This course introduces the fundamental concepts and tools of information theory and discusses their salient applications to analysis of social and economic systems. This goal is pursued in three parts. Students will first be introduced to the core problems, concepts, and formalisms that have defined the development of information theory. This includes debates on the meaning of probability, the analytical challenges posed by complex socioeconomic systems, and the historical development of the concept of “entropy,” from Ludwig Boltzmann and Willard Gibbs to Claude Shannon. Following this introductory discussion, the course turns to the formal development of entropic measures, their canonical applications to physical and communication systems, and their more recent multivariate generalisations. This sets the stage for discussion on three broad areas where information-theoretic tools have been used in socioeconomic research: Statistical-equilibrium models of economic systems, statistical inference, and the measurement of inequalities in social and economic outcomes. Prior to taking this course, students must have taken at least one course in multivariate calculus, and at least one course in econometrics or an equivalent course in statistics.