# cbloom rants

## 3/08/2008

### 03-08-08 - 5

There's this girl who lives in my building. Thanks to the thin walls and my windows in the front, I know pretty much everything that goes on in the building - I wish I didn't, I don't want to be a nosey neighbor, but it's impossible to block out of your ears when you can hear people talking. She has a steady boyfriend who lives with her, but he seems to leave pretty regularly, I dunno if he goes out of town for work or what. Literally every single night that he's not here, she has some new random guy over. She leaves the house around 10, usually gets back around 2, and then the new guy leaves in the morning. She's only lived here a few months and has had at least 20 partners that I know of. I assume she's going out to bars alone to find these guys.

She's a living example of a "supervector" in human sexuality. A "vector" here means a connection for disease transmission. If we think about the system of STD's in humans, humans are a bunch of random scattered points that don't transmit disease except through vectors, which are coupling events. I've long held a theory that there exist "supervectors" in the population which faccilitate much wider transmission than simple coupling would indicate. Basically if you have a large population with a constant low coupling rate, STD's cannot survive. Talking about average rates is misleading, because the distribution of sexual partners is very skewed, particularly in women. There are lots of women with 3 or fewer partners, and then a very tiny minority with a huge number of partners, on the order of 100 over a lifetime.

So I just wrote a quick simulator. The simulation works like this : The population is randomly seeded. Old people die when their lifetime is reached. Couples break up when their relationship duration is reached. Single people randomly couple in each discrete timestep. When two singles meet they form a couple with probability equal to the product of their "coupling chances", Ci * Cj.

The population is divided into two groups, the "normals" and the "sluts". In this test the sluts are 10% of the population. The sim runs until steady state is reached and then we look at how the people have coupled. In particular we look at the average # of partners of each group, and the % of the normals partners that are sluts. If the populations behaved the same, the normals should sleep with sluts 10% of the time, since that's the fraction of the population.

Coupling chance captures how "easy" someone is, that when two random normals meet they often don't connect, but if a normal-slut or a slut-slut pair happens they are far more likely to engage. Relation duration captures the fact that normals tend to commit and stay together more, so that once the population is paired up, the single people left out are more likely to be sluts.

Here are the results :

```
pop = 90/10
pop 1000
life 100
group 0 = normals
group 1 = sluts

Run 1:
coupling chance variation :
both relation durations = 10

couple0, couple1, partners0, percentslut, partners1
0.100, 2.500, 2.114 , 42.873% , 9.134
0.150, 1.667, 3.079 , 28.150% , 8.966
0.200, 1.250, 3.981 , 20.873% , 8.785
0.250, 1.000, 4.766 , 16.549% , 8.600
0.300, 0.833, 5.441 , 14.378% , 8.291
0.350, 0.714, 6.017 , 12.847% , 8.006
0.400, 0.625, 6.495 , 11.632% , 7.744
0.450, 0.556, 6.904 , 10.729% , 7.492
0.500, 0.500, 7.255 , 10.001% , 7.254

Run 2:
both coupling chances equal 0.5
relation duration1 = 5
relation duration0 variation :

duration0, partners0, percentslut, partners1
10, 7.734 , 13.503% , 10.905
15, 5.973 , 15.854% , 10.223
20, 5.013 , 17.773% , 9.790
25, 4.409 , 19.213% , 9.467
30, 3.958 , 20.419% , 9.201
35, 3.701 , 21.425% , 9.054
40, 3.413 , 22.322% , 8.857
45, 3.158 , 23.198% , 8.677
50, 3.046 , 23.793% , 8.579

Run 3:
coupling chance 0.25,1.0
relation duration1 = 5
relation duration0 variation :

duration0, partners0, percentslut, partners1
10, 5.293 , 22.934% , 14.228
15, 4.439 , 25.573% , 13.835
20, 3.916 , 27.523% , 13.544
25, 3.565 , 29.236% , 13.346
30, 3.311 , 30.550% , 13.183
35, 3.112 , 31.635% , 13.052
40, 2.951 , 32.528% , 12.925
45, 2.839 , 33.321% , 12.842
50, 2.754 , 33.699% , 12.768

Run 4:
coupling chance : 0.25,1.0
relation duration : 33, 5
slut population fraction variation :

slutfrac, partners0, percentslut, partners1
0.050, 2.814 , 18.761% , 12.409
0.100, 3.188 , 31.200% , 13.099
0.150, 3.526 , 40.320% , 13.640
0.200, 3.844 , 47.589% , 14.080
0.250, 4.148 , 53.546% , 14.454
0.300, 4.441 , 58.587% , 14.763
0.350, 4.714 , 62.880% , 15.026
0.400, 5.000 , 66.790% , 15.271
0.450, 5.282 , 70.379% , 15.481
0.500, 5.565 , 73.629% , 15.671

```

The sim confirms what you would obviously expect, which is that a normal is much more likely to have sex with a slut than their small fraction of the population would suggest. The average partner counts here for sluts is not even that extreme, obviously if I crank up the promiscuity of the sluts the numbers get even more radical.

One thing that may be surprising is that even with only 5% of the population being sluts, 20% of the normals partners are sluts. Also even with equal coupling chances, relation duration alone creates a big bias.