I’m Dr. Samantha Joel. My team and I use AI to predict the relationship satisfaction of 11,000 couples - AMA!

As a single person looking for a long-term relationship partner....do the results of this study mean I could be happy with literally anybody? Aren't there some people who would be more likely to appreciate me and act in ways that show me they're committed to our relationship? (and vice versa)

That’s really hard to say. One of the limitations of the project is self-selection – we only looked at couples who are already together. We didn’t, say, pair people at random. If we had, we might have found much stronger partner effects. So, there may very well be plenty of people who you wouldn’t match well with, but those people are selected out by the time you enroll in our study.

What the results do suggest is that by the time you’re in a sufficiently established partnership to enroll in a research study together, your partner’s traits aren’t very important anymore.

Really, we need a lot more research on the early relationship stages—how do these relationship dynamics form in the first place?—to produce a satisfying answer to your question.

Hi! Thanks for doing this AMA.

What would you say is the biggest takeaway for a couple based on the results of your study? And is there anything a single person should take from it while looking for a partner? If I'm understanding it correctly, it looks like a lot of the factors that lead to success are things it might not be easy to evaluate until you've actually been in a relationship with someone for a bit.

Thank you!

I think the biggest takeaway, to paraphrase my old friend and colleague Geoff MacDonald, is that the person you choose may not be as important as the relationship you build. As a culture, we put so much emphasis on choosing the right person. These results suggest that it’s really more important to be the right person. To create the conditions that will allow a relationship to flourish.

In terms of your point about evaluations, this is something I’ve spent a lot of time thinking about myself. Can a relationship be objectively evaluated—are some partnerships inherently better than others--and if so, when do these objective criteria first come online? This is somewhere my students and I would really like to take our research next. We want to recruit couples in brand new relationships and study how they evaluate each other for compatibility and fit, and how those evaluations change as the relationship develops.

We were supposed to launch the study in March, but it got stalled due to COVID. Hopefully soon we’ll be able to open the lab up again, and I’ll have some more concrete answers for you.

Why do you think that it's so difficult to predict which relationships will work out well, and which won't? (whether using AI or not)

Thanks for doing the AMA!

That’s a great question. I think when it comes to relationship quality and longevity, there are a lot of chaotic processes at work that make long-term prediction difficult. Stressors and life events that come up, idiosyncratic experiences that you might happen to have with your partner, other people who may enter or exit your life and who give you different perspectives and ways of thinking about the partnership, etc.

So we can predict the aspects of the relationship that are stable, but they also change over time in unpredictable ways. I think that’s because the changes are largely driven by these kinds of environmental and contextual factors that are very difficult to measure, let alone predict.

Dr. John Gottman has been able to predict divorce with 94% accuracy. Check him out, his books are fantastic!

I can't speak to Gottman's books, which I'm sure are fantastic. But, from what I can tell, his claim that he can predict divorce with 94% accuracy comes from this study of 52 couples, published in 1992: https://search-proquest-com.proxy1.lib.uwo.ca/docview/614305792?accountid=15115

13.5% of the sample had divorced over a three-year period, or 7 couples. After the data were already in hand, the researchers used a discriminant function analysis with nine predictors to predict which couples divorced, with 93.6% accuracy.

This model suffers from a statistical problem called overfitting. With a small sample size, and a technique that doesn't use any kind of cross-validation, you can essentially keep adding predictors until you explain close to 100% of the variance. We call that a saturated model. Almost all the variance has technically been "explained", but only for the very specific sample that the model was built on. If I went and recruited 52 new couples, and applied this exact same model to those data, the accuracy would likely be much less - likely closer to 86.5% (which is the baseline here - you get 86.5% accuracy if you simply predict that no one gets divorced).

Tldr Although I have lots of respect for Gottman, I am incredibly dubious of that 94% claim.

Did any of your couples include arranged marriages?

I ask this because my husband and I both come from cultures with a high degree of parental/community involvement in matchmaking. Without even planning to do so, we did effectively the same thing to ourselves. I told him on our first date (set up by our friend community) that I was only interested in someone who was serious about marriage/kids and he agreed. We operated under the idea that we would do our best to build a healthy relationship that would end in marriage and I think that mindset is key to us having such a happy, healthy, and satisfied relationship now. I would be curious to see if other couples who were in either arranged marriages (willfully) or had a very strong marriage goal early on had the same results as couples who did not.

