Maths of Love and Sex

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Speaker 8 there, the last one.

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Speaker 8 refreshes.

Speaker 8 Hello, I'm Robin Inks.

Speaker 1 And I'm Brian Cox. And in a moment, you're going to be hearing me saying, hello, I'm Robin Inks.

Speaker 8 And I'm Brian Cox.

Speaker 1 Because this is the longer version of the Infinite Monkey Cage.

Speaker 9 This is the podcast version, which is normally somewhere between 12 and 17 minutes longer than that that is broadcast on Radio 4.

Speaker 1 It's got all the bits that we couldn't fit in with Brian over explaining ideas of physics.

Speaker 10 I do object to the use of the word longer, though, because that's obviously a frame-specific statement.

Speaker 9 Yeah, we haven't got time to deal with that, because even in the longer version, we can't have a longer intro.

Speaker 1 Can we just let them listen?

Speaker 10 I've got an idea. Can we just not have a podcast version of this intro to the podcast, which can be longer than the intro to the podcast?

Speaker 10 And then we can have a podcast version of the podcast intro to the podcast.

Speaker 1 We can't get started by now, but if you're still hearing this, I don't know what's going on.

Speaker 10 And then we can have a podcast, podcast, podcast version of the podcast, and then it would be a podcast version.

Speaker 12 Hello, I'm Robin Inks.

Speaker 11 And I'm Brian Cox.

Speaker 13 And we're well into series 13.

Speaker 13 As usual, we're getting lots of lovely emails and questions about the nature of the universe, the definition of reality, the standard model, epigenetics, and whether Brian uses shampoo and conditioner or an all-in-one formula.

Speaker 13 But the most common question that we always get is...

Speaker 13 I've been listening to Monkey Cage for 13 series, and though it's greatly enhanced my ability to talk about weakly interacting particles and the Higgs boson, I still can't find love. Can you help me?

Speaker 13 Well, yes, we can, because today we're going to give you the power to find your partner of choice using statistical analysis and mathematical formulas.

Speaker 13 No more shilly-shalling with psychobabble books on men from Mars and women from Venus. We're going to get Euclidean in the boudoir.

Speaker 10 That's a meaningless statement, isn't it? Because how could you be non-Euclidean in the boudoir?

Speaker 13 I like to experiment in the bedroom. By which I actually mean I like to experiment in the bedroom.
I'm currently researching blackbody radiation and I've got a very small photon gun as well.

Speaker 16 That's what

Speaker 12 my wife's not happy.

Speaker 12 The curtains are both on fire and not on fire.

Speaker 17 So

Speaker 10 this is a concept, Monkey Cage. We'd like to invite you to a party.
A party just populated by mathematicians, statisticians, and comedians.

Speaker 14 What a party!

Speaker 10 You may or may not meet someone, you may or may not go home with them, you may or may not get married, have a baby boy or girl, have an affair, get divorced, and de-invent yourself as a postmodernist philosopher.

Speaker 10 We'll analyse the probability of each of these events and show you how to optimize your behavior algorithmically in order to find the perfect mate and live the perfect happy life using mathematical logic and statistics alone.

Speaker 13 Let the party begin!

Speaker 13 So, swelling from the bottle of reason, dipping suggestively into the guacamole of appropriate doubt, our party guests tonight are Dr.

Speaker 3 Hannah Fry. I'm a mathematician from UCL, and the thing I think is most attractive about mathematics is its absolute truths.

Speaker 6 I'm David Spieger-Holter. I'm the Winton Professor for the Public Understanding of Risk from Cambridge.

Speaker 6 And the thing I think is best about statistics is that you can strip off those layers of messy noise and reveal that elegant, smooth signal underneath.

Speaker 13 Well done, David.

Speaker 17 These people are hot under the collar already.

Speaker 22 My name is Paul Foote. I'm a comedian.
And the most beautiful thing about mathematics is a cute angle.

Speaker 17 Thank you very much. This is our panel.

Speaker 13 Hannah, we'll start with you. Start with the mathematics.

Speaker 13 How do you go about applying mathematics to something as, well, I suppose what people would see as seemingly random as finding the perfect partner?

Speaker 3 Well, you certainly can't account for that that sort of mysterious side of things. You can't write an equation for exactly who you'll find attractive.

Speaker 3 But there are all sorts of patterns in the way that you search for people, in the way, in the things that you, or rather, in when you decide to settle down in your life, in how to optimise your searching strategies, those kind of things.

Speaker 3 And that's where the mathematics can really come in.

Speaker 13 So, can you give us an example of something that someone would, you know, there are people here mathematically inclined, they're thinking I haven't yet used it as a bridge towards, you know, love and future security.

Speaker 3 So, the starting point give them a tip, is that what you're saying? Okay, well, so for example, there is a lovely piece of mathematics called the stable marriage problem.

Speaker 3 And essentially, it's you're at a party, and you have to imagine that there is a group of boys and girls who are trying to target each other.

Speaker 3 I have to say, a lot of the mathematics when it comes to dating tends to be framed in a traditional way, just because it's a lot easier to have boys and girls or two groups of people targeting each other.

Speaker 3 So, each person at this party has an ordered list in their head of who they'd most like to date.

Speaker 3 And if you allow it to play out in a very boy-meets-girl way, you can follow that through with a mathematical proof and show that every single time, every person will end up finding a partner.

Speaker 3 But you can also prove that if the boys are the ones who do the approaching, they will always, always, always end up much better off than the girls will.

Speaker 3 And the thing is, that sort of goes against what a lot of people's strategy is when they're they're at a party because to risk humiliating rejection by going up to people and

Speaker 3 seeing if they like you, it doesn't seem like a particularly comfortable thing to do.

Speaker 3 But the thing is, by doing that, what you're doing is you're starting at the top of your list and you're working your way down.

