The Metropolis blog

Imagine that attractiveness could be scored from 1 to 10, measuring everything that makes a person attractive: looks, wealth, personality, intelligence, etc. You'd want to be a 10, right? The world would be your oyster: you could date anyone you wanted, and you'd never be lonely. Well, maybe not ... Sexual attraction is driven by our lizard brains, and they don't lower their standards. So if you were a 10, you probably wouldn't be attracted to anyone lower than a 9. But that's not so bad is it? Dating 9's and 10's sounds awesome. The tyranny of the normal distribution The problem is that 9's and 10's are pretty thin on the ground. Many human characteristics follow a normal distribution , a.k.a. a bell curve . Intelligence does. Height does. Weight does. So it's reasonable to assume that attractiveness does too. If that's the case, there are a lot of people rated 4, 5, 6, or 7, but relatively few rated 1, 2, 9, or 10. A quick look around a sho

The term assortative mating  refers to the tendency for people to partner with someone similar. Similar in terms of things like height, ethnicity, religion, language, education, and socioeconomic background. But how much of this is due to innate preferences, and how much to limited opportunities? An example of innate preferences would be if women prefer men who are taller than them (which appears to be the case). Then tall women would end up with taller men and short men with shorter women, so couples would (on average) tend to be of similar height. But consider a scenario where people's main opportunity to socialize is at a local church. Many people would meet their partners there, who would be of the same religion. In this case their similarity would be due to limited opportunities. They might have an innate preference for — or be neutral to — other religions, but have fewer chances to meet those people. With endless choice, who do we choose? So, what happens in the world of unli

What's a fair price to pay for something? In particular, for something you aren't familiar with? In an ideal world you'd shop around to see a range of prices. But that isn't always practical: you might be in a hurry, or maybe the price isn't high enough to be worth the effort. In a situation like this you might be influenced by  anchor pricing . Dropping an anchor Imagine you've walked into a liquor store to buy a bottle of tequila. What's a fair price to pay? The store may have placed bottles of a popular brand in a prominent location, and let's say they're $40 each. That could become your anchor price. Then you might be willing to pay $50 for a premium brand, or $30 for a generic one. Compared to a shop down the road they might be over-charging by $10 a bottle, but the prices seem  fair because your expectations have been anchored at $40. Deploying decoys A related practice is known as decoy pricing . Imagine there was a bottle of generic tequila f

It's fun to see how science fiction influences real-world technology. Sometimes it simply provides a name for a new concept, such as cyberspace , robot , waldo , and the metaverse . Other times it serves as an inspiration that leads to an actual breakthrough, such as talking computers and direct neural interfaces. Either way, it can point to a possible future. Movies and TV So what does science fiction have to say about dating? Here are some shows where dating played a part. Black Mirror . In the episode Hang the DJ , the characters find themselves in a strange world where they're assigned to random relationships, each with an expiration date.  Spoiler alert.  They're simulated characters in a simulated world, and the simulation runs a thousand times with minor variations to see how often they end up together, thus calculating a compatibility score. As far as dating technology is concerned, this is the gold standard. You know two people are compatible because they were  com

In your online journeys you have probably encountered recommendation algorithms . Amazon will tell you that Customers who viewed this item also viewed . YouTube will automatically queue up another video that it thinks you'll like. Instagram will suggest new people to follow. Many of these algorithms use an approach called collaborative filtering , where recommendations are based on the selections of people who have similar tastes to you. The best known of these techniques is  matrix factorization , used by the team that won the Netflix Prize in 2009 — which is why it's often called the Netflix recommendation algorithm . Dating recommendations Could collaborative filtering be used for online dating, to recommend people that you'd be attracted to? There's no reason why not. There's a spreadsheet floating around with data from dozens of speed dating events. With a bit of effort you can apply collaborative filtering to answer the question: given the first N rounds of

  In 1878, when Francis Galton discovered how to overlay photographic images, he observed that an averaged  face is more attractive than an average  face. In other words, averaging together lots of faces will result in a face that's more attractive than the average (or median) face in the original set — although not as attractive as the most attractive one. The effect was re-discovered in  1990 , and, ever since, researchers have speculated as to why. However, a problem with these studies is the way the faces are "averaged". Typically, it means calculating the average pixel values of the faces. But unless you first align the faces, all you get is a pinkish-brown blur. So researchers first resize and align the images so the eyes and mouths line up. Highlight the features, hide the flaws The result is faces where the eyes and mouths are clearly defined, and everything else is somewhat blurry. Coincidentally, this is what women do when they put on makeup. Or early movie dir

If you exclude Chinese firms, the world's biggest tech companies are all based in Silicon Valley (San Francisco Bay Area) or Seattle. The trillion-dollar club are Apple , Bay Area Amazon , Seattle Microsoft , Seattle Alphabet (Google et al), Bay Area Meta (Facebook et al), Bay Area And there are plenty of other Bay Area firms in the next tier, such as Twitter, Uber, and Netflix. Now let's look at the world's biggest adult-related tech firms. Match Group  (Tinder, Hinge, et al), Dallas, Texas Bumble , Austin, Texas MindGeek  (Pornhub, YouPorn, et al), Montreal, Canada OnlyFans , London, UK Tumblr  (pre-2018 family-friendly reboot), New York These are all multi-billion-dollar businesses, but notice that none of them are located on the US west coast. OK, technically Bay Area firms like Twitter and Reddit tolerate adult content, but it's a small part of their traffic, and they're rather coy about it. It's not like a Silicon Valley venture capitalist to pass up a