Courses 2026-27: Social Science

This course is an introduction to the wide, fast-moving range of ways in which AI methods are being used in social science, and how social science is used to understand AI impacts (e.g., in labor markets). The focus will be mostly on LLMs and machine learning, with one example from CLIP. Topics include: AI agents as substitutes for people in political and economic surveys and experiments; alignment: sycophancy, possible AI harm; machine learning for prediction in small (experiments) and large data sets (e.g., bail-setting), and AI used in analysis of financial data. Students will be graded on (1) in-class contributions, (2) two homework sets, (3) a class project either extending (or reproducing) a recent paper or doing an original analysis, and (4) an in-class final. Some funds will be available for computing power.

Course will introduce the student to the central problems of political theory and analysis, beginning with the essential components of the democratic state and proceeding through a variety of empirical topics. These topics will include the analysis of electoral and legislative institutions, legislative agenda processes, voting behavior, comparative political economy, and cooperation and conflict in international politics. The student will be sensitized to the primary empirical problems of the discipline and trained in the most general applications of game theoretic reasoning to political science. Open to Social Science graduate students only.

Mathematical theories of individual and social choice applied to problems of welfare economics and political decision making as well as to the construction of political economic processes consistent with stipulated ethical postulates, political platform formulation, the theory of political coalitions, and decision making in political organizations. Part a and c not offered 2026-27.

May be repeated for credit. Advanced work in a specialized area of economic theory, with topics varying from year to year according to the interests of students.

This three-quarter sequence is designed for advanced Social Science Ph.D. students with the aim of introducing students to the methodology of modern experimental economics and to provide an in-depth overview of the contributions of experimental methods to a wide variety of fields. The specific topics covered, which will vary from year to year, include but are not limited to individual decision making, preference and belief elicitation, game theory, social learning, bargaining, labor economics, public finance, auctions, voting and elections, competitive markets, networks, matching, mechanism design, coordination/communication, and information aggregation. The focus will be on theory-based experiments and how the dialog between theoretical analysis and laboratory data feeds each other, thereby leading to new avenues of theoretical and experimental research.

The courses in this sequence cover advanced methods and tools in econometrics, as well as their applications to a variety of topics in economics, including industrial organization, dynamic choice, information economics, political economy, market design, and behavioral economics.

This course provides broad coverage of empirical methods in the social sciences. This includes both methods of data collection and practical aspects of data analysis, as well as related issues of survey design, experimental design, techniques for handling large datasets, and issues specific to the collection and analysis of field and historical data. This course also provides students with hands-on experience with data. Open to Social Science graduate students only.

May be repeated for credit. Introduction to modern quantitative history. The tools of economic and political theory applied to problems of economic, social, and political development in a historical context. Second and third terms will be graded together. A pass/fail will be assigned in the second term and then changed to the appropriate letter grade at the end of the third term.

Only open to advanced graduate students in social science. How can social scientists write in a style that makes someone actually want to read their papers? This seminar combines writing exercises with help in planning a professional social science paper and with extensive comments on drafts.

Course for graduate students in social sciences. Students present their research and lead discussion of material relevant to their research program. Open to Social Science Graduate Students only.

The goal of this course is to introduce graduate students to current research questions in cognitive sciences, political science, and economics. Select faculty will present their research background, methods, and a sampling of current studies. Background readings and pdf of presentation will be provided.