London School of Economics

This course offers an introduction to corporate law and to the legal and non-legal governance mechanisms which encourage directors to act in their company's and not in their own interests. The course sets corporate law and governance within its economic and business context, with particular regard to how corporate law and governance mechanisms facilitate or inhibit economic activity. The course adopts an explicitly comparative approach drawing on both UK, US, and continental European law.

This course examines the key concepts and schools of thought in the study of foreign policy. Concentrating on the process of decision making, internal and external factors which influence foreign policy, and the instruments available to foreign policy decision makers, the course provides students with an understanding of the role and effect that foreign policy has on international politics.

This course examines the relationship between science, policy and society. It explores how different knowledge traditions – the scientific method, post-positivist science, Indigenous and local knowledges, citizen science, and the knowledge-industrial complex – converge or collide in real policy arenas. It focuses on translation and impact: how evidence informs legislation, markets and multilateral agreements; how scientists and communities communicate more effectively with policymakers; and how non-expert knowledge can reframe agendas. Learning is interactive, combining lectures, class discussions and practical activities to critique “truth” claims and better understand evidence-informed action.

This course introduces students to the study of the dynamic interaction between the pursuit of wealth and the pursuit of power in the global economy. The course presents the key concepts and theories of IPE, and how these can be used to understand pressing empirical and economic policy questions facing policymakers and citizens in the 21st century.

In this course, students develop concepts such as convergence, continuity, completeness, compactness, and convexity in the settings of real numbers, Euclidean spaces, and more general metric spaces.

The course discusses probability, distribution theory, and statistical inference. It covers mathematical statistics as important discrete and continuous probability distributions (such as the Binomial, Poisson, Uniform, Exponential, and Normal distributions) and investigates properties of these distributions, including use of the moment generating function. The course discusses point estimation techniques including method of moments, maximum likelihood, and least squares estimation. Statistical hypothesis testing and confidence interval construction follow, along with non-parametric and goodness-of-fit tests and contingency tables. A treatment of linear regression models, featuring the interpretation of computer-generated regression output and implications for prediction are also covered.