Is romantic desire predictable? Machine learning applied to initial romantic attraction

Joel, Samantha (2018). Is romantic desire predictable? Machine learning applied to initial romantic attraction. [Data Collection]. Colchester, Essex: UK Data Archive. 10.5255/UKDA-SN-852716

Data description (abstract)

We used machine learning to test how well such measures predict people’s overall tendencies to romantically desire others (actor variance) and to be desired by others (partner variance), as well as desire for specific partners above and beyond actor and partner variance (relationship variance). Close relationships theoretical perspectives and matchmaking companies suggest that initial attraction is, to some extent, a product of two people’s self-reported traits and preferences. In two speed-dating studies, romantically unattached individuals completed over one hundred traits and preferences identified by past research as relevant to mate selection. Participants then met one another in a series of four-minute speed-dates. Random forests models predicted 4-18% of actor variance and 7-27% of partner variance, but, crucially, they were unable to predict relationship variance using any combination of traits and preferences reported beforehand. These results suggest that compatibility elements of human mating are challenging to predict before two people meet.

Data creators:
Creator Name Affiliation ORCID (as URL)
Joel Samantha University of Utah
Name Affiliation ORCID (as URL)
Eastwick Paul University of California, Davis
Finkel Eli Northwestern University
Sponsors: N/A
Topic classification: Psychology
Keywords: attraction, dating, speed-dating, romantic desire, romantic relationships, machine learning, statistical learning, random forests, ensemble methods
Date published: 19 May 2017 09:43
Last modified: 12 Sep 2018 09:01

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