Game Theory I • Static Games
Lead instructor: Justin Grana
 About the Tutorial:
Game theory is the standard quantitative tool for analyzing the interactions of multiple decision makers. Its applications extend to economics, biology, engineering and even cyber security. Furthermore, many complex systems involve multiple decision makers and thus a full analysis of such systems necessitates the tools of game theory. This course is designed to provide a highlevel introduction to static, noncooperative game theory. The main goal of this course is to introduce students to the idea of a Nash Equilibrium and how the Nash Equilibrium solution concept can be applied to a number of scenarios. Students are assumed to be familiar with the concept of expected value and the basics of probability. While calculus is not required for the majority of the course, lesson 7 focuses on an example that employs calculus. However, lesson 7 can be skipped without any harm in understanding lessons 8 − 10.
 About the Instructor(s):
Justin Grana is a postdoctoral fellow at the Santa Fe Institute. He earned a Ph.D. in economics from American University in 2016 and his research focuses on how the timing of events and decisions impacts interacting humans. To apply his research, Justin has analyzed computer network attacks, collusive cartel formation and air traffic control scenarios.
 How to use Complexity Explorer:
 How to use Complexity Explorer
 Enrolled students:

2,669
 Participant map:
 Prerequisites:

Basic Probability
 Like this tutorial?
 Donate to help fund more like it
 Twitter link
Syllabus
 What is Game Theory?
 Elements of a Game and the Normal Form Representation
 Nash Equilibrium
 Two Examples  Dominant Strategies and Coordination Games
 Mixed Strategy Nash Equilibrium
 Brief Historical Interlude
 A CalculusBased Example
 Bayesian Games: Introducing Uncertainty
 Example: Cyber Security
 Example: Tragedy of the Commons
 Summary