Speed dating predictions

Speed dating is one of the best ways to research human attraction, because it's based on revealed preferences rather than surveys, and everyone has a strong incentive to give truthful answers.

If you'd like to do your own analysis of speed dating data, there's a freely-available spreadsheet from a Columbia University study, with the results of over 4000 dates. The dates occurred at events with (typically) 10 male and 10 female participants, who "dated" everyone of the opposite sex in quick succession.

As well as the outcomes of the dates, the spreadsheet contains (somewhat patchy) demographic data, such as the age, undergraduate major, race, and some personality attributes of the participants.

For an in-depth analysis of the data, have a look at this Kaggle notebook.

A simple analysis

There are lots of ways to analyze the data, but a simple hypothesis to test is: given the results of previous dates, can you predict how people will decide on the next date? So, at an event with 10 dates, can you predict the decisions in round 10 from the decisions in the previous 9 rounds?

It turns out you can.

The idea is to calculate two attributes for each participant: their popularity and their pickiness. Their popularity is the fraction of dates where their partner said yes to them. Their pickiness is the fraction of dates where they said no to their partner. Then in future dates you assume they'll say yes if their partner's popularity is at least as high as their pickiness.

Using this simple rule — and no other demographic data whatsoever — you can predict the decisions in the final round with 75% accuracy. And you can predict the two-way matches with 80% accuracy (although, to be honest, that's not much better than just predicting no for each date, since most dates are unsuccessful).

If attraction were truly in the eye of the beholder, and people's preferences were uncorrelated, the accuracy would only be 50%. Clearly that's not the case, and you could almost certainly improve on 75% accuracy by using demographic data and more rounds of dates.

So, at least when it comes to initial attraction, humans are fairly predictable.

Detailed results

For the data geeks ...

  • Male decisions: accuracy = 76.3%, precision = 0.838, recall = 0.695 (true pos: 98, true neg: 102, false pos: 19, false neg: 43)
  • Female decisions: accuracy = 74.0%, precision = 0.681, recall = 0.521 (true pos: 49, true neg: 145, false pos: 23, false neg: 45)
  • Matches: accuracy = 81.7%, precision = 0.636, recall = 0.259 (true pos: 14, true neg: 200, false pos: 8, false neg: 40)

Comments

Post a Comment

Popular posts from this blog

Are average faces more attractive?

Image
  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
Read more

The geography of adult tech

Image
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
Read more

Race and attraction

Image
For many years the online dating site OkCupid produced the OkTrends blog, where they analysed their data to explore people's dating habits. In 2009 they looked at racial preferences — the original blog has disappeared, but a copy of the article can be found here . The article analysed the data through the lens of American racial politics, and, perhaps unsurprisingly, concluded that their users are racist. However, there are a few problems with their analysis. First, they measured preferences by looking at how frequently people responded to initial messages. However, this measures not just the preferences of the recipient, but the judgement of the sender. For example, a sender who mostly contacts attractive people will almost certainly get a lower response rate than one who contacts unattractive ones. Does judgement vary by race? We have no way of knowing. Second, much of their analysis is based on men responding to women's messages. But in online dating — as in the real world —
Read more