The purpose of this course is to give the doctoral students an introduction to and survey of modern philosophy of science, so that their research will be informed by philosophical reasoning about science. The class will be managed as a discussion class based on an extensive set of readings.

This course introduces classical rational models of decision making, and contrasts these with experimental evidence on individual preferences, as well as successful descriptive models that predict how individuals make decisions. Focus will be on Bayesian rationality, subjective expected utility, and exponential discounting, as well as prospect theory for risk, hyperbolic discounting for time, heuristics for multi-attribute decisions, naïve Bayesian updating, and mental accounting models.

The purpose of this seminar is to provide a critical introduction to scholarly reading and writing concentrating on one portion of the foundations of management theory; namely, pragmatism and stakeholder theory. While we will address some "classic" texts in management and ethics, you should understand that the syllabus is idiosyncratic to me, rather than systematic.

This course is designed to introduce students to the strategy literature and the research approaches that strategy research uses. We will explore how to frame a research problem and develop and apply theory, including both the development of mathematical models and the formulation of formal hypotheses. We will learn about various empirical approaches and focus on general areas of concern such as measurement, selection, and endogeneity.

An independent study course is a faculty supervised study in which students explore a specific topic in the area of business administration.

The Competitive Dynamics Doctoral Seminar is designed to introduce students to the competitive dynamics (CD) literature and to train them in a programmatic approach to conducting research within this subfield of strategy. The elective is open to all PhD students in business and related disciplines.

Dynamic Programming provides a set of general normative methods for making sequential decisions under uncertainty. While there are no formal prerequisites for the course, some level of exposure to optimization, probability theory, and matrix-vector algebra is highly desirable. The course brings a new dimension to static models studied in optimization by investigating dynamic systems and their optimization over time.

This course has two main functions: first to give doctoral students exposure to experimental methods and second to help them understand the current theories and research in moral psychology.

Management scholarship ¿ particularly economics and strategic management - has historically been hesitant to include explicit reference to values, norms, and ethics in much of its theorizing. This course will examine the insights, contours, and challenges of this combination. The course will consider recent and emerging management scholarship as well as the legacy concepts that have informed this recent work.

For doctoral research taken under the supervision of a dissertation director.