboptimal. | ||
211 | 10:24 | Sorry about that. | ||
212 | 10:25 | But I do think that there's an opportunity here for Hallmark to cash in on | ||
213 | 10:29 | and really cater for this market. | ||
214 | 10:30 | A Valentine's Day card like this. | ||
215 | 10:32 | (Laughter) | ||
216 | 10:33 | "My darling husband, you are marginally less terrible | ||
217 | 10:36 | than the first 37 percent of people I dated." | ||
218 | 10:38 | (Laughter) | ||
219 | 10:40 | It's actually more romantic than I normally manage. | ||
220 | 10:43 | (Laughter) | ||
221 | 10:45 | OK, so this method doesn't give you a 100 percent success rate, | ||
222 | 10:49 | but there's no other possible strategy that can do any better. | ||
223 | 10:53 | And actually, in the wild, there are certain types of fish | ||
224 | 10:56 | which follow and employ this exact strategy. | ||
225 | 10:59 | So they reject every possible suitor that turns up | ||
226 | 11:02 | in the first 37 percent of the mating season, | ||
227 | 11:05 | and then they pick the next fish that comes along after that window | ||
228 | 11:08 | that's, I don't know, bigger and burlier | ||
229 | 11:11 | than all of the fish that they've seen before. | ||
230 | 11:13 | I also think that subconsciously, humans, we do sort of do this anyway. | ||
231 | 11:18 | We give ourselves a little bit of time to play the field, | ||
232 | 11:21 | get a feel for the marketplace or whatever when we're young. | ||
233 | 11:25 | And then we only start looking seriously at potential marriage candidates | ||
234 | 11:29 | once we hit our mid-to-late 20s. | ||
235 | 11:31 | I think this is conclusive proof, if ever it were needed, | ||
236 | 11:35 | that everybody's brains are prewired to be just a little bit mathematical. | ||
237 | 11:39 | OK, so that was Top Tip #2. | ||
238 | 11:41 | Now, Top Tip #3: How to avoid divorce. | ||
239 | 11:45 | OK, so let's imagine then that you picked your perfect partner | ||
240 | 11:48 | and you're settling into a lifelong relationship with them. | ||
241 | 11:52 | Now, I like to think that everybody would ideally like to avoid divorce, | ||
242 | 11:56 | apart from, I don't know, Piers Morgan's wife, maybe? | ||
243 | 12:00 | (Laughter) | ||
244 | 12:02 | But it's a sad fact of modern life | ||
245 | 12:04 | that one in two marriages in the States ends in divorce, | ||
246 | 12:07 | with the rest of the world not being far behind. | ||
247 | 12:11 | Now, you can be forgiven, perhaps | ||
248 | 12:13 | for thinking that the arguments that precede a marital breakup | ||
249 | 12:17 | are not an ideal candidate for mathematical investigation. | ||
250 | 12:21 | For one thing, it's very hard to know | ||
251 | 12:23 | what you should be measuring or what you should be quantifying. | ||
252 | 12:26 | But this didn't stop a psychologist, John Gottman, who did exactly that. | ||
253 | 12:32 | Gottman observed hundreds of couples having a conversation | ||
254 | 12:37 | and recorded, well, everything you can think of. | ||
255 | 12:40 | So he recorded what was said in the conversation, | ||
256 | 12:42 | he recorded their skin conductivity, | ||
257 | 12:44 | he recorded their facial expressions, | ||
258 | 12:46 | their heart rates, their blood pressure, | ||
259 | 12:48 | basically everything apart from whether or not the wife was actually always right, | ||
260 | 12:55 | which incidentally she totally is. | ||
261 | 12:58 | But what Gottman and his team found | ||
262 | 13:01 | was that one of the most important predictors | ||
263 | 13:04 | for whether or not a couple is going to get divorced | ||
264 | 13:06 | was how positive or negative each partner was being in the conversation. | ||
265 | 13:11 | Now, couples that were very low-risk | ||
266 | 13:13 | scored a lot more positive points on Gottman's scale than negative. | ||
267 | 13:17 | Whereas bad relationships, | ||
268 | 13:20 | by which I mean, probably going to get divorced, | ||
269 | 13:23 | they found themselves getting into a spiral of negativity. | ||
270 | 13:27 | Now just by using these very simple ideas, | ||
271 | 13:29 | Gottman and his group were able to predict | ||
272 | 13:32 | whether a given couple was going to get divorced | ||
273 | 13:35 | with a 90 percent accuracy. | ||
274 | 13:38 | But it wasn't until he teamed up with a mathematician, James Murray, | ||