Not to my knowledge. Our data were from Canada, the US, the Netherlands, New Zealand, Israel, and Switzerland. Very Western-centric, as you can see, so they don't lend themselves well to cross-cultural research questions.

Arranged marriages have always intrigued me, and a long-term research goal of mine is to prospectively follow people in arranged marriages and compare their trajectories to the trajectories of self-selected marriages.

The existing literature that I know of on arranged marriage--and it's not a very large literature-- has produced pretty mixed findings. Some studies have compared people in arranged vs. self-selected marriages and found no differences in relationship quality. Some have found higher quality for the self-selected marriages. Some studies have shown different results depending on which marital quality measure you use, or on how you define "arranged". So it's very much a topic in need of further research.

Have you found that the partnerships need to have a similar understanding of what the commitment translates to? For example, putting equal effort into maintaining the home, or equal involvement with children.

Do any of the studies collect information to confirm or deny the reliability of zodiac sign (eastern and western) compatibility?

For participants who had a “type” they were attracted to while dating, did their significant other match that description?

This is one of the more interesting aspects of the findings, IMO – we did not find any evidence for any kind of partner matching predicting relationship quality.

The algorithm we were using detects interactions. So if my traits and preferences match with your traits and preferences to predict relationship quality, we should have picked up on that. For example, if Andrea says she likes extraverted guys, and she’s happy with Tom because he’s an extraverted guy, we should have found that putting Andrea’s desired extraversion and Tom’s own extraversion into the same model would have predicted more variance than either on its own. But that’s not what happens. Combining both partner’s variables didn’t predict more variance than just one partner’s variables. So that goes against the idea of matching, similarity, having a type, etc. If there was any matching going on, it didn’t predict how happy people were with their partners.

Very thought provoking. Have you been able to find evidence that predicts the relationship quality?

And thank you for doing this AMA!

Relationship-specific variables did a great job of predicting relationship quality. Your own perceptions of the relationship--such as your own sexual satisfaction, how much conflict you think there is in the relationship, and how committed you think your partner is--predicted 45% of the variance in your own relationship quality, at the beginning of the study. These same variables also predicted 18% of relationship satisfaction at the end of the study.

And in fact, no other variables added to that total variance explained. Not your traits, not your partner’s traits, and not your partner’s perceptions of the relationship. All of the effects were driven by own judgments about the relationship.

So, basically, if one is in a relationship and they are making the point to perceive themselves as in a happy relationship, they will be.

How much does it matter to the success of the relationship if one perceives themselves positively but the other does not?

That’s a great question. My team and I were surprised that the partner’s perceptions of the relationship predicted so much less variance than own perceptions. Own perceptions of the relationship predicted 45% of the variance in relationship quality, but the partner’s perceptions (measured with the exact same variables!) predicted only 15%.

That difference suggests that there’s a pretty big discrepancy in those ratings--how you perceive the relationship is not necessarily how your partner perceives it. It’s not clear at this point what the implications of those discrepancies are, or where they come from, but that would be a great topic for future research. How can two people be in the same relationship, and disagree so much about what it’s like?

I'm an extrovert and I've been intensely unhappy dating introverts. So this seems to go against my own experiences, because there's not enough in common between us to keep a relationship going, and I don't feel that they care about me enough to compromise (e.g. they agree to attend game night with me once a month vs weekly).

I think this really highlights that self-selection problem I mentioned—your relationships with introverts may not last long enough to be included in a study like this, which means those data are not part of the results. That’s why I really want to see more data on fledging relationships. I’d love to enroll you in a study at the point when you have just started dating an introvert, and ask you about your experiences over those few ephemeral weeks or months that the relationship lasts before it fizzles out. Those sorts of data are so difficult to collect but I think they’re a really important piece of the puzzle.

Well I've been with my introverted husband for nine years. We've decided just recently that separation is probably the best course of action in the future (neither of us want to make such a large decision right now, in the midst of the world being on pause and both of us being depressed about it).

I'm really sorry to hear that, Transplanted_Cactus.

Hello,

Very interesting findings! What would you suggest single people using tinder etc should make sure to find out early / use in their “screening” process for best possible outcomes?

Insofar as our data can speak to this (which is debatable), I would say you want to look for a partner who seems genuinely interested in you, who is good at perspective-taking with you, and who seems to be responsive to your needs. Someone who makes you feel understood, validated, and cared for. If I was a betting person, I would bet on those things.

Thanks for doing this Dr. Joel!

Very interesting research.

What made you think machine learning would be a good way to study the success of romantic relationships?

