Do men or women have more brothers?
math.stackexchange.com
math.stackexchange.com
One interpretation is "if I am the last born (or some other gender-neutral specifier), do I have more brothers if I am a man or a woman?" As paulmd answers, the chances are even.
But we can also interpret it at the aggregate level. Say we have a family with M men and W women, and we decide to count brothers by polling each person for how many brothers they have, and totaling the result up. If we poll men, we get M(M-1) brothers, and if we poll women, we get MW brothers. The result depends on the distribution, but at the expectation value (M = W), women have more. Same conclusion if we average instead of sum.
The third, most natural interpretation is to "deduplicate:" ask each person to list his or her brothers, take the count of the union. This is the familiar sense: we would say there are three Brontë Sisters, even though none of them individually had three sisters. Here the number of brothers equals the number of men, except when then there's only one man; then each women has 1 brother and each man has 0.
Gender: B G
# brothers: 0 0
B avg # brothers: 0
G avg # brothers: 0
Gender: B,B B,G G,B G,G
# brothers: 1,1 0,1 1,0 0,0
B avg # brothers: (2 + 0 + 0) / (2 + 2) = .5
G avg # brothers: (0 + 2 + 0) / (2 + 2) = .5
Gender: B,B,B B,B,G B,G,B G,B,B B,G,G G,B,G G,G,B G,G,G
# brothers: 2,2,2 1,1,2 1,2,1 2,1,1 0,1,1 1,0,1 1,1,0 0,0,0
B avg # brothers: (6 + 6 + 0 + 0) / (3 + 6 + 3) = 1
G avg # brothers: (0 + 6 + 6 + 0) / (3 + 6 + 3) = 1
Gender: B,B,B,B B,B,B,G (x4) B,B,G,G (x6) B,G,G,G (x4) G,G,G,G (I collapsed the different permutations here)
# brothers: 3,3,3,3 2,2,2,3 1,1,2,2 0,1,1,1 0,0,0,0
B avg # brothers: (12 + 24 + 12 + 0 + 0) / (4 + 12 + 12 + 4) = 48/32
G avg # brothers: (0 + 12 + 24 + 12 + 0) / (4 + 12 + 12 + 4) = 48/32
In a family of N children, both boys and girls will have, on average, (N-1)/2 brothers.Arguments for unequal results are slipping in assumptions that make the observed distribution different from the enumerated one.
It's an exercise for the reader to prove that this equality holds for higher N, it's quite easy (the way I formatted the arithmetic shows how to match up terms).
And there are a half dozen arguments in this thread that claim to disprove this result even if they accept the coinflip hypothesis - it's really those that I take issue with.
First of all as many sources will afirm, for example http://www.pewresearch.org/fact-tank/2013/09/24/the-odds-tha..., the ratio of boys to girls at birth is not 50-50 and is not the same across ethnic groups, cultures, etc. With some surprising interaction effects.
As soon as you have a mix of families with different sex ratios, the odds of siblings being brothers is now positively correlated (even if ever so little) with your own gender. And the result of that is that the average man has more brothers than the average woman does.
However the theoretical effect is very small, and you would need a large random sample to reliably detect its existence.
Interestingly, these sort of things are much easier to model empirically the way you did (all you would need is a small tweak to the "haveAKid" condition in the while loop around line 35), whereas the math gets pretty complicated fast.
Edit2: I don't remember why I was getting all girls, but the other issue was integer division. If boybrothers/boys truncates to 0, then (girlbrothers/girls)/(boybrothers/boys) will divide by 0.
You might try throwing in a random seed. I just trusted that my system would iterate enough that it wouldn't matter, but if you're getting all boys (I'm not sure that's the case, but you said that you're getting 0 girls...), it could be that for whatever reason, the system might not be advancing the seed. Try inserting the following two lines immediately after import random:
from datetime import datetime
random.seed(datetime.now())
You actually bring up an interesting question of design philosophies. I thought that the script that I posted was extremely explicit, and therefore clear. Yours is far more terse, and implicit, which means that there's less to understand, but there is some onus on the reader to decode what is happening.
I'm not sure which is better, as a policy. Obviously, solving problems with fewer lines is desirable, but it's also good to make solutions that are intrinsically obvious.
The notebook below shows a simulation of 50k towns of 100 families. It also holds for different max numbers of children and more families.
https://gist.github.com/rcarneva/7baac666bd65f487df73378a90c...
That's probably how they begin planning them, but plans change. Also, there's likely quite a few unintended births.
Or, the alternative reply, if you prefer:
You sarcasm is far too understated to do well on a forum like this. ;)
But if you make the towns larger, the discrepancy will disappear (for example at 100 towns of 50k families I would expect it to be lost in the noise).
Or to rephrase the question - after flipping a coin and getting 50 heads in a row, what's the chance of the 51st coin coming up heads? (answer: 50%)
Now in reality the chances of each sex may not be equal - some people have a genetic predisposition to have more sons than daughters or vice versa. And of course there's family planning and so on that may artificially alter the number of brothers/sisters (eg to carry on the fmaily name).
https://www.sciencedaily.com/releases/2008/12/081211121835.h...
I'm pretty sure that effect is a lot stronger than any genetic predispositions.
- Half the parents will have an only child of sex B.
- One quarter will have one A, one B.
- One eighth will have A, A, B.
- One sixteenth A,A,A,B.
This converges on 50/50! Number of male brothers of males = 1 each * 2 Males = 2
Number of male brothers of females = 2 each * 2 Females = 2
and proceed to add all of those. In other words, if you weight the count by the number of siblings that have that count.If you just add up, for any group, how many brothers a female in that group has vs how many brothers a male in that group has, then the females win. In the case above, for example, males have 1 brother, females have 2. So, said another way, odds are that a given female has more brothers than a given male.
