Modeling in Economic and Social Sciences
When is it the right time to bluff in poker and diplomacy? Why do stock markets crash? How should the delivery of global healthcare be managed? And why do people have the values and beliefs that they do? All of these phenomena depend on models for the way that humans make decisions. This cluster, with faculty from economics, anthropology, sociology, and statistics, will teach students how to build and test models in the social sciences.
As part of their applied courses, students will be asked to work in small groups and complete an original research project. Students will have access to computer labs and other pertinent research facilities. The goal is to insure that students in this cluster achieve the technical toolkit and analytic perspective that will enable success at Duke and in life.
Cluster Prereq: Math 21 (Introduction to Calculus I) or the equivalent. (Normally obtained through AP or IB course work. Please contact the FOCUS office with questions)
Statistics 110FS — An Introduction to Statistical Modeling (QS)
David Banks, Professor of the Practice, Department of Statistical Science
In this course, students will learn about statistical modeling, with primary emphasis on developing critical thinking skills. Topics vary, but we often cover statistical genetics, agent-based modeling, Shannon's theory of communication, game theory, and mathematical models for epidemics. After completing this course, students will be able to design and analyze basic statistical studies, to understand and criticize statistical methods in journals and the media, and to appreciate the power and utility of statistical thinking. Examples and methods are drawn primarily from the behavioral, natural, and social sciences and public policy.
Political Science 189FS: Introduction to Machine Learning and Computational Models in the Social Sciences (R)
Scott de Marchi, Professor of Political Science
Our goal as social scientists is to build models of the world and provide advice to policy makers. Given that human actors are often strategic and the games they play are complex, building and testing these models is difficult and distinct from common examples of machine learning. A task that is often used to motivate introductions to machine learning is teaching a model to recognize hand-written characters using MNIST data (https://www.tensorflow.org/datasets/catalog/mnist). Our task is harder: we must build models that involve forecasting human behavior ranging from votes in a legislature to changes in stock prices. Given the complexity of these sorts of problems, we will cover both human decision-making and machine learning in this course. The hope is that our machine learning models will be better when they are informed by theoretical models of behavior.
Evolutionary Anthropology 212FS/ Genome 212FS: Evolution, Society, and Health in Comparative Perspective (NS, STS)
Jenny Tung, Associate Professor of Evolutionary Anthropology
Social group living long predates the emergence of the human species. Thus, evolutionary and comparative research can provide important insight into the social factors that influence how we do in the present-day. This course will introduce approaches from evolutionary anthropology and biology that are used to understand how society shapes health, lifespan, and Darwinian fitness. Students will receive an introduction to how verbal arguments about the evolutionary process can be formalized as models that can be tested with data or simulation (including introductions to the statistical software R and the markup language R Markdown). At the course conclusion, students will be better prepared to critically evaluate claims about human evolution and biological variation that are now regular parts of the news cycle.
Economics 190FS: Thinking Through Models (SS, STS)
Jason Brent, Adjunct Assistant Professor, Fuqua School of Business
Models of agents, behaviors, mechanisms and markets are essential to the way economists think about and describe the world. This course examines several of the models that have been most crucial to the development of economic thought from the early nineteenth century through to the present. Students will learn to understand how economists have used models as tools of inquiry and prediction, assess the functional elements of these models and consider how accurately they map on to the empirical phenomena they describe.
- Professor of the Practice of Statistics
- Director of the Statistical and Applied Mathematical Sciences Institute (SAMSI)