Well, traditional statistical methods that we use in this field—like regression and multilevel modelling--are really great for delving into the mechanisms or inner workings of a handful of variables. But, they aren’t very good at dealing with a large number of variables at once.

The major advantage of machine learning is that it can handle a very large number of predictors, and tell you which ones are really driving prediction, as well as how well they are performing as a group. So, the goal of the project was to take all of the many many variables that have already been examined in separate studies, and make them directly compete for that variance. Which of these hundreds of measures are most important, and when taken as a whole, how well do they perform?

Really interesting work and I really appreciate the approachable explanations.

Out of curiosity, what kind of machine learning are you using? How many features are you starting with and how are those coded?

We conducted the analyses with Random Forests, using the randomForests package in R. Each dataset was collected by a different team of researchers and therefore had different predictors - typically ~50 variables per dataset, which we manually coded into either features of the self or features of the relationship. We also used the VSURF package to initially pair down the number of predictors in each model.

Got it thank you! Why did you choose random forests?

Key advantages: it can handle a very large number of predictors at once, it's able to capture non-linear effects and interactions, and its use of out-of-bag sampling helps to minimize overfitting issues.

Just for clarity, these aren't advantages that are unique to random forests at all. Instead, with a dataset like yours, any choice of standard classical classifier should have performed similarly. The random forest is nice because it lends itself to interpretability of feature importance through the GINI coefficient, and doesnt require a separate feature selector.

I'm wondering why you called it AI in the post though? In the machine learning community, we wouldnt call this AI. I'm not sure if you're aware, but the public perception that this kind of thing is AI has been harmful to our field.

Our dependent measure was continuous, so this was random forests built on regression trees, rather than classification trees. But yes - plenty of other ML methods likely would have done a fine job.

The decision to put "AI" in the title was made by the media team in order to shorten the title. Although it's technically correct, I do agree with you that it's a stretch. "Machine learning" is a more accurate descriptor.

What would be more useful for growing a healthy relationship? 1 horse-sized duck or 100 duck-sized horses?

Well Dr. MacDonald, taking an academic approach to this question, I would have to say that having 1 horse-sized advisor would likely be more useful than 100 duck-sized advisors.

How do control for the self-reported nature of the data? I would imagine people would be biased in their description of their current relationship compared to past relationships or the prospect of a future one. More plainly, I would expect Ex's to have a largely negative connotation and re-entering the dating pool requires substantial effort; so I may respond more positively about my current relationship.

Absolutely – people tend to hold a lot of positive illusions about their romantic partners, and to perceive their partners in a highly biased way. But, I think I would push back on the idea that this is something that needs to be controlled for or somehow subtracted from the ratings. When we’re talking about relationship quality, really, perception is reality. You’re happy if you think you’re happy! It’s an inherently subjective construct.

I think that’s why own traits did such a better job of predicting relationship quality than the partner’s traits, in these analyses. Your own proneness to things like positive and negative affect are going to shape how you perceive your partner and the relationship, and therefore how satisfied you are with that relationship. To a large extent, we project our own personalities, feelings, biases, etc. onto our partners.

Do these factors change in order of importance with age? Is there any set of factors that predicts divorce?

In fact, age was one of the only demographic variables that performed well in our models. Age contributed to 68% of the models we tested. Now, machine learning is pretty black boxy, so we can’t tell you exactly what age is doing in these models. But it’s quite possible that it’s a moderator of a lot of the other variables—that different variables are important for relationship quality depending on your age.

We did not try to predict divorce or breakups in these models. Other papers have done that though, although not with machine learning. Karney & Bradbury 1995 (https://psycnet.apa.org/buy/1995-36558-001 ) is, I believe, still the most comprehensive paper to date on the predictors of divorce. Le et al 2010 (https://onlinelibrary.wiley.com/doi/full/10.1111/j.1475-6811.2010.01285.x?casa_token=pSw5wWgnZSYAAAAA%3ANGeIEDkDNcUmWWi4XiZN1gXDX4F8zMGP98V_O7sWkaW-Z8N0XZ0IuoJNoaSWAwHlZstwN_18X99JT8WQ) is the best paper on predictors of breakups.

Top predictors of divorce and breakups tend to be global evaluations of the relationship. Variables like how satisfied you are in the relationship, and how committed you are to the relationship. That’s part of why we focused on these outcome variables in our project.

Dr. Joel! Really interesting research, I can't imagine the tenacity needed to collaborate and coordinate with so many researchers.

