Filtered HSS Courses (2026-27)

Ec 1 will provide a window into economics, covering a "greatest hits" curriculum in economic theory, empirical economics and experimental economics. Topics will include: game theory, the theory of auctions, information design and mechanism design, the analysis of voting rules, experiments in economics, macroeconomic growth, and data science. This course satisfies the menu requirement of the Caltech core curriculum.

Pomatto's section (second term) An introduction to economic methods, models, and institutions. The course focuses on a set of foundational questions: what prices represent, how they are determined, and how they function to allocate goods and resources. The class emphasizes abstract reasoning and formal arguments. Students seeking a wide-ranging introduction to economic institutions may prefer Professor Druckenmiller’s section. This version of the course is concentrated on a narrower set of topics.  Druckenmiller's section (third term): This course provides a rigorous introduction to microeconomic theory at a level comparable to an intermediate course, using calculus to analyze how individuals and firms make decisions and how these decisions shape markets. Students develop demand from consumer optimization and supply from firm cost minimization and profit maximization, then study competitive markets, general equilibrium, and welfare. The course emphasizes applying theory to real-world problems and policy questions, while also examining departures from perfect competition, including market power, uncertainty, incomplete property rights, and asymmetric information. Students seeking an introduction to economics based in first principles may prefer the section taught by Professor Pomatto.

This course offers advanced undergraduates the opportunity to pursue research in Economics individually or in a small group. Graded pass/fail.

Senior economics majors wishing to undertake research may elect a variable number of units, not to exceed 12 in any one term, for such work under the direction of a member of the economics faculty.

Topics to be determined by instructor.

Economic policy questions increasingly depend on novel measurement: satellite imagery to track deforestation, digitized historical maps to study long-run development, and neural networks to predict poverty. This course surveys how economists build new spatial data from computational methods and deploy it for credible policy evaluation. Topics are organized as self-contained modules covering remote sensing and land use classification, digitization of historical maps, and deep learning for prediction. Data science is presented as an input to spatial causal inference techniques: boundary discontinuities, market access approaches, and spatial instrumental variables. Each module pairs a methodological introduction with a recent application from development, trade, or environmental economics. Students will complete hands-on assignments and develop an original policy evaluation proposal that leverages one of these modern measurement tools.

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.

A study of how technology affects issues of market structure and how market structure affects observable economic outcomes, such as prices, profits, advertising, and research and development expenditures. Emphasis will be on how the analytic tools developed in the course can be used to examine particular industries-especially those related to internet commerce-in detail. Each student is expected to write one substantial paper.

In this course we will examine behavioral economics models in several key decision-making contexts: choice over time, choice under uncertainty, choice in market settings (i.e., buying and selling), and others. In each topic area we will begin by providing the standard neoclassical account for behavior, building from assumptions (axioms) to corresponding utility models. We will then ask whether critical axioms of neoclassical models are plausible by calling upon psychological insights and empirical data. Rejection of neoclassical axioms will lead to new models, behavioral economics models, in each setting. These models, mechanisms and their testable predictions will be discussed.

This course will study topics in behavioral economics demonstrating departures from the classic economics assumptions of rationality and pure self-interest. We will study evidence of these departures, models that have been designed to capture these preferences, and applications of these models to important economic questions. Topics will include biases and heuristics, risk preferences, self-control, strategic uncertainty, and social preferences, among others. The course will be based in readings from both classic and modern research. Methodologically, the course will combine both theoretical and empirical evidence of the mentioned above topics.

This course provides an introduction to Bayesian Statistics and its applications to data analysis in various fields. Topics include: discrete models, regression models, hierarchical models, model comparison, and MCMC methods. The course combines an introduction to basic theory with a hands-on emphasis on learning how to use these methods in practice so that students can apply them in their own work. Previous familiarity with frequentist statistics is useful but not required.

Field experiments are widely used in academia, government and industry to evaluate the effectiveness of changes in social policy, business models, and medical interventions, among others. The course provides an introduction to the design and analysis of field experiments, with applications in each of these domains. The course has an emphasis on hands-on learning in order to build practical skills in real research scenarios. A previous course covering linear regression is a prerequisite for the class.

We will tackle the fundamental question of how to allocate resources and organize exchange in the absence of prices. Examples includes finding a partner, allocating students to schools, and matching donors to patients in the context of organ transplantations. While the main focus will be on formal models, we will also reason about the practical implications of the theory.

The use of large data sets and innovative statistical methods has revolutionized professional and intercollegiate sports. This course introduces students to the academic and professional world of contemporary sports science. The course will meet biweekly with instructor lectures on sports science and with guest speakers from collegiate and professional sports. Students will be introduced to the primary data sources for sports science, to methods used to collect sports performance and outcomes data, and to the statistical tools used for sports analytics (for example, logistic regression, regression trees and random forest, network models, time series, and natural language processing). Students will be responsible for weekly writing or homework assignments based on readings and speaker presentations, as well as a quarter-long sports analytics research project. Students should have some background in econometrics, statistics and probability, data science, or machine learning.

A rigorous treatment of microeconomic theory, serving as a bridge to graduate-level courses in general equilibrium theory. Topics include consumer choice theory—preferences, utility maximization, and demand; tools for welfare analysis, including compensating and equivalent variation; the theory of the firm—production, cost minimization, and profit maximization; and partial equilibrium analysis—market equilibrium, the effects of taxation, and market failure due to monopoly. Through theoretical models and applications, the course develops the analytical skills needed for advanced work in economics. Ec 150 (Mathematical Methods for Economics) is useful preparation but is not required.

