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Afterward, the participants rated their interactions, indicating level of interest in and sexual attraction to each person they met.
Joel and her colleagues used a cutting-edge machine learning algorithm to test whether it was possible to predict unique romantic desire based on participants’ questionnaire responses and before the individuals met. They found it was possible to predict the overall tendency for someone to like and to be liked by others — but not which two particular people were a match.
The study, which used speed dating data, found a computer could predict who is desirable and how much someone would desire others — who’s hot and who’s not — but it could not unravel the mystery of unique desire for a specific person.
“Attraction for a particular person may be difficult or impossible to predict before two people have actually met,” said Samantha Joel, a University of Utah psychology professor and lead author“A relationship is more than the sum of its parts.
“We found we cannot anticipate how much individuals will uniquely desire each other in a speed-dating context with any meaningful level of accuracy,” Joel said.
“I thought that out of more than 100 predictors, we would be able to predict at least some portion of the variance.
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There is a shared experience that happens when you meet someone that can’t be predicted beforehand.” The study, “Is Romantic Desire Predictable?
Machine Learning Applied to Initial Romantic Attraction,” was published today in the journal . Eastwick of the University of California, Davis, and Eli J.
Finkel of the Kellogg School of Management at Northwestern University.
Joel also will present the findings at TEDx Salt Lake City on Sept.