Looking forward, what variables do you envision accounting for that initial spark between two people, before an established relationship exists?

My colleagues and I looked at this very question in another project, where we applied machine learning to speed-dating data. These data were collected by Paul Eastwick (key player in the current project), and also by Eli Finkel. They had over a hundred measures in that study, which I fed into the algorithms. But, despite that, we found that we could not predict that initial spark at all. Zero variance explained.

https://journals.sagepub.com/doi/full/10.1177/0956797617714580?casa_token=SinsSsmAG6EAAAAA%3Ah1e4KUls_Ohk0ODleHlTLpD7l94PfX0R9GZ2yMVjR--ERRHNwSHkymy7nD1WOeJh3enfqRf-uZvWCA

What’s your 2nd favorite aquatic creature?

Top favorite is whales, hands-down. Second favorite? Gonna go with dolphins. Ceteceans for the win.

If​ you​ were​ to​ give​ a​ teenager​ an​ advice​ about​ pursuing​ AI​ field, which​ courses​ or​ curriculum​s would​ you​ recommend​ both​ bachelor and​ master​ degree?

This I can't say much about, as I took a pretty serendipitous route to learning about machine learning. My background is in psychology, which includes a lot of statistical training but not machine learning per se. I think it's safe to say that you can't go wrong with programming and statistics courses. If you learn some programming environments like maybe R or Python, and learn about some foundational statistical techniques like regression, that should give you a solid basis of knowledge.

Are you against the Gottman research that’s been done and widely used as a relationship predictor? How is your work different and how is it the same?

Thanks!

IIRC, the Gottman findings you're referring to attempted to predict divorce, using coded interactions that were videotaped in the lab. That's pretty different from our project, which predicted relationship quality with primarily self-report variables. So, we can't directly speak to the veracity of Gottman's findings with these data.

I am personally quite skeptical about the claim that divorce can be predicted with 94% accuracy, using any combination of variables. That seems extremely high. The data and code supporting that claim are not available to my knowledge, but I suspect that the models may be quite overfitted to a particular dataset, and would thus have difficulty replicating in a different dataset.

Did you study partners with open relationships? Do you believe that open relationships can be long lasting and fulfilling?

Thank you for all the hard work. It's incredibly intriguing. I'll have a lot to read up on tonight.

This project didn’t really touch on open relationships, but I have done other work in this area. A couple of years ago, one of my students recruited 233 people who were interested in opening up their relationships—but hadn’t done so yet—and tracked them over two months. https://journals.sagepub.com/doi/abs/10.1177/1948550619897157

We found no differences in relationship quality between those who opened up over the course of the study and those who didn’t. We did find increases in sexual satisfaction for those who opened up. This is consistent with other, cross-sectional work on open relationships. So, we don’t have definitive answers yet, but so far, the data are looking promising for open relationships!

What was your methodology for quantifying which factors are most predictive? Meaning, how did you model the data and how did you establish importance of each variable?

The project included 43 longitudinal datasets. Each dataset included a large questionnaire collected at the beginning of the study (different measures in each study). We organized all measures collected at baseline into traits vs. relationship variables, reported by each partner. Then, we put different combinations of those groups of variables into Random Forests models to predict relationship satisfaction and commitment at the beginning vs. the end of the study. In total, we ran up to 42 Random Forest models on each study, then meta-analyzed the results.

The Random Forest algorithm pulls out the most important variables and lists them in their order of strength. It also tells you the total amount of variance explained.

Thanks for the detailed response. Where would one be able to look up the details of the study such as how feature importance was computed (I assume based on decreasing node impurity), if results were cross validated (and how folds were created), and what the predictive performance of the classifiers was? I'm interested since the importance of the variables is only meaningful when the model has good generalization performance. I could not find such details when doing a quick keyword search on the paper.

You can find all of the code and detailed results for each dataset here: https://osf.io/g8tm7/

These are random forests built on regression trees, not classification trees, so feature importance is calculated based on reduction of the MSE. Results were not cross-validated - instead we relied on the models' out of bag performance (essentially, the technique tests each tree on a sample not used to construct the tree).

What do you think of the ‘love languages’ and are there any parallels?

The love languages are a really fun and intuitive concept. Unfortunately the scale on the website is, psychometrically speaking, a mess. One of the big problems with it is its forced choice format. It makes you choose between options in a way that artificially exaggerates your preference for one love language over another.

