Offered by Stanford School of Engineering via Coursera
COURSE DESCRIPTION
Popularized by movies such as “A Beautiful Mind,” game theory is the mathematical modeling of strategic interaction among rational (and irrational) agents. Beyond what we call `games’ in common language, such as chess, poker, soccer, etc., it includes the modeling of conflict among nations, political campaigns, competition among firms, and trading behavior in markets such as the NYSE. How could you begin to model keyword auctions, and peer to peer file-sharing networks, without accounting for the incentives of the people using them? The course will provide the basics: representing games and strategies, the extensive form (which computer scientists call game trees), Bayesian games (modeling things like auctions), repeated and stochastic games, and more. We’ll include a variety of examples including classic games and a few applications.
Topics
Game Theory
Backward Induction
Bayesian Game
Problem Solving
CERTIFICATION
When you finish every course and complete the hands-on project, you’ll earn a Certificate that you can share with prospective employers and your professional network. You can share your Course Certificates in the Certifications section of your LinkedIn profile, on printed resumes, CVs, or other documents.
LEARNING OUTCOMES
By the end of the course, you will be able to perform exploratory data analysis, understand key principles of sampling, and select appropriate tests of significance for multiple contexts. You will gain the foundational skills that prepare you to pursue more advanced topics in statistical thinking and machine learning.
