This is Why Science Can't Predict Love According to Science (This Will Make You Think Twice About Dating Sites)

There are two camps when it comes to modern science and dating/sex. The one camp, the one I belong to, is that science can describe and find which partners are best with which people. The other is that love is indescribable and will forever be beyond science.

The folks in the second camp have gotten a feather in their cap with the release of a new study that provides that evidence that science, as it is now, cannot predict which people will be attracted to each other. University of Utah researchers led by Samantha Joel created a detailed personality questionnaire similar to that commonly found on dating sites such as Match.com and OkCupid and gave it to 350 heterosexual college students.

The research did reveal that people can tell when a meeting or date went well, but the list of partner preferences self personality descriptions failed utterly to provide meaningful feedback on who was a good match and who wasn't. Even such traits as 'good-looking' and 'warm' were not effectively paired between two people describing themselves as such.

Before those of you who believe that love is beyond science throw a party, it should be noted that their findings aren't too surprising. Two people meeting initially, even briefly, is an impossibly complex interaction involving thousands of subtle combinations of expression, body language, tone, and response. These are things that can't be predicted or accounted for in a written questionnaire.

However, with the advent of machine learning and less places to hide from the all-seeing eye of technology, it might one day be possible for a computer monitoring you to gain enough information to predict a winning mate with a high degree of accuracy.

That time is still a long ways off, but I'm not sure that it's safe to say that love is totally beyond science, just yet. For the time being, though, you are free to make all of the poor romantic decisions you like. You'll do no worse than today's algorithms.

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