But your argument led me to a result that was not at all intuitive (for me)- which is very cool.
That also makes sense if you think about it intuitively. As the best (IMO) answer to the SO post put it:
"I'll write p for the gender ratio, i.e. the proportion of all people who are men. In real life p is close to 0.5, but this doesn't make any difference. In any random set of n persons, the expected (average) number of men is n⋅p.
1. Take an arbitrary child x, and let n be the number of children in x's family.
2. Let S(x) be the set of x's siblings. Note that there are no gender-related restrictions on S(x): It's just the set of children other than x.
3. Obviously, the expected number of x's brothers is the expected number of men in S(x).
4. So what is the expected number of men in this set? Since x has n−1 siblings, it's just (n−1)⋅p, or approximately (n−1)÷2, regardless of x's gender. That's all there is to it."
Assume you're a man and you have a sister. Your next sibling is about to be born.
There is a 50% chance that it's a boy! In which case, he will have one brother (you), you will have one brother (him), and your sister will have 2 brothers. She wins.
There is a 50% chance that it's a girl! In which case, both she and your other sister will both have 1 brother (you), whereas you won't have any. You loose again!
You just put your finger on the scale. All you can assume is that you're a man, not that you have a sister.
The girls each have 2 brothers...
The boys each have 1 brother.
6 siblings, 3/3... 8 siblings, 4/4... etc
Gender: B,B B,G G,B G,G
# brothers: 1,1 0,1 1,0 0,0
Average # of brothers for boys: 0.5Average # of brothers for girls: 0.5
You can see that in the families with a mix of boys and girls, the girls do indeed have more brothers. But this is balanced out by the families with more (or in this case all) of one or the other.
There's one way to have zero daughters. In BBBB, each of the four boys has 3 brothers. Score 12 for the boys here.
There's four ways to have one daughter. In each of them, she's got 3 brothers, while the 3 boys each have 2 brothers. Score 12 for the girls, 24 for the boys.
There's six ways to have two daughters. In each of them, the 2 girls each have 2 brothers, while the 2 boys each have 1 brother. Score 24 for the girls, 12 for the boys.
There's four ways to have three daughters. In each of them, the 3 girls each have one brother, and the boy has none. Score 12 for the girls, zero for the boys.
There's one way to have four daughters, but obviously nobody has a brother.
Add up the scores, and you'll see a tie at 48.
(EDIT: Bogus expected value removed after re-reading.)
So brother and sister goes to boy. Brother brother stays brother brother.
Not sure on percents though. It might not override the issue of correlated data and selective breeding.
(Edit - actually now I try think about it this is wrong since now there's also one less women in the world, the mens average also changes)
sensible answer is 3 down with half as many upvotes.
Answer #3 is right far too often for my liking. And sometimes answer #4 brings up corner cases the others missed.
Because you're wrong.
If we take some distribution over size n of the families, say a distribution where family size is either 1 or 2, then the 1-size families don't contribute (they add a zero to the fraction, dragging the probability down in a systematic way equal for both sexes), and the 2-size families have four equally probable outcomes.
those four outcomes (bb bg gb gg) yield:
two females with no brothers. two females with one brother. two males with no brothers. two males with one brother.
i.e. the average numbers of brothers are equal (1/2 for males, and the same for females.)
The next layer, where n = 3, there are (ggg ggb gbg bgg gbb bgb bbg bbb) 8 combinations, yielding:
three females with no brothers. six females with one brother. three females with two brothers. three males with no brothers. six males with one brother. three males with two brothers.
Average number of brothers for females is (30 + 61 + 32) / 12 = 1. Average number of brothers for males is (32 + 61 + 30) / 12 = 1.
for n = 4, (gggg gggb ggbg gbgg bggg ggbb gbgb gbbg bggb bgbg bbgg gbbb bgbb bbgb bbbg bbbb), there are 16 combinations (yes, these map to binary integers quite nicely):
four females with no brothers. twelve females with one brother. twelve females with two brothers. four females with three brothers. four males with no brothers. twelve males with one brother. twelve males with two brothers. four males with three brothers.
Again, this comes out at an even probability - 48 / 32 = 2 brothers, on average.
By induction, we can say that for any n, the probabilities are equal (if the probability of male and female children is equal).
Thus, for any distribution of family sizes, the probabilities are equal.
---
Real world criticisms:
* males are more likely (~51% of births naturally, higher thanks to sex-selective terminations in China and India.) Thus, outcomes with more males are more likely, which skews towards male-heavy families. While this means both males and females have more brothers, there are more males to count with higher numbers of brothers, so their average goes higher.
* males die more frequently at younger ages, so older people will have fewer brothers than younger people.
In China, there may be more sons, but they're not likely to have brothers.
> * males die more frequently at younger ages, so older people will have fewer brothers than younger people.
Most people still consider dead siblings to be siblings.
The obvious rebuttal: no woman can be her own brother either.
The question sounds like an elegant and abstract question straight out of a mathematical textbook, but to answer it you need to start making assumptions about the the distribution of family sizes, which leads you to family planning and cultural differences. There is no way to solve this problem on a napkin.
Why does everyone try to solve this problem from first principles? The best way to solve this problem would be to look at actual statistics!
So now I wonder: Is there any publicly available census data that's detailed enough to answer this question? Or are there any public genealogical databases that might be helpful in answering this?
Trying to solve the problem from first principles is probably how a lot of the posters came to the conclusion that it wasn't possible. If the problem were possible to solve from first principles, it would have been a waste of time to find/create data, so trying to solve it from first principles is the correct approach given no prior knowledge of the answer.