Review written by Paula Brooks (NEU, G6)
How similar are bears and bulls?
If you ask a biologist, she might say that they are pretty similar, since they are both four-legged mammals found in North America. However, if you ask an economist, he might say they are polar opposites, since they are used to describe distinct stock market conditions. The unique way in which individuals organize their semantic knowledge, or general information gained through life experiences, could cause two people to judge the similarity between two animals in very different ways.
Continue reading "Using Machine Learning to Better Understand Human Behavior"
Review written by Kimberly Sabsay (QCB, G3)
Socioeconomic status (SES), often simplified as absolute material wealth, is often linked to a variety of human health metrics. At a fundamental level, it makes sense that higher SES likely corresponds with access to better medical services, and in turn, better overall health. Studies have shown that, indeed, higher SES is associated with better human health, but the majority of this data comes from high-income countries (HICs). Despite the growing amount of scientific evidence for the apparent gradients in disease risk and survival explained by access to medical care and other health-related lifestyle factors, we cannot be certain that these trends are universal. Understanding the relationship between SES and health is crucial for policy design and to ensure we make economic decisions that do not negatively impact overall human health. Ultimately, the relationships between SES and health can be used to motivate positive change that benefits all of humanity.
Review written by Leon Mait (PSY)
In times of financial hardship, low-income individuals can often turn to their communities for support. Unfortunately, this buffer against financial difficulties provided by community resources can erode over time. One factor that may contribute to such erosion is economic inequality (which has been on the rise in the United States). This connection was recently found by a group of international researchers, including Princeton’s own Elke U. Weber, who holds joint appointments in Psychology, the School of Public and International Affairs, and Engineering.
Review written by Rohini Majumdar (PSY)
Research on life trajectories is one of social science’s most popular exports to policy making. Predictive models for specific life outcomes have been applied to a number of settings, including criminal justice and child-protective services. Despite the real-world relevance and far-reaching impact of this research, not many high-quality datasets exist to support it. The Fragile Families and Child Wellbeing Study (FFCWS), however, is one such enterprise by Princeton’s Center for Research on Child Wellbeing and the Columbia Population Research Center. Over 900 studies have used the FFCWS’s rich longitudinal data, which follows the families of almost 5000 babies born in major US cities around the turn of the millennium. Most of these children were born to unmarried parents, putting these families at a greater risk of splitting up and living in poverty compared to traditional families. Although life trajectory research has been used to inform policy, whether it can also be used to accurately predict specific life outcomes for individuals remains undetermined. To find out, Princeton Sociology’s Matthew Salganik, Ian Lundberg, Alex Kindel, and Sara McLanahan spearheaded the Fragile Families Challenge (FFC), a mammoth scientific mass collaboration based on a research design called the common task method.