Essentials of Game Theory

1. Front Matter

   Pages i-xvi

   PDF

2. Games in Normal Form

   • Kevin Leyton-Brown, Yoav Shoham

   Pages 1-8

3. Analyzing Games: From Optimality To Equilibrium

   • Kevin Leyton-Brown, Yoav Shoham

   Pages 9-14

4. Further Solution Concepts for Normal-Form Games

   • Kevin Leyton-Brown, Yoav Shoham

   Pages 15-30

5. Games With Sequential Actions: The Perfect-Information Extensive Form

   • Kevin Leyton-Brown, Yoav Shoham

   Pages 31-40

6. Generalizing the Extensive Form: Imperfect-Information Games

   • Kevin Leyton-Brown, Yoav Shoham

   Pages 41-48

7. Repeated and Stochastic Games

   • Kevin Leyton-Brown, Yoav Shoham

   Pages 49-55

8. Uncertainty About Payoffs: Bayesian Games

   • Kevin Leyton-Brown, Yoav Shoham

   Pages 57-68

9. Coalitional Game Theory

   • Kevin Leyton-Brown, Yoav Shoham

   Pages 69-77

10. Back Matter

    Pages 79-88

    PDF

Game theory is the mathematical study of interaction among independent, self-interested agents. The audience for game theory has grown dramatically in recent years, and now spans disciplines as diverse as political science, biology, psychology, economics, linguistics, sociology, and computer science, among others. What has been missing is a relatively short introduction to the field covering the common basis that anyone with a professional interest in game theory is likely to require. Such a text would minimize notation, ruthlessly focus on essentials, and yet not sacrifice rigor. This Synthesis Lecture aims to fill this gap by providing a concise and accessible introduction to the field. It covers the main classes of games, their representations, and the main concepts used to analyze them.

• University of British Columbia, Vancouver, Canada

  Kevin Leyton-Brown

• Stanford University, Palo Alto, USA

  Yoav Shoham

Kevin Leyton-Brown is a professor of Computer Science at the University of British Columbia and an associate member of the Vancouver School of Economics. He holds a PhD and M.Sc. from Stanford University (2003; 2001) and a B.Sc. from McMaster University (1998). He studies the intersection of computer science and microeconomics, addressing computational problems in economic contexts and incentive issues in multiagent systems. He also applies machine learning to the automated design and analysis of algorithms for solving hard computational problems.

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