I saw a talk by a graduate student once who tried to validate a love languages scale and use it in her research. But when she measured the languages with a likert scale, she got a huge ceiling effect. Everyone topped out on most of the languages, e.g., most everyone loves hugs, AND receiving presents, AND quality time etc. Basically, she found that everyone speaks every love language.

How many of the couples reported being unhappy? Because my experience, compared to what you've answered so far, and what I've read from literally thousands of women on a forum in regards to why they are happy in their relationships, has been entirely opposite of what your data is saying.

Most couples were pretty happy, as is typical of relationship samples. But, the responses did cover the full range of the scale, so there were plenty of unhappy couples in there as well.

Hard to say why the results differ from the first-hand accounts you have read. But, the data are the data, and this is what the data showed!

So basically, you guys determined that successful relationships are more likely to be successful? I don't mean to be snarky, but how can you say you are predicting how happy people will be with their relationships by essentially asking them, how happy are you with these different aspects of your relationship? This study comes across as more commentary than prediction. The study would be interesting if you could prove that political idealogy, body type, age, religion, upbringing, personality traits are all predictors of varying degree as to whether a relationship will be successful because those are data points that remain somewhat constant before and after the start of a new relationship, and you could then determine how compatible a couple would be together should they choose to pursue a relationship, but the way I am reading this is that you guys basically asked people how happy they were with certain aspects of their relationship, and then said, "if you are in a good relationship, you are more likely to be happy!" It should not have taken 43 data sets from 11,000 couples and a machine learning algorithm to figure this out. This is obvious. Sure, maybe people didn't have an exact value to assign to each variable, but it's no secret that if you don't feel your partner isn't committed to the relationship or you aren't sexually satisfied, the relationship is likely doomed. Can you please offer me a rebuttal to this criticism?

I totally get this perspective. But the thing is, it's not science's job to be counterintuitive. Its job is to be robust and accurate, and sometimes reality is just not that surprising.

Many of those more "interesting" variables you mentioned-- political ideology, religion, upbringing, etc--were in this project. They were measured, they were tested, and they didn't work. This project had hundreds of measures, many of which, it turns out, just aren't that important.

For example, take individual differences. Many of these studies included measures of:
- education
- income
- stress levels
- anxiety
- depression
- relationship beliefs
- the big five measures (extraversion, openness, etc.)
- life values
- ethnicity
- self-control

All that stuff combined, measured from one partner, explained a grand total of 5% of the variance in the other partner's relationship satisfaction. That's it.

We preregistered these analyses before we ran them, and were prepared to publish them no matter how they came out. This is how they came out, so this is what we published.

Do you want to develop an app?

I fully, deeply, absolutely do not. https://twitter.com/datingdecisions/status/1288635730336591872

Will the ai ever be released to the public?

Yes! Details of the project, including all of the code and meta-data, are available here: https://osf.io/d6ykr/

Hi Dr Joel,

Thanks for the AMA. I was reading your paper, and its really interesting, could you please tell me what 'actor' and 'partner' variables/effects are?

"Actor" refers to the person who's relationship quality we're predicting, and "partner" refers to their partner. So, if Andreas and Mary are participating in this study, and we are trying to predict whether Andreas is happy in the relationship, Andreas is the actor and Mary is the partner. When we're predicting Mary's satisfaction, Mary is the actor, and Andreas is the partner. We set the models up this way instead of distinguishing the partners by gender (e.g., husband and wife) so that we can include same-sex couples in the analyses.

Hey, I'm also from UWO. Do you have any papers published that I could learn further?

Hello, fellow Mustang! A full list of my publications is available on my lab website: http://relationshipdecisions.org/publications/

which relationships last longer? the ones with people with different interests or similar interests?

We didn't predict relationship longevity per se. But in terms of predicting relationship satisfaction and commitment, we found no evidence that matching matters in any way. Combining both partner's traits into one model did not predict more variance than one partner's traits on their own.

So we found no evidence for the idea that birds of a feather flock together, nor did we find evidence for the idea that opposites attract.

Have you ever watched Black Mirror, or anything else explaining why this is a bad idea?

Black Mirror is a really nice illustration of the importance of research ethics boards.

I think they were referring to the episode Hang the DJ which I won't spoil but is very pertinent to your work. I came to ask if you had seen this.

Ethics aside, I love the Hang the DJ episode of Black Mirror. It's consistent with my view of relationship compatibility, which is that you cannot predict the quality of a relationship that hasn't formed yet.

