You Are Beautiful (to at least some people) (2015)
danfrank.ca
danfrank.ca
I found that when we look at the behavior, such as replying to messages on OkCupid or Tinder, selecting a partner in speed dating events, or who people choose to dance with in couples dancing events, women behave differently from men.
The behavior of women generally follows the power law distribution, and the behavior of men follows the normal distribution.
This means that women rate the attractiveness of men based on something that is based on preferential attachment, for example how women believe others rate the attractiveness of the man, whereas men rate the attractiveness of the women on something that is independent of the evaluations of others.
When the pool of potential mates grows, the inequality experienced by most men grows, whereas the inequality experienced by most women stays the same. And the pool s growing due to internet dating.
Considering our median male, that gives us the following quantiles:
> for (q in c(0.01, 0.05, 0.5, 0.95, 0.99)) { print(qnorm(q, 5.9, 0.4)) }
[1] 4.969461
[1] 5.242059
[1] 5.9
[1] 6.557941
[1] 6.830539
I appreciate the article's message, but I don't think that those numbers support such a strong thesis:
> Think of the most beautiful people you know; if you are average looking, you will almost certainly be that attractive to some other people you know.
> library(dplyr)
> df = read.csv("~/Downloads/Speed Dating Data.csv", header=TRUE)
> male.df = df %>% filter(gender == 1)
> female.df = df %>% filter(gender == 0)
> analyze = function(df) { df %>% group_by(iid) %>% summarize(mean_attr_o = mean(attr_o, na.rm = TRUE), max_attr_o = max(attr_o, na.rm = TRUE), sd_attr_o = sd(attr_o, na.rm = TRUE)) %>% ungroup() %>% summarize(median_of_mean_attr_o = median(mean_attr_o), median_of_max_attr_o = median(max_attr_o), median_of_sd_attr_o = median(sd_attr_o))}
> analyze(male.df)
# A tibble: 1 × 3
median_of_mean_attr_o median_of_max_attr_o median_of_sd_attr_o
<dbl> <dbl> <dbl>
1 6 8 1.63
> analyze(female.df)
# A tibble: 1 × 3
median_of_mean_attr_o median_of_max_attr_o median_of_sd_attr_o
<dbl> <dbl> <dbl>
1 6.53 9.95 1.52 > for (q in c(0.01, 0.05, 0.5, 0.80, 0.95, 0.99)) { print(qnorm(q, 6, 1.63)) }
[1] 2.208053
[1] 3.318889
[1] 6
[1] 7.371843
[1] 8.681111
[1] 9.791947
> personal_distributions = function(df) { df %>% group_by(iid) %>% summarize(mean_attr_o = mean(attr_o, na.rm = TRUE), max_attr_o = max(attr_o, na.rm = TRUE), sd_attr_o = sd(attr_o, na.rm = TRUE)) %>% ungroup()}
> pdists = personal_distributions(male.df)
> prob_q_thinks_person_above_score = function(df, quantile, score) { (nrow(pdists %>% filter(qnorm(quantile, mean_attr_o, sd_attr_o) >= score))) / nrow(pdists) }
> for (quantile in c(0.50, 0.80, 0.99)) { for (score in c(7, 8, 9)) { print(paste(quantile, score, prob_q_thinks_person_above_score(pdists, quantile, score))) } }
[1] "0.5 7 0.220216606498195"
[1] "0.5 8 0.0397111913357401"
[1] "0.5 9 0"
[1] "0.8 7 0.620938628158845"
[1] "0.8 8 0.296028880866426"
[1] "0.8 9 0.0649819494584837"
[1] "0.99 7 0.985559566787004"
[1] "0.99 8 0.895306859205776"
[1] "0.99 9 0.711191335740072"
Saying what proportion finds the median person in the top percentile requires some more complicated math than simply looking up the width of a bell curve.
Also this blog post is from 2015.
Something or somebody is beautiful if it is pleasant to see it.
Somebody is attractive if you enjoy their company and you have the desire to touch them and engage in activities that would cause mutual pleasure.
These are distinct feelings, even if there exists some correlation between them, mostly because when you consider somebody ugly, you are unlikely to be attracted to them. The reverse is not true, you can consider someone as very beautiful without feeling the slightest attraction toward them.
And there are things that have inherent beauty partly because of their wretchedness and being unpleasant. Prime example would be greek tragedy. You and the protagonist know that it will end horribly and it is almost torture to see it unfold. But still, it is beautiful.
To go a bit deeper on attration and beauty. My personal experience showed me that beauty tends to be a (surprisingly low) threshold requirement for attraction.
You have also used "beautiful" in its generalized abstract sense, when it becomes applicable to things like a beautiful mathematical theorem or a beautiful computer algorithm.
In the second sense of the word, there is no relationship with the attractiveness of a human. In the first sense of the word, I recognize anything beautiful when I see it, without thinking about a reason. In the second sense, I realize that something is beautiful only after an intellectual analysis of it.
Also in the first sense, the word "beautiful" is frequently reused for other sensations than vision, e.g. for a beautiful song or a beautiful fragrance, though it would have been better to have distinct words for these cases.
Yes it's that Gelman, but he's not an author.
In the contemporary world of OLD, imagine you scaled that group size from 20 to 2000. You would see a massive increase in selectivity (around 10 doublings), which accounts for a lot of the issues with OLD, while at the same time offering an alternative: focus on small groups instead of the digital meat market.
And the breeeding age opinions are most critical to the people who most care about attractiveness.
It's nice when the 60 year old grandparent thinks you're attractive. It matters whether the 22 year old clerk thinks you're attractive.
That data is much harder to come by, but it'd show a significant correlation in who people choose to convert with.
Everyone took this to mean that women only want to date the top 20% of men. But it actually just meant women give out lower ratings and that's the only thing it meant.
Live must be though when the 60 year old grandparent tells a grandkid he's fugly!
Or step-grandparent.
1) hazel eyes
2) bigonial width
3) high cheekbones
4) neutral canthal tilt
5) some iq as well but not a lot - i still dont know what p value means lol
1-A pulse
2-Not currently incarcerated
the bar is low