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About the content
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
Syllabus
Week 1. Introduction: Introduction, overview, uses of game theory, some applications and examples, and formal definitions of: the normal form, payoffs, strategies, pure strategy Nash equilibrium, dominated strategies.
Week 2. Mixed-strategy Nash equilibria: Definitions, examples, real-world evidence.
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. Coalitional games: Transferable utility cooperative games, Shapley value, Core, applications.
Week 7. Bayesian games: General definitions, ex ante/interim Bayesian Nash equilibrium.
Instructors
Matthew O. Jackson
Professor
Economics
Kevin Leyton-Brown
Professor
Computer Science
Yoav Shoham
Professor
Computer Science
Content Designer
Platform
Coursera is a digital company offering massive open online course founded by computer teachers Andrew Ng and Daphne Koller Stanford University, located in Mountain View, California.
Coursera works with top universities and organizations to make some of their courses available online, and offers courses in many subjects, including: physics, engineering, humanities, medicine, biology, social sciences, mathematics, business, computer science, digital marketing, data science, and other subjects.
Мне показалось странным, что в итоговом экзамене были вопросы, которые освещались в материалах для отличников.
Мне показалось странным, что в итоговом экзамене были вопросы, которые освещались в материалах для отличников.
Очень хорош в качестве примеров - яркие и живые, но иногда теории не хватает. Но если вы параллельно читаете учебник, то саме то.
Превосходный курс. Отличный лектор. Разве что на последних лекциях требуется чуть больше математической подготовки. Плюс в последних лекциях мало примеров разобрано, но так наверное даже лучше - можно самому подумать.
Это было сложно... То, что вначале показалось простым и очевидным на пятой неделе превратилось в какой-то неразрешимый кошмар, над которым я бился две недели: пересмотрел лекции, наверное, раз пять, прежде чем сумел более или менее понять материал. Но, в целом, огромное спасибо авторам курса за интересный материал.
Доступное изложение и огромный простор для творчества слушателей, блестящий курс!