275 | 13:41 | that they really started to understand | ||
276 | 13:43 | what causes these negativity spirals and how they occur. | ||
277 | 13:47 | And the results that they found, | ||
278 | 13:49 | I think, are just incredibly impressively simple and interesting. | ||
279 | 13:54 | So these equations predict how the wife or husband is going to respond | ||
280 | 13:58 | in their next turn of the conversation, | ||
281 | 14:00 | how positive or negative they're going to be. | ||
282 | 14:02 | And these equations depend on | ||
283 | 14:04 | the mood of the person when they're on their own, | ||
284 | 14:06 | the mood of the person when they're with their partner, | ||
285 | 14:09 | but most importantly, they depend on | ||
286 | 14:10 | how much the husband and wife influence one another. | ||
287 | 14:14 | Now, I think it's important to point out at this stage, | ||
288 | 14:16 | that these exact equations have also been shown | ||
289 | 14:20 | to be perfectly able at describing | ||
290 | 14:22 | what happens between two countries in an arms race. | ||
291 | 14:27 | (Laughter) | ||
292 | 14:30 | So that an arguing couple spiraling into negativity | ||
293 | 14:33 | and teetering on the brink of divorce | ||
294 | 14:35 | is actually mathematically equivalent to the beginning of a nuclear war. | ||
295 | 14:40 | (Laughter) | ||
296 | 14:43 | But the really important term in this equation | ||
297 | 14:45 | is the influence that people have on one another, | ||
298 | 14:47 | and in particular, something called "the negativity threshold." | ||
299 | 14:51 | Now, the negativity threshold, | ||
300 | 14:52 | you can think of as how annoying the husband can be | ||
301 | 14:57 | before the wife starts to get really pissed off, and vice versa. | ||
302 | 15:01 | Now, I always thought that good marriages were about compromise and understanding | ||
303 | 15:06 | and allowing the person to have the space to be themselves. | ||
304 | 15:09 | So I would have thought that perhaps the most successful relationships | ||
305 | 15:12 | were ones where there was a really high negativity threshold. | ||
306 | 15:15 | Where couples let things go | ||
307 | 15:17 | and only brought things up if they really were a big deal. | ||
308 | 15:20 | But actually, the mathematics and subsequent findings by the team | ||
309 | 15:24 | have shown the exact opposite is true. | ||
310 | 15:27 | The best couples, or the most successful couples, | ||
311 | 15:30 | are the ones with a really low negativity threshold. | ||
312 | 15:33 | These are the couples that don't let anything go unnoticed | ||
313 | 15:37 | and allow each other some room to complain. | ||
314 | 15:40 | These are the couples that are continually trying to repair their own relationship, | ||
315 | 15:45 | that have a much more positive outlook on their marriage. | ||
316 | 15:48 | Couples that don't let things go | ||
317 | 15:50 | and couples that don't let trivial things end up being a really big deal. | ||
318 | 15:56 | Now of course, it takes a bit more than just a low negativity threshold | ||
319 | 16:01 | and not compromising to have a successful relationship. | ||
320 | 16:06 | But I think that it's quite interesting | ||
321 | 16:08 | to know that there is really mathematical evidence | ||
322 | 16:11 | to say that you should never let the sun go down on your anger. | ||
323 | 16:14 | So those are my top three tips | ||
324 | 16:16 | of how maths can help you with love and relationships. | ||
325 | 16:19 | But I hope, that aside from their use as tips, | ||
326 | 16:21 | they also give you a little bit of insight into the power of mathematics. | ||
327 | 16:25 | Because for me, equations and symbols aren't just a thing. | ||
328 | 16:30 | They're a voice that speaks out about the incredible richness of nature | ||
329 | 16:35 | and the startling simplicity | ||
330 | 16:36 | in the patterns that twist and turn and warp and evolve all around us, | ||
331 | 16:41 | from how the world works to how we behave. | ||
332 | 16:44 | So I hope that perhaps, for just a couple of you, | ||
333 | 16:46 | a little bit of insight into the mathematics of love | ||
334 | 16:49 | can persuade you to have a little bit more love for mathematics. | ||
335 | 16:52 | Thank you. | ||
336 | 16:53 | (Applause) |