Speaker 3 Whereas if you do the opposite, if you allow people to come to you, then you essentially end up with the least bad person who will approach you.

Speaker 3 So that I think is my first tip then: is to be proactive, the matters, be proactive.

Speaker 10 So So, to define by better off, you mean that essentially we're forming an ordered list of attractiveness, essentially. Exactly.
So, one, two, three, four, five, seven, nine, ten.

Speaker 10 And you're saying that

Speaker 10 by being proactive and approaching number one and then approaching number two, you'll get higher up your own list.

Speaker 3 Exactly. So, even if

Speaker 3 you approach number one and they turn you down, and number two turns you down, and so on and so on and so on, because you're starting at the top of your list and working your way down, you'll end up with the very best person on that list who would even consider you as a smooch.

Speaker 10 What's the branch of mathematics that you're using there? Because it's easy to see that when you describe it in words, but you say that you can perform an analysis.

Speaker 10 So, what actually is the analysis that you do?

Speaker 3 Well, so you can, because you can structure that as a mathematical situation, you can structure it using equations and so on, you can prove that these things are the case.

Speaker 3 It's following something called the Gale-Shapely algorithm, that process of boy approaches girl, girl decides whether she likes boy and rejects him if she doesn't and accepts him if she does.

Speaker 3 And if you follow that through using

Speaker 3 these sort of equations, you can prove that these things are always true.

Speaker 13 But how do you know which ones are up for it?

Speaker 22 And also, wouldn't you be better off sleeping with all of them,

Speaker 22 seeing which is the best one and then choosing?

Speaker 3 That is another strategy, yes.

Speaker 3 The thing is, with this example especially, because it starts off in quite an abstract world, of these equations where you have boys and girls who are approaching each other and no other sort of connections are possible.

Speaker 3 You have to be slightly careful about how much you pull that out of the abstract world and apply it to the real world.

Speaker 3 But I think that the general message is quite clear and quite obvious when you put it back into words.

Speaker 3 It does make good intuitive sense that being proactive is more likely to get you what you want than standing back and allowing your suitors to sort of queue up for you.

Speaker 13 So, what mathematics has discovered is that sitting back and doing nothing leads to less results than doing something.

Speaker 16 Brilliant. Sorry,

Speaker 13 another monkey cage revelation.

Speaker 21 David,

Speaker 10 the subtitle of your book is What Statistics Can Tell Us About Sexual Behaviour.

Speaker 15 So,

Speaker 10 what can statistics tell us about sexual behaviour?

Speaker 6 Well, a lot, but you do have to ask people. You can't put cameras in rooms because that might affect their behaviour, rather.
So, it's probably illegal as well.

Speaker 15 Well, it probably would be.

Speaker 6 I mean, but you don't have to go and ask people, and therefore you have to sort of trust what they say.

Speaker 6 And this is one of the problems that if you just put out a survey online or a magazine does it, well, you know, people might reply. I don't know,

Speaker 6 people in the audience might reply. Who would fill in an online sex questionnaire if it was put up online? Just a we want to know about your sex life.

Speaker 15 Well, no one's put their hand up, but obviously.

Speaker 16 No, I know.

Speaker 21 No one will be the first.

Speaker 16 They're not going to volunteer for that.

Speaker 13 Exactly.

Speaker 17 Exactly.

Speaker 13 Tell you what, if this was Jeremy Vine's audience.

Speaker 21 Oh, absolutely.

Speaker 24 Yes. Yeah.

Speaker 6 So

Speaker 6 if you want to get a representative sample and use proper survey methods, you've got to work really hard.

Speaker 6 The last sex survey done in this country cost £7 million to find out what people were getting up to, how often, with how many people. And this is useful information, believe it or not.

Speaker 6 What sex surveys?

Speaker 6 Oh, well, it grew out of the 90s, it also started in the 1980s, when it started the AIDS epidemic. There's a panic, really, and people realized that they didn't know just what was going on.

Speaker 6 Who was having sex with whom, as same sex, how many partners were being changed, were they using unprotected sex or whatever. And so they started this survey and they had it all planned.

Speaker 6 And then Margaret Thatcher took the money away. She said, Oh, government, we're not paying for anything like that.

Speaker 6 So they found some more money from the Wellcome Trust, and it's been going ever since.

Speaker 10 Oh, I see. So that was essentially public health driven.

Speaker 6 It was a public health, and it's still public health.

Speaker 6 The people paying for it, the Wellcome Foundation and other departments, are paying for it to find out about sexual behaviour because that allows you to know, to plan sexual health services.

Speaker 13 Is there,

Speaker 13 you were saying there about the obviously a government where it seems to show a level of prudishness. Do you see in different countries a sense of greater revelation?

Speaker 13 Because you would think that maybe in Britain there would be that kind of, well I'll go up to the point where I take off my trilby, but the rest is very much between myself and my wife, whoever she may be.

Speaker 13 And but it's so do you see that there are some countries where you would think, yes, these are people very honest, and that perhaps in other nations you may be a little bit more dubious about the statistics when you...

Speaker 6 Yeah, I mean they've tried to do big national surveys, particularly to do with sexual dysfunction.

Speaker 6 The pharmaceutical industry have tried to do this and found that actually getting in the Far East and Asian communities is actually very difficult to carry out interviews such as this.

Speaker 6 In the States, they also had their big survey banned, but now they ask these questions and it's the survey of families, they call it.

Speaker 6 And so they've hidden all their sexual questions within this national survey of family growth. And that's allowed them to carry this on under federal funding.

Speaker 13 Paul, you're a man of the world. You've been both a mathematician, which I know on your website it says you don't like to talk about it, so we've talked about it on every time.

Speaker 14 It was a long time ago.

Speaker 13 You're also a comedian. Do you, in terms of what you've heard so far and in terms of your own experience, have you used mathematics more than humour in the wooing process?

Speaker 14 Or the other way around?

