Abstract

Prior mate preference research has focused on the content of mate preferences. Yet in real life, people must select mates among potentials who vary along myriad dimensions. How do people incorporate information on many different mate preferences in order to choose which partner to pursue? Here, in Study 1, we compare seven candidate algorithms for integrating multiple mate preferences in a competitive agent-based model of human mate choice evolution. This model shows that a Euclidean algorithm is the most evolvable solution to the problem of selecting fitness-beneficial mates. Next, across three studies of actual couples (Study 2: n = 214; Study 3: n = 259; Study 4: n = 294) we apply the Euclidean algorithm toward predicting mate preference fulfillment overall and preference fulfillment as a function of mate value. Consistent with the hypothesis that mate preferences are integrated according to a Euclidean algorithm, we find that actual mates lie close in multidimensional preference space to the preferences of their partners. Moreover, this Euclidean preference fulfillment is greater for people who are higher in mate value, highlighting theoretically-predictable individual differences in who gets what they want. These new Euclidean tools have important implications for understanding real-world dynamics of mate selection.

Highlights

  • Exploring the content of human mate preferences is a well-established research area with both a long history (e.g. [1]) and a cutting edge (e.g., [2])

  • Four studies provide evidence that a Euclidean algorithm is a strong candidate for the algorithm by which human mate preferences are integrated in mate choice

  • That our agentbased models converged across three samples with empirical data on real mating couples to support the Euclidean algorithm as the best description of human mate preference integration

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Summary

Introduction

Exploring the content of human mate preferences is a well-established research area with both a long history (e.g. [1]) and a cutting edge (e.g., [2]). Exploring the content of human mate preferences is a well-established research area with both a long history [1]) and a cutting edge (e.g., [2]). The downstream consequences of preferences, have received comparatively little attention. One critical unanswered question in mate selection is how people incorporate information on many individual mate preferences in order to choose which mates they want to pursue. Humans have many qualitatively distinct preferences which are presumably somehow combined, weighted, or compared in order to make actual mating decisions. Many candidate algorithms could accomplish this computation. We use agent-based models of mate choice evolution to test competing hypotheses about which of several algorithms are best able to motivate adaptive mate choice. We evaluate the ability of the most successful algorithms to explain actual mating data

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