Aspirational pursuit of mates in online dating markets
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Abstract (arXiv)
Romantic courtship is often described as taking place in a dating market where men and women compete for mates, but the detailed structure and dynamics of dating markets have historically been difficult to quantify for lack of suitable data. In recent years, however, the advent and vigorous growth of the online dating industry has provided a rich new source of information on mate pursuit. Here we present an empirical analysis of heterosexual dating markets in four large US cities using data from a popular, free online dating service. We show that competition for mates creates a pronounced hierarchy of desirability that correlates strongly with user demographics and is remarkably consistent across cities. We find that both men and women pursue partners who are on average about 25% more desirable than themselves by our measures and that they use different messaging strategies with partners of different desirability. We also find that the probability of receiving a response to an advance drops markedly with increasing difference in desirability between the pursuer and the pursued. Strategic behaviors can improve one's chances of attracting a more desirable mate, though the effects are modest.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Aug 2018.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 0; 2025: 1; 2026: 2.
5 papers.
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- 2026AI-Driven Multi-Agent Simulation of Stratified Polyamory Systems: A Computational Framework for Optimizing Social Reproductive Efficiencycited by 0
- 2026Delegation Asymmetry in Agentic Recommender Systems: Measuring Two-Sided Receptivity in Online Datingcited by 0
- 2026Delegation Asymmetry in Agentic Recommender Systems: Measuring Two-Sided Receptivity in Online Dating18 Aug 2026
- 2026AI-Driven Multi-Agent Simulation of Stratified Polyamory Systems: A Computational Framework for Optimizing Social Reproductive Efficiency21 Mar 2026
- 2025Counterfactual Reciprocal Recommender Systems for User-to-User Matching3 Aug 2025
- 2023Exact and rapid linear clustering of networks with dynamic programming25 Jan 2023
- 2021The interplay between ranking and communities in networks23 Dec 2021