# Sexual Market Value and the Pareto Frontier

I have recently been reading F. Roger Devlin’s book, “Sexual Utopia in Power”, as well as several manosphere blogs, and these readings set about the gears and sprockets whirring in my head. The concept of sexual market value (SMV) is interesting to me, as it describes an enormous amount of the male-female interactions around us. Reading on SMV though, shows a distinct lack of mathematical precision on the part of the writers. In this blog post, I have tried to describe a firm mathematical procedure for calculating the SMV for each member of a population, based on a set of quantitative criteria describing each person.

# The Concept of Sexual Market Value

Sexual market value (SMV), as explained by a variety of manosphere blogs, is simply a measure of the “quality”, or “attractiveness” of a given person, in a sexual sense. Typically, SMV is scaled on a score from 0 (least desirable) to 10 (most desirable). While most manosphere blogs typically only report SMV values as integers, there is no reason that SMV cannot be a continuous variable, e.g. 8.5 or 7.3.

For women, sexual market value is almost wholly determined by:

• Body fat percentage
• Race
• Age

For males, sexual market value is based on:

• Good looks
• Race
• Height
• Musculature (which is indirectly related to body fat percentage)
• Income
• Wealth
• Social dominance. Good examples of socially dominant males would be the chairman of a major corporation, a wealthy entrepreneur, a high-ranking military officer, a congressman, or the President.

For example:

• A female with very low SMV is above the age of 35 and a body fat percentage of 30% or higher.
• A female of very high SMV is in the age range of 18-25 and a body fat percentage of 21% or less.
• A male of low SMV has a body fat percentage of 25% or higher, is short, has little to no wealth, and a works a bottom-rung dead-end job (or is unemployed).
• A male of high SMV has a body fat percentage of 12% or less, is 74 inches (188 cm) or taller, has a high income, a large amount of wealth, and has a socially dominant position in society.

# More Detail on Male versus Female Sexual Market Value

For males, all of the discussed variables are quantitative, with the exception of “good looks” and “social dominance.” Looks could probably be quantified using computer-vision software, along with a large dataset of rankings supplied by females in a rigorous observational study. “Social dominance” could be quantitatively assessed in a variety of ways. One way would be compute the total salary of his subordinates at his job. For example, a tech entrepreneur who bosses around 10 people who all make \$100,000 a year is more socially dominant than a McDonald’s manager who bosses around 20 people that all make minimum wage. Someone like President Obama is extremely socially dominant; he has a huge number of subordinates, and they all command high incomes.

President Obama is an example of a high-social dominance male.

For females, income and education level are irrelevant to sexual market value (though they may be important with regards to marriage market value). As I said before, a woman’s SMV is almost completely determined by her age, race, and body fat percentage.

# The Distribution of SMV Among Males and Females

The distribution of SMV throughout the genders is not known, though it can be qualitatively described. Some bloggers describe SMV as a normal distribution, however, I do not believe this to be accurate. Let us examine the case for males. Females place a great deal of weight on a male’s ability to provide, and so income and wealth are heavily weighted attributes. Bearing this in mind, the distribution of male SMV is likely lognormal, since income and wealth are distributed lognormally. Regardless of which exact distribution is used to model male SMV, it would probably be heavily weighted toward the lower values, with a rapidly declining number of 7’s, 8’s, and 9’s.

The lognormal distribution. This distribution is characterized by a fat left-end, with a very thin right-tail.

Female SMV, being almost wholly derived from genetics and age, likely follows a different distribution, but is probably similar in shape; fat, ugly women are legion, while slim, attractive girls that make the 7-9 category are comparatively rare. I do not think a normal distribution can really describe female SMV, since this predicts that women of score 3 or lower are rare, when they are actually legion. Again, a lognormal distribution is probably more appropriate.

# A Definition of SMV Based on Non-Dominated Sets

It is unlikely that a male or female can ever possess the most desirable value of all of the traits at once, so a tradeoff exists between them. I have already discussed dating and the Pareto frontier previously. The concept of the Pareto frontier applies nicely to the concept of SMV. We know intuitively that a male who is deficient in one area but outstanding in another area can still command a respectable SMV (e.g. an obese multi-billionaire). A person’s (male or female) SMV is directly linked to the number of people they dominate in the set of people of their gender. For example, let us look at the figure below:

An example population of males being compared. The most sexually-attractive males are in the lower-left hand corner. Middle-quality males are in the central set. The worst-off males are in the upper-right hand corner

When viewing the above figure, we will take the assumption that a lower value of an objective is better. While males are ranked on the basis of many attributes, we have, for ease of visualization, considered the case of only two objectives. The non-dominated set (the first “front”) lies in the lower left-hand corner of the plot. These are the most sexually desirable males in the population; they are “Pareto optimal.” Among these three males, there is always some “trade-off” between the three of them; a decrease in objective 1 always implies an increase in objective 2. This property makes them all part of the same set (“front” or “frontier”). They are however, strictly superior to all the males in the second front and the third front; they have lower values for both objective 1 and objective 2, with no trade-off necessary. In an extremely large population, the number of fronts would be very large, with a relatively small number of males occupying each front, leading to more or less a continuum of fronts.

