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.
You can find a full syllabus and description of the course here: http://web.stanford.edu/~jacksonm/GTOC-Syllabus.html
There is also an advanced follow-up course to this one, for people already familiar with game theory: https://www.coursera.org/learn/gametheory2/
You can find an introductory video here: http://web.stanford.edu/~jacksonm/Intro_Networks.mp4
Overview
Syllabus
- Week 1: Introduction and Overview
- Introduction, overview, uses of game theory, some applications and examples, and formal definitions of: the normal form, payoffs, strategies, pure strategy Nash equilibrium, dominant strategies
- Week 2: Mixed-Strategy Nash Equilibrium
- pure and mixed strategy Nash equilibria
- Week 3: Alternate Solution Concepts
- Iterative removal of strictly dominated strategies, minimax strategies and the minimax theorem for zero-sum game, correlated equilibria
- Week 4: Extensive-Form Games
- Perfect information games: trees, players assigned to nodes, payoffs, backward Induction, subgame perfect equilibrium, introduction to imperfect-information games, mixed versus behavioral strategies.
- Week 5: Repeated Games
- Repeated prisoners dilemma, finite and infinite repeated games, limited-average versus future-discounted reward, folk theorems, stochastic games and learning.
- Week 6: Bayesian Games
- General definitions, ex ante/interim Bayesian Nash equilibrium.
- Week 7: Coalitional Games
- Transferable utility cooperative games, Shapley value, Core, applications.
- Week 8: Final Exam
- The description goes here
Taught by
Matthew O. Jackson and Yoav Shoham
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Reviews
3.6 rating, based on 35 Class Central reviews
4.6 rating at Coursera based on 4741 ratings
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All three of the professors present the material, but not in a way that is easy to understand. I mention this because there are no prerequisites required for the class. Once the material is presented in this class, I found that searching via google…
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I was looking forward to this course - a lot. But unfortunately the lectures are very hard to follow for someone with university degrees but no mathematical/game theory background. Random internet sources, including YouTube videos, were making it easier for me to understand some of the topics - but it was basically taking up too much of my time to constantly look for other sources. I wish they would film another (and better) series of lectures for this course.
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I really wanted to like this class but found the variation in the lecture styles between the three prof's distracting. One was cool, one was interesting, and one was so bad he sucked every ounce of enthusiasm for the subject right out of me. The leverage in online courses should allow only the best presenters to teach. Dr. Shoham acted like he wanted to be anywhere but sharing his genius with a dullard like me.
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Extremely poor presentation of the material. Look elsewhere. Presenters made many errors and often could not accurately verbalize (hence conceptualize) the material. Professor Jackson was top notch but the other presenters just did not measure up. I have no doubt that each presenter is an expert in his field, but with the exception of Professor Jackson they have a very limited understanding of how to present new material to adult learners.
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I have mixed feelings about this course. The material is inherently interesting to me but the three professors have a way sucking the fun out of it with an excess of mathematical formalism. Their lecturing ability runs the gamut from poor to atrocious. I learned quite a lot despite their best efforts, but I couldn't recommend the course to anyone without a love of math and a passion for the subject.
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This is computer science theory course. It focuses on the formal mathematical concepts and shies away from applications. Don't be fooled by the title, it's not fun along with games. The course is highly quantitative and you will need a solid algebra and probability knowledge to feel comfortable. But if you're aiming fro MSCS , take it.
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Interesting material, however, I spent more time googling concepts than listening to lectures though as that was a better way to learn. They tried to teach the online course like a University classroom ignoring the obvious differences. This is basically just a collection of online videos about a subject I wouldn't call it a 'course' at all. There's such better ways to teach online.
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My background is computer science and I'm interested in this topic in relation to machine learning applications. I have no background in economics, but in mathematics.
The course was very interesting for my interests and I will start now with the second part.
I gave four stars only, because the course is somehow incomplete without the book which I used in addition to the course material. -
With no prerequisites required, I jumped right in.
The material was okay. The quizzes , though, were out of this world. I don’t know math, like, at all. I am a linguist, I have never really used anything more than basic maths and I am not good at logic.
Definitely not for me. Checking if the Internet has better answers. Going to take the first week again and will probably drop it if I am still clueless when I retake the quiz. -
Aryan completed this course, spending 5 hours a week on it and found the course difficulty to be moderate.
Many parts if the course (like regarding P and NP problems) are to complex and presented in a way that us not at all student friendly. Overall an average course. -
Very interesting course. The teachers do a good job explaining the different aspects of game theory. Difficulty increasing at he end of the course, you need some time to get it right. I had a good time with this course!!
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Fascinating topic, well presented. Homework and exams were a good learning experience.
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Super boring and super slow.
It is the type of course that makes you think you don't like the topic because of the way it is presented.
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Osama HebaThis is an academic level course. I found it a bit boring, especially that I lacked enough background and that I was exploring rather than studying the topic. Anyways, it delivers -very well- the topics enlisted in the syllabus. Moreover, it has a sequel that covers even more advanced topics, which I audited it too, and it was as good. -
What I was looking for to complete some economics studies ;
Give the basics ; it is well summarized ; examples are easy to understand and well chosen.
However, still missing some more mathematical proofs but the course can be completed with books.
Very useful. thanks ! -
I changed my view on games completely, it was a great experience. The only flaw I found is the Bayesian game explanation.
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Paweł Klimczuk