Speaker 22 There is the elephant in the room here, isn't there? That

Speaker 22 there may be, obviously, there is mathematics behind meeting people, but if you mention to people that you are a mathematician, the chances of a sex session are incredibly low.

Speaker 22 So that's one of the reasons I don't talk about it.

Speaker 26 That was 20 years ago.

Speaker 22 20 years ago I did a maths degree and I've been just living it down ever since. And that's why I went into show business.
I took extreme measures.

Speaker 22 So yes, in answer to your question, most of my sex sessions have been because I'm in show business. Or, I suppose, my own personality.

Speaker 22 There must be something genuine about me. So maybe people are attracted to that, but definitely not the mathematics.

Speaker 10 Are you disturbed that when you said it's down to my own personality? You got it with a big laugh.

Speaker 15 It's not fair, is it?

Speaker 22 That's well, I don't know what my personality is anymore. I've been in show business for so long.

Speaker 13 Hannah,

Speaker 13 in your book about the mathematics, love, you talk, for instance, about symmetry.

Speaker 13 And this is something that we've seen quite a lot in newspapers. You know, once every three months, they'll have a picture in a newspaper going, This is the faith that everyone loves most.

Speaker 13 So, can you run us through partly what that also means, not merely mathematically, but I know that some of this is also kind of biologically revealing as well?

Speaker 3 Yeah, of course. So, scientists have been trying for a very long time to really capture the essence of what it is that makes somebody beautiful.

Speaker 3 And there are a few different things that sort of work, and one of them is symmetry, which is that people tend to prefer images of people with naturally symmetrical faces.

Speaker 3 But the thing about beauty is that every time there's a rule, there's sort of a counter-rule, if you like.

Speaker 3 Because while that works wonderfully for pictures of people's faces, when it comes to moving images, so videos or people in the flesh, actually people tend to prefer asymmetry because it's seen as much more authentic.

Speaker 22 Can I ask, what about if you have a symmetrical face but both sides are ugly?

Speaker 3 I mean, it's less than ideal.

Speaker 27 It's less than ideal.

Speaker 10 So

Speaker 10 we're talking earlier about the

Speaker 10 algorithm for approaching approaching people at a party. So that supposes that there's a,

Speaker 10 first of all, that you can make a list in the model. We're talking about mathematical modelling here.
So you can make a list of,

Speaker 10 I suppose, there's a figure of merit you can attach to someone's attractiveness.

Speaker 10 And the suggestion is that people pair off according to the figure of merit. So I think you said something about

Speaker 10 your chance of doing the best that you possibly can.

Speaker 23 So

Speaker 10 is that borne out by the data that attractive people by some measure tend to pair off with attractive people, and less attractive people tend to pair off with less attractive people?

Speaker 3 Well, I mean, it's slightly difficult to get really good data on that.

Speaker 3 One thing you can say in terms of the couples and how they match up with each other, because you have to have an ejective third person measuring how attractive those couples were.

Speaker 3 But one thing that there is good data for is how well or how popular attractive people are on things like online dating websites.

Speaker 3 So, So, there's a wonderful study done by OK Cupid, whose founders, incidentally, are mathematicians, so have deliberately built in bits to their website to allow them to experiment on their customers.

Speaker 3 Which takes you're allowed to rate how attractive you think other people are on a scale between one and five.

Speaker 3 And you can look at how that attractiveness, the average score of attractiveness, relates to how popular somebody is, so how many messages they receive each month.

Speaker 3 And you might expect that the people, the more beautiful people find you, the more messages you'd get. That would kind of be the obvious thing.
But actually, it's not true at all.

Speaker 3 The thing that makes a difference is how much you divide opinion. That's the thing that really counts.
So, people who

Speaker 3 some people think that they're very attractive, but other people think that they're very ugly indeed.

Speaker 3 Those are the ones who do very well. So, the people who have something a bit strange or a bit quirky about them.

Speaker 3 And the suggestion or the reasoning behind this is that when you're looking at how attractive somebody is, you're not just thinking,

Speaker 3 you're also also thinking about your own chances of getting them.

Speaker 3 So, if you come across somebody who is generically attractive, the kind of girl or guy next door, you're imagining that they're gonna be very popular online and that they'll get lots of messages.

Speaker 3 And so, yours will get lost in the thousands. You think that there's a lot of competition for this person.

Speaker 3 Whereas, if you come across somebody, say, who's very beautiful but got lots of tattoos or you know, lots of piercings or something a bit strange about them, which is your cup of tea, you imagine that there's a lot less competition.

Speaker 3 So, it's a better bet for you to try and get involved.

Speaker 13 Well, you talk a little bit in your book about, I don't know how much it's based on truth, but in the film Beautiful Mind about the work of John Nash, there is a certain element of kind of game theory that comes into when he goes out, you know, partying with, if John Nash ever partied, that doesn't seem like

Speaker 13 with three other scientists. Can you give us a little bit about the game theory element?

Speaker 3 Yeah, there are a few different things.

Speaker 3 One thing that's quite important to remember when it comes to dating is that it's not just, it's not like going to a supermarket and picking up the best-looking tomato, right?

Speaker 3 Somebody's got to like you as well. So, there are a few different tricks and techniques that you can use to make yourself appear more attractive.

Speaker 3 You can use some tricks from mathematics and economics to manipulate how attractive people think you are to give you a better chance of sort of capturing the person that you like.

Speaker 22 So, what things like say you've got more money than you have,

Speaker 22 pretending you're going to see them again next week, that sort of thing.

Speaker 3 I mean, lying would work, yes, for a short time.

Speaker 3 But one of them is it comes from something called discrete choice theory, which is about when you give somebody a number of options and they have to make a decision.

Speaker 3 And in particular, something called the decoy effect. Now, this is used quite a lot of the time in, for example, a very famous example of this would be in cinemas.

