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Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships.

We thank Vaida Thompson and John Schopler for their support and advice throughout all stages of this work. We are also grateful to Ximena Arriaga, Chris Agnew, Keith Campbell, and Jeff Green for their suggestions regarding various components of this work. Finally, we thank several particularly outstanding research assistants-Lindsay Shaw, Abby Toner, and Erin Williams-for their invaluable assistance in conducting research sessions and coding responses.

Building on attribution and interdependence theories, two experiments tested the hypothesis that close friends of victims (third parties) are less forgiving than the victims themselves (first parties). In Experiment 1, individuals imagined a scenario in which either their romantic partner or the romantic partner of a close friend committed the identical relationship offense. Third parties were less forgiving than first parties, a phenomenon we termed the third-party forgiveness effect. This effect was mediated by attributions about the perpetrator's intentions and responsibility for the offense. In Experiment 2, first and third parties reported an actual offense and their subsequent unforgiving motivations. The third-party forgiveness effect was replicated and was mediated by commitment to the perpetrator. Perpetrator apology or amends to the victim increased third-party forgiveness. Future third-party research can expand interpersonal forgiveness research beyond the victim-perpetrator dyad.