Artificial intelligence (AI) may not be programmed to love, but it can determine the top predictors for a happy relationship.
In a first-of-its-kind study, researchers conducted a machine learning analysis of over 11,000 couples and found romantic success is achieved when partners believe the other person is fully committed.
The technology revealed other elements including feeling close, appreciated and sexual satisfaction all lead to a successful partnership.
On the other hand, the data also showed factors that run the risk of a doomed romance – depression and insecure attachment.
The study was conducted by a team from University of California and other researchers around the world, who analyzed 11,196 couples across 43 distinct self-reported datasets.
Western Psychology professor Samantha Joel said: ‘Satisfaction with romantic relationships has important implications for health, wellbeing and work productivity.’
‘But research on predictors of relationship quality is often limited in scope and scale, and carried out separately in individual laboratories.’
Joel and her team used machine learning to sift through the massive collection of predictors with the goal of determining the most reliable factors for relationship satisfaction.
After feeding the AI data, it produced relationship-specific predictors like ‘perceived partner commitment,’ ‘appreciation’ and ‘sexual satisfaction’ account for nearly half of variance in relationship quality.
Individual characteristics, which describe a partner rather than a relationship, explains 21 percent of variance in relationship quality.
The top five individual characteristics with the strongest predictive power for relationship quality are ‘satisfaction with life,’ ‘negative affect,’ ‘depression,’ ‘avoidant attachment’ and ‘anxious attachment.’
‘Relationships-specific variables were about two to three times as predictive as individual differences, which I think would fit many people’s intuitions,’ Joel said.
‘But the surprising part is that once you have all the relationship-specific data in hand, the individual differences fade into the background.’
In modeling-simulation terms, the individual differences did not seem to regulate or moderate the relationship-specific variables.
Paul Eastwick, a researcher at Northwestern said: ‘Who I am’ doesn’t really matter once I know ‘who I am when I am with you.’
Joel notes she was surprised the study showed that one partner’s individual differences predictors—like life satisfaction, depression or agreeableness—explained only 5 percent of variance in the other partner’s relationship satisfaction.
‘In other words, relationship satisfaction is not well-explained by your partner’s own self-reported characteristics,’ she said.
However, that does not necessarily mean that a person’s choice of romantic partner is unimportant.
‘Partners may help to shape the relationship-specific processes—such as conflict, intimacy, and perceived partner commitment—that do seem to be so important for relationship maintenance,’ Joel added.