Speaker 3 So, when you go to a cinema, there might be a small popcorn that's like, I don't know, £3.50 or something, and a large popcorn that's £6, which is like ludicrously expensive.

Speaker 3 But on on the list, they also then include a medium-sized popcorn, which will be like £5.50.

Speaker 3 Now, no sane person would go for the medium popcorn when for 50p more you could have the large popcorn. So it's kind of an irrelevant alternative.

Speaker 3 But because it's on the list, it makes the large popcorn suddenly appear as though it's more attractive.

Speaker 3 Now, you can use this same effect, the decoy effect, to make yourself appear more beautiful. And this is an experiment that was done by an economist, Dan Urali, in the States.

Speaker 3 And he found two students of his that were, he did a survey and that were considered equally attractive. So 50% of people said one was more attractive, 50% of people said the other.

Speaker 3 And what he did is he wanted to create an irrelevant alternative, one that nobody would pick. So he took one of the men's faces and he used Photoshop to make them as ugly as possible.

Speaker 3 And then he sent out to 300 people these three faces, the two original faces and an uglified version of one of the boys. Now, nobody chose the uglified version.
It was an irrelevant alternative.

Speaker 3 But its presence on the list served to make the original owner of the face appear more attractive.

Speaker 3 So, suddenly, 75% of people thought that the person of the original face was more attractive. And if you switch over and uglify the other person on the list,

Speaker 3 the original face suddenly becomes more attractive.

Speaker 3 So, the sort of result of this then is that if you want to make yourself appear as attractive as possible when you pick a wingman or a wing woman going to a party, you should pick someone who's as similar looking to you as possible, but just slightly less attractive.

Speaker 13 That's why I always go drinking with Brian. Yeah?

Speaker 12 Little does my ugly friend Brian know.

Speaker 12 Irrelevant alternative.

Speaker 10 We did swap cardigans once, didn't we?

Speaker 13 Yeah, we changed everything. It's really bizarre.
Put him in a cardigan, and he looks as awful as me.

Speaker 13 But I would like to make it clear that when we first started working on the show, I had a full head of hair that was dark. And I've changed quite a lot, and he hasn't changed at all.

Speaker 13 Suggesting he uses some kind of physics to steal my life force.

Speaker 10 David, one of the few chapters in your book we can talk about on Radio 4 is the

Speaker 10 you write about the probability of a couple. So we've met now, we've gone through, we've had

Speaker 24 deviously selected,

Speaker 10 we've gone through, deviously selected a partner using Hannah's algorithms. So the probability of a couple having a boy or a girl, which is interesting.
Yeah. And there are many nuances

Speaker 14 to settle for your baby.

Speaker 6 Yeah,

Speaker 6 for every 20 boys that are born, 21 girls are born. And this was recognised back in the 17th century.
A guy called John Arbuthnock looked at data over 80 years and found that in every year,

Speaker 6 sorry, more boys than girls been born. And he put it down to his paper, he wrote on it.

Speaker 6 He said this was an argument for divine providence, the name of the paper, because he thought that this must be the will of God, because the increased mortality rate of boys, so that more boys would be killed in wars and things like that.

Speaker 6 In fact, he did the very first statistical significance test to show that the probability if they were equal, 50-50, of getting 80 years like that, was one over two to the 80.

Speaker 6 So it's 21 boys to 20 girls. And the point is, though, that fluctuates.

Speaker 6 It actually, if you plot over the last 150 years, you can see that there are peaks and troughs. And the peaks in the UK, for example, were in 1919 and 1944.

Speaker 6 So, more boys are born at the end of wars. And this is reproducible, same in the States, same in other countries.
Why are more boys born at the end of wars? It's not a big peak, but there it is.

Speaker 6 And people have argued about this for ages. And there's this lovely theory,

Speaker 6 which I like, which says that the probability of having a boy is related to what I shall term coital rate, is the formal term. In other words, how much sex is going on.

Speaker 6 And the argument for that is that

Speaker 6 during a month, the woman's got fertile in the peak in the middle, but if you're having lots of sex, sex every day, twice a day, it's more likely that she'll conceive earlier in the month.

Speaker 6 And there's evidence that conceptions earlier in the month of a small tendency, more likely to be a boy. Okay, so that's fine.
So, what happens at the end of wars? There's a lot of sex.

Speaker 6 You know, men coming home on leave, you know, coming home being demobbed, a lot of unprotected sex. The most babies that have ever been born in this country in number is in 1919.

Speaker 6 So, there's a story, you know,

Speaker 6 frantic fornication breeds boys.

Speaker 24 So

Speaker 6 the interesting flip side of that, of course, is to look at where the dip in the graph is. And the dip in the graph was about 1900, when fewer boys were born.

Speaker 6 And that had declined all the way through the Victorian period.

Speaker 6 And historians have used this, you said that actually what was happening in the Victorian period, why people weren't having so many children, was that they were not having sex.

Speaker 6 And the dip in the number of boys actually reinforces that idea that there was a lot of abstinence going on.

Speaker 22 Because Queen Victoria put them off.

Speaker 6 She relished sex.

Speaker 25 Yeah, she was all miserable, wasn't she?

Speaker 22 And I suppose if you saw a picture of her over the bed or something, it would put you off.

Speaker 13 So you're now saying that depending on the attractiveness of the monarch, that will also happen.

Speaker 13 Who do you currently have? Which of the monarchs of England, post-1066, do you currently have next to your bunk bed?

Speaker 22 Well, I was finding a picture of Henry III is always what gets me going.

Speaker 7 Or

Speaker 22 Queen Anne, lovely, very elegant

Speaker 22 arm. You know, you get a lot of lovely, elegant Queen Anne furniture, don't you?

Speaker 22 Very elegant body as well.

Speaker 13 I think we're going to see quite a spike in the sales of images of Queen Anne amongst radio fallers.

