Euclidean distances discriminatively predict short-term and long-term attraction to potential mates

We tested the ability of a Euclidean algorithm to predict attraction to potential mates-a relatively upstream domain in the temporal sequence of the mating process. Participants in two studies reported their ideal mate preferences using a 23-item preference instrument. Separately, they rated their attraction to profiles of potential mates that varied on those 23 dimensions. Study 1 (N = 522) found that Euclidean distances predicted attraction to potential mates both in terms of (1) overall mate value and (2) unique mate value. Study 2 (N = 411) replicated these effects and further found that Euclidean mate values discriminatively predict between short- and long-term attraction. Across both studies, a Euclidean model outperformed a variety of alternative models for predicting attraction to potential mates. These results suggest that a Euclidean algorithm is a good model for how multiple preferences are integrated in mate choice.