The funny thing about supply and demand views is coming up with the same metric of success. What is success for a woman versus a man varies differently and also the “quality” of the partnering also differs.
My favorite example is of course dating and the fascinating thing is from what I have read things stay even as long as you stay within a 54:46 ratio; however, once it passes 55:45 ratios the mix gets nasty. Why? The above statement.
Take a college with 100 people, 55 women and 45 men. Lets pretend everyone “wants” a relationship of some type where it encompasses sex and some version of commitment. Since 10% of the population on average is not heterosexual, lets take that out of the mix for now …. so 6 women and 4 men get yanked and they are happy together ….. we are now 49 to 44.
Now comes the QoS issue, 10 guys walking into the bar, a woman may show interest in 4 and have interest in sex with 2. Men on the other hand may show interest in 8 and have interest in sex with 7.
So in this instance 49 women on average 18 of the guys and rounding up will want to have sex with 9, while guys will be interested in 40 and will want sex with 35 of them. Things are great for women …. not so much …
Why? Using the Nash assumption it is far worse. Take the assumption that 9 will mate up on demand whenever or however, leaving 40 and 35 left. That leaves an equal amount “9” that maybe immediately possible if things are right …. 31 and 26 ….
18 people who maybe happy, leaving 5 completely out of the mix and the remaining 26, ready to serve up the demand.
That means those guys that can have it on demand actually has a 200–300% chance at any moment, which means you have a logarithmic demand curve. Almost log “normalish” in design.
So how do you create an app that has to handle two or three levels of supply/demand requirements, when a 5% skew can generate logarithmic demand on demand?
Humanity did create that app …. it was called religion and strict morality codes, which society just has broken down …. :-)
I don’t know if that “intellectual” math example defines the near impossibility of creating an application for something that can be a nasty thing to define ….