Speaker 21 Can you describe it?

Speaker 13 Can you check that out statistically, please, David, in a month?

Speaker 6 Describe Henry III.

Speaker 10 That's of interest.

Speaker 22 Well, Henry III,

Speaker 7 very,

Speaker 25 very tall, but very gentle.

Speaker 22 Yeah, I don't actually know. Are there any pictures of Henry III? I don't know.
I just...

Speaker 13 You made up the Henry III!

Speaker 12 I believed you had a picture of Henry III above your bunk bed.

Speaker 10 Does anybody know?

Speaker 22 Of course I don't have a picture of him. A picture of Camilla.

Speaker 22 It's not for sexual reasons, it's for loyalty to the crown.

Speaker 24 I was going to ask Hannah,

Speaker 10 given what you've heard, how would your algorithm deal with someone like Paul?

Speaker 3 Well, he's definitely quirky.

Speaker 3 There's that going for you. So.

Speaker 21 Oh, that was too bad. Let's move on.

Speaker 13 So, what

Speaker 13 Hannah, that was apart from if we can just skip the bit about Queen Anne, but we'll probably come back to it.

Speaker 13 Where, in terms of using mathematics to understand love, to understand romance,

Speaker 13 where's the shortfall? What are the things that we can't analyse?

Speaker 3 Oh, certainly, there's some mysterious romantic

Speaker 3 something that you just can't capture with equations.

Speaker 3 There's lots of studies that have been done to see whether you can write a checklist of things that you're looking for in a partner and whether that has any bearing on your long-term happiness once you actually get into a relationship.

Speaker 3 Say things like, you know, what type of job they have, how much money they earn, what they look like, those kind of things.

Speaker 3 There's no evidence, really, no strong evidence whatsoever that you can write down what you're looking for and it matches up with what you actually want.

Speaker 13 But are people actually, I mean, that's the thing, is it isn't a lot of this in the end post-hoc rationalization where

Speaker 13 you think they go, oh, I was drawn to this person because of this, this, and this, but in fact, it was just it was chemicals,

Speaker 13 which I know is not a popular kind of choice, and one of those Valentine's cards, if you do just give a periodic table, it's not there, but it's you know, that, but there is

Speaker 13 so a lot of the research, a lot of what we think we're drawn to is perhaps something of an illusion.

Speaker 3 Yeah, so there's um the dating algorithms that you get on on dating websites are trying to do exactly that, right?

Speaker 3 They're trying to take a list of things that you're looking for in a partner, as well as your own characteristics, and from the huge numbers of people available in the world, to try and filter them all out and deliver somebody to you who you're likely to have a meaningful relationship with very quickly.

Speaker 3 But the thing is, if they were really capable of doing that perfectly, no one would ever go on any bad internet dates anymore, right? And I mean, they certainly do.

Speaker 3 And there's a couple of experiments that have tried to test out how effective these algorithms are.

Speaker 3 There was one in particular where they tricked some people into thinking that they were more compatible than they actually were.

Speaker 3 They lied to them and said that they were a 30% match rather than a 90% match.

Speaker 3 And then tracked how that relationship evolved over time compared to a couple who, or a number of couples, who really were a 90% match.

Speaker 3 And there was a slight difference in how likely they were to exchange more than four messages. I think the way they worked out was.

Speaker 3 But really, I mean, ultimately, the biggest thing about those kind of algorithms is that if you're told that you'll get on well with somebody, you believe that you will.

Speaker 3 That's really the biggest thing.

Speaker 10 David, the use of statistics, as you said earlier, sometimes or often reveals counterintuitive results, as you said about the fact that the number of boys born after a war, shortly after a war, increases.

Speaker 10 And then there's an interesting reasons for that you can explore. So I wondered what the most counterintuitive statistical result is that you've come across in this field.

Speaker 4 Oh, I don't know about counterintuitive.

Speaker 6 The one that's constantly challenging is the conflict between a mathematical result and what you get when you ask people. And that's to do with asking people how many sexual partners they've had.

Speaker 6 Talking about opposite sexual partners. Now, in a closed group of the same number of men and same number of women, the mean number of partners should be the same for men and for women.

Speaker 6 Not the median or the mode, the mean number, because both of those are related to the total number of partnerships.

Speaker 6 When I do this in schools, I talk about people shaking hands or whatever.

Speaker 10 Perhaps you should explain the terminology because we're an educational show as well.

Speaker 6 Educational show. It's not what I mean of

Speaker 6 the mean number.

Speaker 6 If you've got a group of men and you add up, you say, how many sexual partners have you got? And you add all those up and divided by the number of men, that's the mean.

Speaker 6 And you do the same for a group of women, the same size. In a closed group, those means must be the same mathematically.

Speaker 6 However, when you actually go out and ask people how many sexual partners they've had, men absolutely,

Speaker 6 essentially in every survey done, report having more sexual partners than the women do.

Speaker 6 Usually it's double.

Speaker 24 But

Speaker 6 which is mathematically impossible.

Speaker 6 In the last survey, for example, among 16 to 44 year olds, the average number for men was 12, and the women said they had an average of eight male sexual partners each, which is just not not feasible.

Speaker 22 But could that be the men having sex with each other?

Speaker 6 Oh, no, no, no.

Speaker 6 That's a good, that's a good thing. No, this is opposite sex sexual partners.
So there's various explanations about this.

Speaker 6 Have they left out some women who have had huge numbers of sexual partners from the survey?

Speaker 6 People lying.

Speaker 6 And the interesting one they've done, survey, the experiment they've done on people to wire them up to fake lie detectors and then ask them how many sexual partners they've had, and then the numbers start matching.

Speaker 6 The other thing is, are people just being a bit more, you know, are some people more careful about how they add up their numbers?

Speaker 6 Because when you actually look at the data, it's quite clear that after people have finished with one hand or maybe two hands, it all gets a bit vague.