I mean, aren't those factors pretty obvious anyway? Why do we need an algorithm to analyze 11,000 couples to tell us we need decent sex, affection, and trust?

It's a good point - the variables that wound up being important are pretty intuitive. But, many of the variables that didn't make the cut seem intuitive as well. For example, you'll notice that gender is not on the list. There are hundreds of studies on the importance of gender in relationships, and it was measured in every study we had. Yet, it almost never emerged as a predictor.

So, I think this is the sort of project where any results would have appeared obvious in retrospect. To me, the surprising findings are not so much the stuff that worked, but the stuff that didn't work. You can see a full list of all the variables tested here: https://osf.io/8fzku/

Yet, it almost never emerged as a predictor.

Surely that's because (almost) no one for who gender is important enters a relationship with someone who isn't that gender? I'm sure if we could take a group and randomize partners gender - gender preference would emerge as significant. I feel like these results say "Gender isn't important in a partner as long as you pick the gender you want your partner to be"

Not gender preference, gender. YOUR gender.

If relationship satisfaction operates differently depending on your gender--for example, if men and women prefer different things in a relationship--then gender should have emerged as a consistent predictor in our models.

Why can't you predict anything with covid?

Believe me: the COVID research is coming. Many academics are currently studying relationships in the wake of COVID, but collecting data, writing up the results, and getting them published is a very slow process. Expect an explosion of papers in another 1-2 years.

How did you like WesternU? London is a great area.

I've only lived here for two years, but so far I like it a lot! Western is a great place to work- awesome students, and tons of research support. London is a smaller city than I'm used to, but it has a lot of hidden gems. The longer I live here the more it grows on me.

Hi! Thanks for doing this ama. Did you study same sex couples? Were there any discernible differences in relationship satisfaction?

Some of the studies had a modest number of same-sex couples, and many studies had sexual orientation as a measure. Neither gender nor sexual orientation tended to emerge as a predictor in the models, suggesting that there probably weren't a lot of differences there. That said, we did not dig into the data and directly test for differences.

I'm preparing to apply for MSc thesis to research in Western. I am an international student.

What would be your suggestion to get in and conduct my research successfully?

This could be a whole other post, but one key piece of advice I have for people applying to graduate school is to spend some time on that research statement. The statement provides an opportunity for you to demonstrate:

• Intrinsic motivation (are you confident that graduate school is how you want to spend your next 5-6 years?)
• Prior research-related experience (how have you honed your academic interests and skills?)
• Research interest fit (is this lab a place where you will be able to conduct the kind of research you want to do?)
  

Also, be sure to do a bit of research into the advisor you're applying to work with and make sure that there's fit there, both in terms of research interests and in terms of their mentoring style.

So why do you even think that it is possible to predict the future of a couple ? In my experience computers are not very good with predictions.

And what are your objective points with wich you feed the ai.

And I think that your work is really great and interesting :)

Thank you, Party_Frozy! Certainly, we went into this project prepared for the possibility that we would not be able to predict relationship quality at all. In fact, the last time my colleagues and I embarked on a machine learning project, it was with speed dating data, and we reached exactly this conclusion – we could NOT predict which pairs of individuals would be attracted to each other. (https://journals.sagepub.com/doi/abs/10.1177/0956797617714580)

So we were pleased to find that we could predict up to 18% of the variance in relationship quality over time. It’s a modest amount, and there’s certainly lots of unexplained variance left there. But it’s more than 0 and that’s exciting!

The predictors we used in the model were hundreds of self-reported measures collected from the couples. There was a total of 43 datasets, each of which measured different things. Tons of traits, preferences, relationship judgments, demographic variables, etc. Some more concrete and objective than others.

Why apply machine learning to something as nebulous and subjective as human relationships? Are you interested in applying ML to other areas of social science, or perhaps even the humanities? It seems to me that you're doing some cross disciplinary research. Is your background more in social science or computer science?

My background is in psychology. I'm a relationships researcher, so romantic relationships are really my focus. I agree that relationships are incredibly nebulous and subjective, which is part of why they are so fascinating to me! I think they’re a central part of many people’s lives, so it’s worth pulling out all the methodological stops to try to understand them, empirically.

I take a multi-method approach to studying relationships. In other projects I've used videotaped interactions between couples, daily experience studies where we send brief surveys to couples about their relationships each day, longitudinal methods where we track relationships over months or years, etc.

For anyone looking to read the original paper, you can download the non-copy-edited version for free here: https://osf.io/kacdx/