Yoav Shoham, Kevin LeytonBrown, Matthew Jackson — Stanford University
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 filesharing 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 realworld applications. Table of contents
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. Mixedstrategy Nash equilibria: Definitions, examples, realworld evidence. Week 3. Alternate solution concepts: iterative removal of strictly dominated strategies, minimax strategies and the minimax theorem for zerosum game, correlated equilibria. Week 4. Extensiveform games: Perfect information games: trees, players assigned to nodes, payoffs, backward Induction, subgame perfect equilibrium, introduction to imperfectinformation games, mixed versus behavioral strategies. Week 5. Repeated games: Repeated prisoners dilemma, finite and infinite repeated games, limitedaverage versus futurediscounted 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. Course requirements


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