Speaker 6 They're going 15, 20, or 40, 100, 500, or whatever. Except one man who said 47, which I think shows a remarkable grasp of history.
So maybe,

Speaker 6 so there's a strong argument. Maybe men and women, when they're looking back over what is possibly a rather ill-remembered history, actually make these guesstimates in rather different ways.

Speaker 22 Could it be that if you've had a few wines, you could mistake the same woman for another woman

Speaker 22 and put her again on the next tally, you know?

Speaker 6 Quite possibly. And also,

Speaker 6 it's a suggestion that many women have had partnerships that they would just rather forget.

Speaker 10 But these are, as you say, these become

Speaker 10 interesting and things to study when you're talking about developing policy based on the data.

Speaker 10 So

Speaker 10 is it is it clear when how do you design a questionnaire to try to get the most accurate data possible in these areas?

Speaker 6 Struggling with this for years.

Speaker 6 The survey methods now are based on ensured confidentiality, face-to-face surveys, not online, but where the person being interviewed is answering them on a computer, the interviewee, the interviewer cannot see the responses, the whole thing is locked down and shut off and made confidential.

Speaker 10 So it's absolutely anonymized.

Speaker 6 Absolutely anonymized, yeah, clearly. It's a complete contrast to Kinsey back in the 1940s, very famous, he did 15,000 of his sex surveys.

Speaker 6 Back in those days, he made friends with them, he asked them in, gave them a cigarette, chatted to them, you know,

Speaker 6 used vernacular terms, whereas now medical terms are used for parts of the body and actions.

Speaker 6 So, and he just chatted away and completed all these figures. He also used a policy of denial.
So he would just ask people, when did you last have sex with someone other than your wife?

Speaker 6 What have you? When did you last? And they'd have to actively deny everything.

Speaker 6 So with the result that he got extraordinary stats, that 50% of men had had affairs,

Speaker 6 37% of men had had sort of homosexual experiences to orgasm, and 17% of farm boys had had sex with animals. So

Speaker 6 this was in 1944.

Speaker 6 This was in 1948. You can imagine the fuss it made at the time.

Speaker 14 What loaded question did he ask to elicit sex with an animal?

Speaker 9 17% of people followed it and said last Wednesday.

Speaker 21 Yeah.

Speaker 16 Told them, told them when they last

Speaker 10 panicked.

Speaker 24 That was a long time ago.

Speaker 22 And also, why did he only ask the farm boys?

Speaker 16 I don't know.

Speaker 6 That was just 70%. In the rest of them, it was half, it was 8% in the general male population.
17% in farmers.

Speaker 16 It's a matter of access.

Speaker 22 Yes, it's just less.

Speaker 22 When you're not a farm boy, it's less convenient, isn't it? You'll actually go to the farm, it's all the travel, both ends, you know, to traffic, it's not worth it.

Speaker 6 But Kinsey's data are not considered hugely reliable.

Speaker 13 So, what are the flawed areas of in terms of, you know, Kinsey was used for quite a long time, I think, as being a representative.

Speaker 13 What would you say are the statistics that are still brought up that come from Kinsey, which actually now are flawed enough to dismiss them, or near dismiss them?

Speaker 13 Oh,

Speaker 6 I don't know, actually, because it depends what you. I think they're the same sex behavior ones, the 37% of men, because

Speaker 6 in a way it depends. He was using whole lives experience.
He was using sort of adolescent behaviours and things like that. So, and in the end, he came up with this figure.

Speaker 6 He's the one where the one in 10 figure came from that was used so much in the gay rights debate in the 1970s and things. And that's been argued about ever since, this one in 10 figure.

Speaker 6 And of course, now, with these decent surveys, you can get get a very good handle on these. For example sexual identity is now an official statistic collected by the Office of National Statistics.

Speaker 6 Whether people say they're gay, lesbian or bisexual is part of official statistics. And that's a lot less.

Speaker 6 That's only about two and a half percent both the men and women would say they're gay, lesbian or bisexual.

Speaker 6 But if you ask about actual same-sex experience, what people have got up to, then it's a hugely different answer.

Speaker 6 You know, now one in six

Speaker 6 younger women will say they've had a same-sex experience.

Speaker 6 One in 20 as an actual same-sex partner. So the things have changed massively, but you have to be very careful what you define.

Speaker 6 Do you mean about

Speaker 6 self-identity or whether it's actual behaviour or whether it's attraction? These are three different things that need to be very carefully deconstructed.

Speaker 10 So we're having two different discussions here. There's the measurement of behaviour.

Speaker 10 And then, Hannah, you're talking more about modelling of behaviour. So I suppose

Speaker 10 it would come as a surprise to many people that we can even begin to consider modelling something as complex as human behaviour in general, particularly sexual behaviour, using mathematics.

Speaker 10 So, how well developed is that area of research?

Speaker 3 I mean, it's not

Speaker 3 well, you're right that it's a hugely complicated problem. There are all sorts of things that come into it that you just can't possibly hope to capture with a mathematical model.

Speaker 3 But one of the things that I think is quite interesting is that through all of the noise, through all of the set of circumstances that led to you getting together with a particular partner, once you look at things from the level of a whole population, there are, as David's mentioning, these very distinctive patterns that come out and patterns that have really elegant mathematical formulas.

Speaker 3 So, certainly, when it comes to looking at sexual relations, the sort of results of the surveys that David is discussing, they have something called a power law relationship in terms of the number of sexual partners that people have.

Speaker 3 And this is something which hints at the way that the network of sexual contact is created. Essentially, what it means is that you have something called a scale-free network, right?

Speaker 3 So, you have these hubs, if you like, that sit at the center of the sexual contact network, these people who've had a huge number of sexual partners compared to the majority of us who've only had

Speaker 3 a handful. And it's very similar to the way something like the Twitter network is structured.

