1 Stanford University: Coursera - Game Theory (2012)
Yoav Shoham, Kevin Leyton-Brown and Matthew O. Jackson
DVDRip | English | MP4 + PDF slides | 960 x 540 | AVC ~71.4 kbps | 29.970 fps
AAC | 128 Kbps | 44.1 KHz | 2 channels | Subs: English (srt) | ~7 hours | 704 MB
Genre: eLearning Video / Lectures: Computer Science: Artificial Intelligence
The course covers the basics: representing games and strategies, the extensive form (which computer scientists call game trees), repeated and stochastic games, coalitional games, and Bayesian games (modeling things like auctions).
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 eBay, Google 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 real-world applications.
Course SyllabusWeek 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.
More Info: https://www.coursera.org/course/gametheory
视频下载：
（1）Coursera.Game.Theory.part1.rar
http://ul.to/hnigihj1
（2） Coursera.Game.Theory.part2.rar
http://ul.to/o4ciykzx
（3 ）Coursera.Game.Theory.part3.rar
http://ul.to/b2pioxhu
2  Stanford University / University of British Columbia - Game Theory II (2013 - week 8-11)
Our 4-week advanced course considers how to design interactions between agents in order to achieve good social outcomes. The course -- which is free and open to the public -- considers three main topics: social choice theory (i.e., collective decision making), mechanism design, and auctions. More specifically, in the first week we consider the problem of aggregating different agents' preferences, discussing voting rules and the challenges faced in collective decision making. We present some of the most important theoretical results in the area: notably, Arrow's Theorem, which proves that there is no "perfect" voting system, and also the Gibbard-Satterthwaite and Muller-Satterthwaite Theorems. We move on to consider the problem of making collective decisions when agents are self interested and can strategically misreport their preferences. We explain "mechanism design" -- a broad framework for designing interactions between self-interested agents -- and give some key theoretical results. Our third week focuses on the problem of designing mechanisms to maximize aggregate happiness across agents, and presents the powerful family of Vickrey-Clarke-Groves mechanisms. The course wraps up with a fourth week that considers the problem of allocating scarce resources among self-interested agents, and that provides an introduction to auction theory.
This course is a follow-up to a more basic course in which we provided the foundations to game theory, covering topics such as representing games and strategies, the extensive form, Bayesian games, repeated and stochastic games, and more. Although to a substantial extent our new course stands alone, some of the previous material -- e.g., Bayesian games, Nash equilibrium, and dominant strategies -- is needed for this more advanced course, whether picked up through our previous course or elsewhere.

Instructors: Matthew Jackson, Kevin Leyton-Brown, Yoav Shoham

Courses list:
Week 1: Social Choice
Release date May 26, midnight PDT

Week 2: Mechanism Design
Release date June 2, midnight PDT

Week 3: Efficient Mechanisms
Release date June 9, midnight PDT

Week 4: Auctions
Release date June 16, midnight PDT
视频下载：
http://filepost.com/files/851fabmb/Stanford_-_Game_Theory_II_2013.part01.rar
http://filepost.com/files/mafd27dm/Stanford_-_Game_Theory_II_2013.part02.rar
http://filepost.com/files/9cad1853/Stanford_-_Game_Theory_II_2013.part03.rar
http://filepost.com/files/bc636f6f/Stanford_-_Game_Theory_II_2013.part04.rar
http://filepost.com/files/1a46ecbf/Stanford_-_Game_Theory_II_2013.part05.rar
http://filepost.com/files/975b6ad1/Stanford_-_Game_Theory_II_2013.part06.rar
http://filepost.com/files/c2754353/Stanford_-_Game_Theory_II_2013.part07.rar
http://filepost.com/files/99297d28/Stanford_-_Game_Theory_II_2013.part08.rar

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