Let us define the “domination number”, $K_i$, as the number of men strictly inferior to the $i^{th}$ male on the plot. Given $K_i$ for a given male, his SMV can be computed. The most straightforward way would be to take the proportion of men a particular male strictly dominates, and multiply by 10. For a population of $N$ males, the equation for the SMV of the $i^{th}$ male is written as:

$SMV_i = 10\times\frac{K_i}{N}$

A man who dominates 95% of the men in the population, would be ascribed an SMV of 9.5. A man who dominates 10% of the men in the population is given an SMV of 1. In the figure above, there are three fronts. There are three males in the non-dominated set (front #1, again in the lower left-hand corner of the plot). There are 15 males total in the plot ($N=15$). Since these three males dominate 12 males, we have 12/15 * 10 = 8. Therefore, the males in the non-dominated set, while differing in individual attributes, would all be ascribed an SMV of 8. The males in the middle front outrank 3 males, so they have an SMV of 3/15 * 10 = 2. The males in the upper-right front don’t outrank anyone; they have an SMV of zero.

This way of defining mathematically defining SMV also has the nice property that people are scored relative to their given population. This matches experience. In elite circles, a handsome male with a high income might be considered to have an SMV of only 3 or 4, simply because there are so many other more handsome, higher-earning males to compete with. If this male were to move to a social circle where most other males are overweight and broke, his SMV would skyrocket. This also describes the behavior that beautiful women engage in of surrounding themselves with fat, ugly friends at clubs – it makes themselves look even more desirable by comparison. Also, defining SMV in this manner makes it impossible to ever achieve a score of 10; the maximum possible score converges to 10. If the non-dominated set contains a single individual, that person is the most sexually dominant in the population. For this person, their SMV is:

$SMV = 10\times\frac{N-1}{N}$

Where $N$ is the number of people of their gender in the population.

The extension to female SMV is easy; all that is different is the input data.

# Calculation of SMV for a Large Population in Practice

Given a large population of males and females, with appropriate data on all of them, the non-dominated sorting algorithm could be used to identify all of the fronts in the data set, with an SMV score applied to the members of each front. I have not read into the implementation of the non-dominated sorting algorithm, but a very large data set of males and females may be computationally taxing to sort. I was able to find this MATLAB code here for the non-dominated sorting genetic algorithm. This code contains a function, “NonDominatedSorting”, which can be used to identify all of the fronts in a given input set. Crude time trials show though, that for a large dataset, the function is rather slow. For a large population of hundreds of thousands (or even millions) of people, sorting the list of fronts is likely to be intractable without serious parallel computing power.

# Perspective on the Longevity of SMV

Sexual market value in dating and life is sort of like a game of Texas Hold’em, but the chips are initially distributed in different ways depending on your gender. Young women are given an enormous stack of chips at the beginning of the game. They can bet as much as they wish. Bet big, win big, lose big. Life is one long party of laughs, drinks, big wins, and big blowouts. But once a woman becomes old – the chips dry up. There is no “faucet” available to replenish her stores. Without any money, the casino doesn’t want her around anymore. They throw her ass out into the cold. On the other hand, men start off with a much more modest chip stack. A man has to bet smart and play smart to grow his chip stack. Life is not one big party – his nose is to the grindstone, focusing on getting wins and getting those numbers. After a while, this man has grown his stack into a princely sum. He cashes out, leaving the casino holding the bag. Not only does he have a lot of cash in his pocket – but the wisdom he gained along the way to get it will make sure he doesn’t give it up easily.

We can make an important observation regarding the criterion that determine male and female SMV: men have a longer shelf life than women. Also, men have far greater prospects at increasing their SMV than women do. Women’s SMV, as already discussed, is mainly determined by chance of birth and age. A woman’s SMV declines with age, decaying essentially to zero as she ages. As a woman ages, the most she can do to maintain her SMV is to exercise regularly and keep her body fat percentage at an attractive level. Most other forms of investment are unlikely to have any effect on a woman’s SMV. I can recall multiple times entering a shopping mall, walking through JCPenney, and seeing old or fat women shelling out big bucks on make-up and perfume, when what they really needed was a treadmill (or a time machine).

Men however, due to the magic of compound interest, observe a substantial increase in their wealth as time goes on. Incomes also tend to increase with time as the male increases his experience level. As I have already state, income has no effect on a woman’s SMV (and even if it did, women tend to earn far less than men anyways).

## 4 thoughts on “Sexual Market Value and the Pareto Frontier”

1. LeShitlourde says:

Do you have any mechanistic rationale as to why a normal distribution would not apply here?

I guess I just have it mentally ingrained that the default statistical distribution would be a normal distribution unless there is a reason for it to be otherwise?

Also I am a bit confused on the log-normal distribution: is this saying that people are concentrated at low SMV with a larger tail on the upper end? Or are you saying the reverse?

I am not a statistical genius or anything though, and they never really teach you what applies in which circumstance.

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• Hi,

No, I have no actual mechanistic rationale as to why a normal distribution would not apply. I just think, judging from what is observed, that a lognormal distribution is probably more likely (but without a large, high-quality data-set, it is difficult to prove).

A normal distribution doesn’t always describe everything though. As I said in the blog post, income and wealth are distributed lognormally. My memory is somewhat foggy, but I believe simulation studies have been done that indicate a “rich get richer” mechanism can explain the appearance of power-law distributions.

Regarding the orientation of the lognormal distribution, I am saying that poor (low) SMVs are where the hump is located, and good (high) SMVs are where the tail is.

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