Speaker 3 So, when you can draw those analogies between how our sexual structure or sexual connections are and and other systems like Twitter, then you sort of create these bridges between the universes that allow you to gain many more insights.

Speaker 10 So, by power law relationship, what do you mean for

Speaker 10 sexual partners, let's say?

Speaker 3 Yeah, so what it means is that the vast majority of people have roughly the same number of sexual partners, which is

Speaker 3 around the sort of 10 mark, I think, from those surveys between, yeah, please definitely.

Speaker 6 Well, yeah, I mean, the power law just means it's a massive long tail, a huge long tail, which it shows up in the data.

Speaker 6 I mean, if you look at, if you ask this distribution I was talking about with an average of 12 or 8 or something for that, the mode, the peak of the distribution, is 1.

Speaker 6 The most common response: yeah, I've had one sexual partner. But then you've also got the people saying 1,000, 2,000, who are way off on the tail of the graph.
Oh, I see, so that means that

Speaker 6 they really influence the mean. In fact, the shape of the distribution might mean that almost that the mean doesn't even exist, which it often doesn't in power law distributions.

Speaker 3 But what this means is the really long tail on this distribution means that you have a non-zero chance of coming across somebody with any number of sexual partners, right?

Speaker 3 So it means that you know 50,000 sexual partners, although there probably aren't very many of them in the world, if actually I probably I chose a number that was too high, don't do that, I probably chose that too high.

Speaker 3 10,000.

Speaker 3 Mick Hucknell said he's 6,000 people and he's, well, Mick Hucknell.

Speaker 10 But I suppose this matters hugely for if you're talking about health policy, for example, actually looking at the transmission of certain sexual diseases, for sexually transmitted diseases, then where the mean is and where these tails are in the distribution matter a lot.

Speaker 6 Yeah, because the spread of a disease is going to be influenced exactly by the network behavior that Hannah's

Speaker 6 describing.

Speaker 6 So it becomes absolutely crucial in estimating what was a few years ago, absolutely, people were in a real panic about the spread of HIV through the population and the old tombstone adverts and things like that.

Speaker 6 So it was absolutely absolutely vital to understand what behaviour was going on and how that's changed over time. And now, you know,

Speaker 6 there's again a resurgence in sexually transmitted diseases. It's very important to understand how that's happening through what sort of network.

Speaker 13 How do you feel, Paul, about the

Speaker 13 listening to, as you know, a former mathematician who's left it long way behind now for show business,

Speaker 13 the use of mathematics and statistics in understanding love? Because there are some people who I would imagine they just don't like the idea.

Speaker 13 There are certain areas where the idea that science will get involved, it ruins the mystique, as you were saying. And how do you feel about that, Paul?

Speaker 22 Well, it's a numbers game, isn't it?

Speaker 22 I mean, the more people you ask, the more sex sessions you get.

Speaker 22 You just have to keep asking, and you keep getting more and more.

Speaker 10 As Plato said, if you don't ask, you don't get.

Speaker 24 Is that what you're saying?

Speaker 14 Well, exactly, that's how it works.

Speaker 22 I've met a, I don't know whether this really has anything to do with anything, anything, really,

Speaker 22 but I met a gentleman in Denmark, and I said,

Speaker 22 he wasn't a particularly attractive gentleman, but he said that he'd been in many, many threesomes with various women. He liked being in threesomes.
He didn't like just one, he preferred a threesome.

Speaker 22 And then I said, How did you get into all these threesomes? And he just said, I asked.

Speaker 22 And nearly always I said, yes.

Speaker 14 It's a tip.

Speaker 22 So

Speaker 22 that's what I think. Just got to asking.

Speaker 13 That was nothing to do with the question I asked, but wasn't as we went down it.

Speaker 25 I can't really remember what the word was.

Speaker 13 No, it was a long time ago. It doesn't matter.

Speaker 10 It's like Gardener's Question Time.

Speaker 10 I mean, it was really full of useful little tips for everyday life.

Speaker 13 But what for you, Paul, has been the most

Speaker 13 of what you've heard this evening, is there anything that you would consider kind of revelatory from either David or Hannah about this understanding or these statistics?

Speaker 7 Well,

Speaker 22 yes, there is something I find revelatory, and maybe I shouldn't say it, but

Speaker 22 I can't believe in this day and age that the most common thing is people say they've only had one sexual partner.

Speaker 22 And I can't believe that the mean is only ten, because people are all certainly young people. Is that to do is that old peoples older people skewing the statistics, skewing the age of the title.

Speaker 6 Well, you get the point is that you get these changing behaviours not just a function of age, it's a function of when you're born as well, because you get these cohorts moving through

Speaker 6 and they take their behaviour with them. So various practices, which I won't go into in detail,

Speaker 6 you know, now are becoming more common in older people because they've grown up with them.

Speaker 16 If you see what I mean.

Speaker 24 Is it whips?

Speaker 17 No,

Speaker 6 no, I don't think we should start guessing. I don't think that's

Speaker 24 the solution.

Speaker 13 Yeah, this is very much reaching its natural conclusion as a show, isn't it?

Speaker 13 Is there a way you think people could be happier using mathematics, using statistics in terms of is there a practical way that those people listening to this programme who are thinking maybe now, maybe now I've been given the armouring of statistics and mathematics, tonight is the night to go to that bar.

Speaker 13 Do you think that is that really true, or is it just going to be something that's good for a book and a text?

Speaker 3 This is aside from their natural love of the mathematics, right? Absolutely. Okay.
I think what's quite nice about using maths in this way, you have to sort of take it with a pinch of salt.

Speaker 3 I'm not suggesting that everybody should live their life by these rules. But what comes out time and time again when you look at things from this perspective are quite positive messages, right?

Speaker 3 It's like be proactive, go for the person that you're after, play up to whatever it is that makes you different, don't allow yourself to be exploited.

