Klappentext

This book presents a modern introduction to the field of algorithmic game theory. It places a heavy emphasis on optimization and online learning (a subdiscipline of machine learning), which are tools that increasingly play a central role in both the theory and practice of applying game-theoretic ideas. The book covers the core techniques used in several majorly successful applications, including techniques used for creating superhuman poker AIs, the theory behind the 'pacing' methodology that has become standard in the internet advertising industry, and the application of competitive equilibrium from equal incomes for fair course seat allocation in many business schools. With its focus on online learning tools, this book is an ideal companion to classic texts on algorithmic game theory for graduate students and researchers.

Inhalt

Preface; Notation; Part I. Introductory Material: 1. Introduction and examples; 2. Nash equilibrium introduction; 3. Auctions and mechanism design introduction; Part II. Game Solving and Regret Minimization: 4. Regret minimization and the minimax theorem; 5. Blackwell approachability and regret matching; 6. Self-play via regret minimization; 7. Optimism and fast convergence of self play; 8. Extensive-form games; 9. Stackelberg equilibrium and security games; 10. Fixed-point theorems and equilibrium existence; Part III. Fair Allocation and Market Equilibrium: 11. Fair division and market equilibrium; 12. Computing Fisher market equilibrium; 13. Fair allocation with indivisible goods; 14. Power flows and equilibrium pricing; Part IV. Auctions and Internet Advertising Markets: 15. Internet advertising auctions: position auctions; 16. Auctions with budgets and pacing equilibria; 17. Pacing algorithms for budget management; 18. Demographic fairness; Appendix A. Optimization background; Appendix B. Probability background; References; Index.