Run Game The game

Evolutionary psychology and attraction: what the science supports

5 min read

Evolutionary psychology is the most quoted and most argued-about science in dating advice. One side says it explains why people pick the partners they do. The other says it's a set of tidy stories that fit almost any result. Both sides have published evidence, so it's worth knowing what each actually found.

One rule before anything else: every number below describes groups. A study can say that one group, on average, rated a trait higher than another. It can't tell you what the person in front of you wants.

The two ideas behind the field

Parental investment. In 1972 the biologist Robert Trivers proposed that the sex that invests more in offspring should be choosier about mates, while the sex that invests less should compete more for access. He meant it as a rule across animal species, and researchers later applied it to humans.

Mate preferences. The psychologist David Buss turned that logic into predictions about people. If women faced higher obligatory costs of pregnancy and nursing, the argument goes, they should on average put more weight on a partner's resources and ambition. If men faced the problem of identifying fertile partners, they should on average put more weight on youth and looks.

What the data show

Buss tested these predictions in a 1989 paper: 10,047 people in 37 samples from 33 countries rated how much they valued traits in a mate. Participants who were women rated earning capacity and ambition higher than participants who were men did. Participants who were men rated youth and physical attractiveness higher.

A 2020 study in Psychological Science repeated the design on purpose. Walter and colleagues, with a preregistered plan, surveyed 14,399 people in 45 countries. The authors report that the same pattern held: men on average valued attractive, younger partners more, women on average valued older partners with financial prospects more. The study also tested a rival idea, biosocial role theory, which says such differences come from social roles and shift as gender equality rises. In more equal countries, both sexes' actual partners were closer in age. But beyond age, neither gender equality nor disease prevalence robustly predicted the size of the differences.

How big is the difference? That depends on how you measure. In 2015 Conroy-Beam, Buss and colleagues re-analyzed the 1989 data (n = 10,153) as one combined pattern across 19 traits instead of trait by trait. They found a large combined difference, with only 22.8% overlap between the sexes, and could classify respondents' sex from their preference profile with 92.2% accuracy. They also listed limits: most items used a narrow 4-point scale, each trait was one question, and the age-gap preference carried a lot of the effect (though the difference stayed large without it).

Where it gets contested

Stated versus real attraction. Eastwick and Finkel (2008) ran speed dating with 163 undergraduates. Beforehand, participants stated the familiar preferences. But in the room, how interested they were in actual people didn't differ by sex in relation to those people's attractiveness or earning prospects. And the preferences people had stated didn't predict who they liked. A 2024 registered report (posted as a preprint) across 43 countries (N = 10,358) found that stated ideals do predict attraction a little: very small effects for specific traits (average beta of .04), larger for overall patterns. It also found that people's stated views of how much looks matter understated how much looks mattered in their ratings.

Who does the approaching. Finkel and Eastwick ran speed dating with 350 undergraduates in which sometimes men rotated and sometimes women did. Whoever rotated was less selective, whichever sex they were. The authors suggest approaching raises confidence. It was one university and an artificial setting.

The animal-biology base. Trivers' theory is often paired with Bateman's 1948 fruit fly study. In 2012 Gowaty and colleagues repeated it and said they couldn't confirm the conclusions, citing a flaw in how offspring were counted. A commentary in the same journal said Bateman's principles should be treated as hypotheses, and also noted that some studies in newts, pipefish and fruit flies support parts of them.

The wider critique. The Stanford Encyclopedia of Philosophy describes objections raised by critics, including philosophers: that explaining a trait as an adaptation can slide into untestable stories, that finding the same preference in many cultures doesn't show it's an adaptation, and that other explanations fit some of the same data (the philosopher David Buller argues pairing off by similar status does better for some of it). Separately, anthropologist Monique Borgerhoff Mulder, as summarized on Wikipedia's parental-investment page, argues that human mating systems vary widely and that Trivers-Bateman predictions depend on local context.

So the honest summary: the average differences in stated preferences have held up in the large surveys above. What causes them is argued over. And what they say about who will like whom, face to face, is weaker than the survey numbers suggest.

Why it can't predict one person

Overlap is the key. Even a large difference in averages leaves many people in each group on the other side of the average. And preferences are only part of attraction: timing, familiarity, humor, shared setting and plain chemistry sit outside those 19 survey items. For more, read how attraction works and what people look for in a partner. Helen Fisher's work takes a different, brain-focused angle.

What to do next

  1. Treat any "evolution says" claim as a base rate. If a post or video says what "women want" or "men want," ask what the sample was and how big the gap was.
  2. Ask about real things. Rather than "what do you look for in a partner?", ask about the last person someone really liked and why. The speed-dating work suggests concrete experience tells you more than abstract lists.
  3. Be the one who starts. In one lab study, the people who approached rated more chemistry. Pick one setting this week and start three conversations.
  4. Read the primary abstract for any study you plan to quote. Check the sample size, who was surveyed and what was measured.
  5. Notice your own assumptions. After your next five conversations, write one line on what you expected and what happened.

If you track conversations in Run Game, the session notes are a handy place for line five.

Sources