Speaker 3 I think all of those messages are generally quite positive and things that you can forget when you're sort of stuck in the drudgery of being single for a long time.

Speaker 3 So, perhaps not in the individual case, but I think the overall message is quite positive.

Speaker 6 And I think looking at stats is quite positive because you know, if you think you know, everybody else is a massive amount of sex and with all these partners and all this activity and all this time, when you actually see yourself where you are on the distribution of things, they maybe think, oh, well, you know, I'm fairly normal.

Speaker 6 You know, the median number of times a couple now, young couple has sex is over four weeks is three. It's gone down over the last 20 years.

Speaker 6 As I said, the most common number of partners people have had is one, although there's a very long tail beyond that.

Speaker 6 Many young people have sexual problems, as many as older people as well, who report having sexual problems.

Speaker 6 So, you know, it's not that every you can actually, and if that is reassuring to find out that actually, you know, maybe you are just sort of normal.

Speaker 22 I had a thing a bit like that because

Speaker 22 this actually happened to me about four years ago.

Speaker 22 I was sitting on a plane in Australia and talking to another comedian, and he'd revealed that the night before he'd been on stage and sort of flirted with someone in the audience and then ended up having a sex session.

Speaker 22 So I thought, oh, you know, I wish my life could be like that, you know. So then I just went on a massive sort of sexual rampage for about three years.

Speaker 22 And then, because I saw him last year, also in Australia, and I said, oh, you know, I've got the life that you have now. I have all these sex sessions.

Speaker 22 And he said, no, no, that was just a one-off thing that happened three years ago.

Speaker 22 And he'd been really quite lonely for

Speaker 22 a long time.

Speaker 22 That is an actual thing that actually happened. But it was based on a misunderstanding because I thought, oh, that's what it's like in show business.
You can have sex with loads of people.

Speaker 10 So you're one of those high-density nodes

Speaker 21 you've been talking about.

Speaker 22 Yeah, I'm definitely a high-density.

Speaker 13 You're a long way down the tail, as we've discovered.

Speaker 13 So I was going to finally ask you, but I was going to go, have you got any advice for anyone, but I think you've you've just given it.

Speaker 13 So that's, and David, for Radio 4, just that bit of going, young people have as many sexual problems as old people. It's made a lot of Radio 4 listeners go, good.

Speaker 13 So I think that's been an upbeat point as well. We asked the audience a question as well, and that question was, how would you use science to attract a partner?

Speaker 13 And the answers are: I would eat as many deep-fried Mars bars as possible, eventually creating strong enough microgravity around me to attract a suitable mate.

Speaker 11 That just says a very lonely man, man.

Speaker 13 Which I also like, he's done Mars TM as well. That's the kind of accuracy we enjoy amongst our audience.

Speaker 10 This one seems like a direct observation of Paul, actually. It says, act chaotically to become a strange attractor.

Speaker 10 Act like a quark and use my charm.

Speaker 15 As a niche joke,

Speaker 13 I'd invent something to make me sound like Brian Blessed and look like Brian Cox.

Speaker 17 Oh, God.

Speaker 21 If his voice came out of.

Speaker 21 Put on it wonderful!

Speaker 21 I want to go to Mars!

Speaker 13 You couldn't do it. Your shows just were one, they'd be over a lot quicker.
Brian doesn't do long lingering gazes.

Speaker 15 Let's just get up the volcano and eat it.

Speaker 13 I would invent something to make my husband say, does that sound like Brian Blessed and look like Brian Cox? Another one there.

Speaker 16 Why are the two of those?

Speaker 13 They may well be married. This is

Speaker 13 create an ex-Machiner version of Brian Cox.

Speaker 13 Use a Doctor Who perception filter to masquerade as Brian Cox.

Speaker 13 I already have travelled back in time for the future to become Brian Cox.

Speaker 17 Hello, David.

Speaker 13 Show them your Brian Cox. Anyway, so the.

Speaker 16 What have you done to us?

Speaker 13 Much of this will make the podcast version. Much less will make the 430 430 Radio 4 version.
So, thank you to David Spiegelholter, to Dr. Hannah Fry and Paul Foote.

Speaker 10 Now, I hope you found this show practical and useful. And if you attempt to put into practice anything you've heard today and it leads to love, please do not write in and give us the details.

Speaker 13 It's disgusting. Anyway, so warning.

Speaker 13 By the way, warning, prolonged use of mathematics to find love may lead to headaches, drowsiness, and astonishing revelations about the shape and structure of the universe.

Speaker 13 Please consult the doctor if your view of the universe becomes unstable. On the other hand, ignoring all reason and deploying a purely illogical approach to your everyday life.
Large

Speaker 27 internal conditions.

Speaker 10 And then we've got the podcast podcast podcast podcast version of the podcast podcast.

Speaker 1 Brian doesn't even know that you have actually now listened to the whole of the show

Speaker 1 and this is all he's been doing

Speaker 1 for the last 47 minutes.

Speaker 1 And it's not going to end for a while either.

Speaker 10 It's a nested infinity of podcasts. And you could probably sum it up.

Speaker 1 This is my life.

Speaker 10 You just end up with a podcast.

Speaker 30 Dashing through the store, Dave's looking for a gift. One you can't ignore, but not the socks he picks.

Speaker 14 I know, I'm putting them back.

Speaker 28 Hey, Dave, here's a tip.

Speaker 27 Put scratchers on your list.

Speaker 6 Oh, scratchers, good idea.

Speaker 30 It's an easy shopping trip.

Speaker 28 We're glad we could assist. Thanks, random singing people.

Speaker 30 So be like Dave this holiday and give the gift of play.

Speaker 19 Scratches from the California lottery. A little play can make your day.

Speaker 10 Please play responsibly.

Speaker 16 Must be 18 years or older to purchase play or claim.