What the science actually says.

Self-description does not predict desire

In 2017, Samantha Joel, Paul Eastwick and Eli Finkel published the most direct test of questionnaire-based matching to date. Using machine learning on more than one hundred self-reported traits and preferences collected before speed dates, they attempted to predict which two specific people would desire each other when they met. The models could predict who tended to like others, and who tended to be liked, but pair-specific desire, the thing a matching algorithm actually needs, was not predictable from the questionnaires at all.

Stated preferences do not survive the meeting

A decade earlier, Eastwick and Finkel had shown a related failure: the qualities people say they want in a partner do not predict who attracts them in live interaction. Speed-dating participants who insisted on particular ideals were no more drawn to people who embodied those ideals than to people who did not. A worldwide replication published in 2024 reached the same conclusion across cultures. What people say they want, and what draws them in person, are different systems.

What follows from this

Incumbent platforms continue to collect the inputs that are cheap to gather rather than the ones that predict outcomes. Kindred's design takes the evidence at face value: self-report is used only where it is reliable, as a dealbreaker filter; ranking is built from behaviour and reciprocity; and, most importantly, the platform measures real relationship outcomes over time, because the only way to learn what predicts lasting relationships is to observe them forming.

Our commitments

• Outcome data is collected only with explicit, informed consent, separate from the consent to use the platform.
• Findings are published openly, including null results. If behavioural matching does no better than chance, that is a finding, and it will be public.
• Published research uses aggregated and de-identified data. Individual members are never identifiable in anything we release.

For researchers

Kindred is building toward a consented longitudinal dataset that follows introductions through to relationship outcomes at six and twelve months, a resource that, to our knowledge, no commercial platform has ever opened to independent study. We are actively seeking academic partners in relationship science and computational social science, particularly at GTA universities, for co-designed studies and partnership grant applications. If that is your field, we would genuinely like to hear from you: kindredmatching@gmail.com.