The introduction to market design discusses the theory and empirical results relevant for the design of multi-object auctions and matching.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Martin Bichler is Professor of Informatics at the Technische Universität München (TUM), and a faculty member at the TUM School of Management. He is best known for his academic work on market design, and he has acted as a consultant for private and public organizations. Projects in which he is involved include the design of auctions for advertising, industrial procurement and logistics, fishery access rights, and spectrum sales. His research addresses algorithmic, game-theoretical, and behavioral questions and has appeared in leading journals in computer science, economics, operations research, and management science. He is currently Editor of Business and Information Systems Engineering and serves on the editorial boards of several academic journals.
Inhaltsangabe
1. Introduction Part I. Microeconomic Fundamentals: 2. Game-theoretical basics 3. Mechanism design 4. Single-object auctions Part II. Multi-Object Auction Design: 5. An overview of multi-object auctions 6. The simultaneous multi-round auction format 7. Sealed-bid multi-object auctions 8. Open multi-object auctions 9. The combinatorial clock auction formats Part III. Approximation and Matching Markets 10. Approximation mechanisms 11. Matching markets 12. Outlook Part IV. Appendices: Mathematical Optimization A. Linear optimization B. Algorithms and complexity.
1. Introduction Part I. Microeconomic Fundamentals: 2. Game-theoretical basics 3. Mechanism design 4. Single-object auctions Part II. Multi-Object Auction Design: 5. An overview of multi-object auctions 6. The simultaneous multi-round auction format 7. Sealed-bid multi-object auctions 8. Open multi-object auctions 9. The combinatorial clock auction formats Part III. Approximation and Matching Markets 10. Approximation mechanisms 11. Matching markets 12. Outlook Part IV. Appendices: Mathematical Optimization A. Linear optimization B. Algorithms and complexity.
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