THREE ESSAYS ON DATING AND MARRIAGE USING DEEP LEARNING

This dissertation consists of three empirical studies that apply deep learning and Artificial
Intelligence (AI) to the field of economics in dating and marriage. Each essay addresses a
distinct question at the intersection of romantic relationships and economic outcomes, unified
by methodological innovation. Chapter 2 investigates whether people tend to pair with
partners who are genetically similar and how such genetic assortative mating influences
household outcomes. Using polygenic scores from genomic data, I find strong evidence that
couples are genetically similar in traits like subjective well-being, Age at First Birth, and
Number of Children Ever Born. Notably, couples that show similar genetic tendency for
higher well-being and delayed childbearing accumulate greater wealth, and they are less
likely to divorce, while those with similar genetic traits toward larger families have more
children but lower wealth. These findings imply that genetic assortative mating may cause
economic advantages or disadvantages, and even lead to wealth inequality and demographic
trends. Chapter 3 examines the role of physical attractiveness in dating and marital stability
by utilizing deep learning-based facial recognition on a unique dataset of celebrity couples.
We construct beauty scores and facial similarity measures from facial images of celebrities to
analyze beauty premium in relationship dynamics. Physically more attractive individuals,
especially women, tend to have more number of partners, however, the beauty effect remains
only temporary for long-term relationships. In both marriage and short-term relationships,
beauty yields greater opportunities initially, but the effect of education and number of past
partners remain longer in relationship stability. For instance, we find that more attractive
female celebrities have more partners but often shorter relationship durations, which suggest
that appearance may negatively influence the relationship duration when no other forms of
compatibility are supported. We also detect patterns in partner selection, such as same-race
pairings and preferences shaped by profession, while finding no support for common beliefs
like couples looking alike or astrological sign compatibility influencing stability. Chapter 4
explores a novel methodology by using large language models (LLMs) to simulate human
behavior in dating markets. We create an artificial sample of silicon agents, AI agents
generated by LLMs, calibrated to mimic a real-world speed-dating experiment. These AIdriven agents remarkably demonstrate human-like decision-making. For example, male
agents prioritize physical attractiveness in partners, whereas female agents place greater
weight on intelligence, mirroring gender differences in real world. These AI agents also
reproduce human patterns of same-race preference. Also, incorporating latent personality
traits (extroversion and openness) generate match predictions that are in line with real world
data, demonstrating strong algorithmic fidelity. This innovative approach shows that
generative AI can serve as a cost-effective, controlled, and ethical tool to replace human in
social science research. Overall, the three essays illustrate how deep learning tools can be
integrated into economic analysis of personal relationships. It is not only about understanding
the determinants of partner selection and relationship stability but also highlighting
implications for economic policy. The results inform debates on inequality (by showing how
“like marries like” can widen wealth gaps), on the limits of observable attributes like beauty
in securing lasting partnerships, and on new research frontiers using AI. Overall, this
dissertation shows that modern AI techniques can deepen empirical economic research,
suggesting ways to better-informed family policies and novel methodologies for complex
human decision problems