Fig 1.
Population size of each algorithm across generations based on 50 total model runs.
Population size is the average population of each algorithm at each generation across model runs. Error bars represent 95% confidence intervals.
Table 1.
Performance of each mate preference integration algorithm across model runs.
Fig 2.
The Euclidean distance method for quantifying preference fulfillment in a small hypothetical population.
A person’s preferences (black) and be plotted in a preference space alongside their partner’s qualities (white) and the qualities of their alternative potential partners (grey).
Fig 3.
Absolute preference fulfillment as a function of agent mate value within one run of Study 4’s Euclidean agent-based model.
Consistent with predictions, agents who are themselves more desirable as mates are on average mated to partners who better fulfill their ideal mate preferences.
Fig 4.
Preference fulfillment as a function of participant mate value.
Participants in Study 4 who better embodied the preferences of the opposite sex were mated to partners who better fulfilled their ideal mate preferences.