EC 414 - Applied Game Theory

Lecture Hours: 3
Lab Hours: 0
Credit Hours: 3

This class introduces students to applying the Nash Equilibrium concept in different informational contexts, from full information to imperfect information to asymmetric information. Further, this class forms part of the core of our decision analytics program by ensuring that practitioners can analyze data with a clear theoretical hypothesis. This class is therefore an introduction to applying game theory to making better data based decisions. In addition to game theory students are introduced to programing machine learning models with R. Prerequisite(s): EC 203 (or math equivalent) with a grade of C or better.