Time: n.a.

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Description: This course will demonstrate how decades of psychological research can be translated into models that can be incorporated into or examined by applied microeconomists. Topics include ways utility theory can be improved—such as incorporating reference dependence, social preferences, self image, and other belief-based tastes—and ways we can relax assumptions of perfect rationality—such as incorporating focusing effects, limited attention, biased prediction of future tastes, present-biased preferences, and biases in probabilistic judgment. As in other field courses, I insist on the importance of neoclassical theory as a benchmark. This course extends those theories to improve psychological realism and empirical predictions. Given the strengths of our graduate program at MSU, this class has largely an empirical orientation. We will explore empirical papers drawn from a variety of fields including but not limited to: asset pricing, corporate finance, consumption, development economics, environmental economics, health economics, industrial organization, labor economics, political economy, and public economics. 

Time: n.a.

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Description: This course will introduce students to decades of psychological research indicating systematic departures from classical economic assumptions. Furthermore, this course will demonstrate how that research can be translated into formal models that can be incorporated into economics. Topics include ways utility theory can be improved—such as incorporating reference dependence, social preferences, self image, and other belief-based tastes—and ways we can relax assumptions of perfect rationality—such as incorporating focusing effects, limited attention, biased prediction of future tastes, present-biased preferences, and biases in probabilistic judgment. The course will emphasize (a) careful interpretation of new evidence, (b) formalizing this evidence into models that can, with discipline and rigor, generate sharp predictions using traditional economic approaches, and (c) exploring economic implications of those models presented. Although the course will primarily emphasize (b), it is intended to be useful to students whose interests lie anywhere in this spectrum.

Time: n.a.

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Description: Innovations in statistical learning have created many engineering breakthroughs. From real time voice recognition to automatic categorization (and in some cases production) of news stories, machine learning is transforming the way we live our lives. These techniques are, at their heart, novel ways to work with data, and therefore they should have implications for social science. This course explores the intersection of statistical learning (or machine learning) and social science and aims to answer two primary questions about these new techniques:

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1. How does statistical learning work and what kinds of statistical guarantees can be made about the performance of statistical-learning algorithms?

2. How can statistical learning be used to answer questions that interest social science researchers, such as testing theories or improving social policy?