Social norms are unwritten behavioral codes. They direct individual behaviors, facilitate interpersonal coordination and cooperation, and lead to variation among human populations. Understanding how norms are maintained and how they change is critical for understanding human evolutionary psychology, social organization, and cultural change. This dissertation uses a mathematical model and a field study to answer two questions: First, what factors determine the content and dynamics of a social norm? Second, how do people make decisions in a normative context? The mathematical model finds that contrary to the popular belief that even arbitrary or deleterious social norms can be maintained once established because deviants suffer coordination failures and social sanctions, norms with continuously varying options cannot be maintained by the pressure to do what others do. Instead, continuous norms evolve to the optimum determined by environmental pressure, individual preferences, or cognitive processes. Therefore, the content of norms across human societies may be less historically constrained than previously assumed. The field study shows that unlike what rational choice theory predicts, people in a small-scale subsistence society do not calculate the ecological and social payoffs of different behaviors in a normative context, even when they have the information to do so. Instead, they rely heavily on social information about what others do. This decision-making algorithm, together with mental categorization that ignores small deviations, and cognitive biases that favor the division prescribed by the norm, maintain an ecologically inefficient and widely disliked cooperative surplus division norm in a Derung village, Dizhengdang, in Yunnan, China.

Studying human genetic variation opens the possibility of understanding the details of population migrations, how humans develop and function, and why they get sick. To fully understand these things, genetic variation must be comprehensively characterized across globally diverse human populations and evolutionary knowledge can be used to inform studies of disease. In my dissertation I use computational methods to study human genetic variation. Each of my dissertation chapters focuses on a unique topic in the field of human evolutionary genetics. In the first chapter, I present PopInf, a computational pipeline to visualize principal components analysis output and assign ancestry to samples with unknown genetic ancestry, given a reference population panel of known origins. This pipeline facilitates visualization and identification of genetic ancestry across samples, so that this ancestry can be accounted for in studies of health and disease risk. In the next chapter, I investigate factors that shape patterns of genetic variation within and among four small-scale pastoral populations in northern Kenya. I find that geography predominantly shapes patterns of genetic variation in northern Kenyan human populations. In the next chapter, I investigate the extent to which Neanderthal introgression impacts liver cancer etiology. I find a pattern of overall enrichment of somatic mutations on Neanderthal introgressed haplotypes. Finally, through simulations, I investigate the effects of standard autosomal versus sex chromosome complement-informed alignment, variant calling and variant filtering strategies on variants called on the human sex chromosomes. I show that aligning to a reference genome informed on the sex chromosome complement of samples improves variant calling on the sex chromosome compared to aligning to a default reference, and variant calling is improved in males when calling the sex chromosomes haploid rather than diploid and when using haploid-based thresholds for filtering variants on the sex chromosomes. I provide recommendations for alignment, variant calling and filtering on the sex chromosomes based on these findings.