The application of statistical techniques to the analysis of economic data.

Statistical inference in the social sciences is a difficult enterprise whereby we combine data and assumptions to draw conclusions about the world we live in. We then make decisions, for better or for worse, based on these conclusions. A simultaneously intoxicating and sobering thought! Strong assumptions about the data generating process can lead to strong but often less than credible (perhaps incredible?) conclusions about our world. Weaker assumptions can lead to weaker but more credible conclusions. This course explores the range of inferences that are possible when we entertain a range of assumptions about how data is generated. We explore these ideas in the context of a number of applications of interest to social scientists.

This course examines the macroeconomic growth of the United States over the past 125 years, with an emphasis on the economic theories and analytical methods used to study long-run development. It is organized around major thematic topics rather than chronology, with each topic covered over two lectures and focused on the key economic phenomena that have shaped the U.S. economy. Topics include the measurement of economic growth; the roles of institutions, geography, and technology in shaping productivity; migration and local labor markets; schooling and human capital accumulation; the spatial distribution of industry; market failures and financial crises; role of government in economic growth; and income and wealth inequality. Students will engage with a range of empirical research methods and theoretical frameworks used in modern economic analysis. Familiarity with introductory economics and econometrics is strongly encouraged. Students are expected to attend all classes, actively participate in discussions, and complete two in-class exams in addition to problem sets. Course open only to juniors and seniors.  This is not a writing intensive course.

In this course, we will study the global economy and how it is shaped by the interaction between market forces and government policy. We will build a foundational understanding of the economics of international trade and finance and examine the incentives that drive the behavior of firms, investors, and governments. Topics will include comparative advantage, factor endowments, and the gains from trade; tariffs, industrial policy, and regional trade agreements; exchange rate regimes, interest rate parity, sovereign debt, and financial crises. Throughout the course, we will frame our analysis within a broader institutional and historical context, examining how global economic systems have been organized through periods of colonialism, globalization, and geoeconomic rivalry.

An examination of the role of political and economic forces in shaping the development of the Chinese economy during the Qing and Republican periods.  The class emphasizes the use of simple economic models to understand how the Chinese economy grew and where it failed to do so. We will also discuss how this history helps explain China’s performance in the last half century. Students are expected to read research papers and discuss them in class.

An analysis of the effects of uncertainty and information on economic decisions. Included among the topics are individual and group decision making under uncertainty, expected utility maximization, insurance, financial markets and speculation, product quality and advertisement, and the value of information.

Social networks, the web, and the internet are essential parts of our lives, and we depend on them every day. CS/EE/IDS 143 and CMS/CS/Ec/EE/IDS 144 study how they work and the "big" ideas behind our networked lives. In this course, the questions explored include: What do networks actually look like (and why do they all look the same)?; How do search engines work?; Why do epidemics and memes spread the way they do?; How does web advertising work? For all these questions and more, the course will provide a mixture of both mathematical analysis and hands-on labs. The course expects students to be comfortable with graph theory, probability, and basic programming.

This course will equip students to engage with active research at the intersection of social and information sciences, including: algorithmic game theory and mechanism design; auctions; matching markets; and learning in games.

Provides the mathematical foundations for graduate-level economics, with applications illustrating each set of tools. Topics include real analysis and linear algebra (sequences, compactness, eigenvalues, quadratic forms, concavity, convexity); constrained and unconstrained optimization (Lagrange multipliers, Kuhn–Tucker conditions, constraint qualification) together with the implicit function and envelope theorems for comparative statics; separating and supporting hyperplane theorems and the theorem of the alternative (Farkas’ lemma); and correspondences, hemicontinuity, the theorem of the maximum, and fixed point theorems (Brouwer, Kakutani). These tools provide a foundation for studying graduate microeconomics, macroeconomics, and econometrics.

This course is an introduction to non-cooperative game theory, with applications to political science and economics. It covers the theories of normal-form games and extensive-form games, and introduces solutions concepts that are relevant for situations of complete and incomplete information. The basic theory of repeated games is introduced. Applications are to auction theory and asymmetric information in trading models, cheap talk and voting rules in congress, among many others.

Climate change has already begun to impact life on the planet, and will continue in the coming decades. This class will explore particular causes and impacts of climate change, technologies to mitigate or adapt to those impacts, and the economic and social costs associated with them - particular focus will be paid to distributional issues, environmental and racial justice and equity intersections. The course will consist of 3-4 topical modules, each focused on a specific impact or sector (e.g. the electricity or transportation sector, climate impacts of food and agriculture, increasing fires and floods). Each module will contain lectures/content on the associated climate science background, engineering/technological developments to combat the issue, and an exploration of the economics and the inequities that exacerbate the situation, followed by group discussion and synthesis of the different perspectives.

This is a graduate course in the fundamentals of economics. Topics include comparative statics and maximization techniques, the neo-classical theory of consumption and production, general equilibrium theory and welfare economics, public goods and externalities, the economic consequences of asymmetric information and incomplete markets, and recursive methods with applications to labor economics and financial economics.

Introduction to the use of multivariate and nonlinear methods in the social sciences.