The Economics Minor introduces students to the basic concepts, theories, modeling approaches, and methods of economics. The 15 credit minor consists of two required field courses, a required methodology course, a modeling course that can be satisfied by taking one of two courses, and an elective from economics or finance.
- ECON 2001 Introduction to Microeconomics
- ECON 2102 Introduction to Macroeconomics
- ECON 2250 Economic Methods
- ECON 4844 Game Theory or ECON 4845 Agents in the Matrix
- One course to be selected from the 2000- or higher-level offerings in economics (ECON) or finance (FIN).
ECON2001 Problems of consumer behavior and demand, the allocation of resources of production, factor pricing and market conduct under pure competition, imperfect competition, oligopoly and monopoly.
ECON2102 A comprehensive introduction to macroeconomics. The concept of national income analysis, the theory of determination of income and employment, problems of fiscal and monetary policy and aspects of international economic activity.
ECON2250 This course provides an introduction to the theories and methods underlying modern social science research across sociology, political science, economics and criminology. In this course, students will learn to assess the validity of social science research, and design their own research projects, using a variety of qualitative and quantitative techniques such as: ethnography, content analysis, experiments and surveys. Students are required to have completed Math 1126 or Math 1128 and should have this level of Mathematical skill in order to succeed in the required Methodological course.
ECON4844 This course is an introduction to some basic concepts in non-cooperative game theory and their application to a range of problems in several social science disciplines, including economics, criminology, political science and sociology.
ECON4845 Agents in the Matrix: Creating & Analyzing Agent Based Models This course introduces to a powerful transdisciplinary computational tool for exploring complex systems; a large number of autonomous agents interacting independently without central control. Rooted in mathematics and computer science, agent-based modeling (ABM) is one of the most widely used methodologies for simulating complex systems by drawing on computing power to run experiments. The introduction to ABM in this course is more than just learning about the methodology; students will also learn how to design, program, execute and analyze their own ABM and then write up and present the results.