Market design: auctions and matching
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge, Massachusetts
MIT Press
[2017]
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030306254&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Beschreibung: | Enthält Literaturnachweise |
| Umfang: | xvii, 373 Seiten Illustrationen |
| ISBN: | 9780262037549 |
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| adam_text | Contents
Preface xv
Introduction 1
i.i Market and Market Design 1
1.2 Do We Necessarily Have Prices? 2
1.3 More on Markets 3
1.3.1 What a Market Needs to Work
1.3.2 Commodities 5
1.4 Market Design: First Example 6
1.4.1 Feeding America 6
1.4.2 The First Design 7
1.4.3 The Problems... 9
1.4.4 ... and a Solution 10
1.4.5 Results 11
Simple Auctions 13
2.1 Introduction 13
2.1.1 Auctions: A Definition 14
2.1.2 Auctions Are Everywhere 14
2.2 Valuations 16
2.3 Payoffs and Objectives 17
2.4 Ascending Auctions 18
2.4.1 The English Auctions 18
2.4.2 Bids, Strategies, and Payoffs in the English Auctions
2.4.3 Ticking Price 22
2.4.4 Truthful Bidding 23
2.5 Second-Price Auction 25
2.5.1 The Essence of the English Auction 25
2.5.2 The Vickrey Auction 26
2.5.3 English versus Second-Price Auctions 28
Contents
2.6 First-Price Auction 29
2.6.1 Definition 29
2.6.2 Optimal Bids in the First-Price Auction 30
2.6.3 Dutch Auction 34
2.7 Revenue Equivalence 36
2.8 Reserve Price 40
2.8.1 Optimal Reserve Price 42
2.8.2 Reserve Price versus Adding More Bidders 45
An Analysis of eBay 49
3.1 Introduction 49
3.2 eBay in Detail 50
3.2.1 Proxy Bidding 50
3.2.2 Bids and Bid Increments 51
3.2.3 Updating Rules 51
3.3 eBay as a First-Price Auction? 54
3.4 Bid Sniping 54
3.4.1 Amazon versus eBay 55
3.4.2 Data Analysis 56
3.5 Reserve Price 59
The Vickrey-Clarke-Groves Auction 61
4.1 Introduction 61
4.2 The Model 61
4.3 The VCG Auction 63
4.3.1 Computing the Optimal Assignment 63
4.3.2 Calculating Prices 66
4.4 Incentives under the VCG Auction 67
4.5 Relation with the Vickrey Auction 68
4.6 The Complexity of the VCG Auction 69
Keyword Auctions 71
5.1 Introduction 71
5.2 Running Billions and Billions of Auctions 73
5.3 The Origins 74
5.3.1 Payoff Flows 75
5.4 The Google Model: Generalized Second-Price Auction 76
5.4.1 Quality Scores 76
5.4.2 Truthtelling under the GSP Auction 78
5.4.3 Equilibrium under the GSP Auction 78
Contents
ix
5.4.4 Assumptions about the Long-Run Equilibria 79
5.4.5 Refinement: Envy-Free Equilibrium 79
5.4.6 The Generalized English Auction 88
5.5 The Facebook Model: VCG for Internet Ads 91
5.5.1 Comparing VCG and GSP Auctions 91
5.5.2 The Rationale for VCG 94
6
Spectrum Auctions 95
6.1 How Can Spectrum Be Allocated?
6.1.1 Lotteries 95
6.1.2 Beauty Contests 96
6.1.3 Why Run Auctions? 97
6.2 Issues 98
95
6.2.1 General Issues 98
6.2.2 Collusion, Demand Reduction, and Entry
6.2.3 Maximum Revenue 101
6.2.4 The Exposure Problem 102
6.2.5 Winner s Curse 103
6.3 The Simultaneous Ascending Auction 104
6.4 Case Studies 106
99
6.4.1 The U.S. 1994 PCS Broadband Auction 106
6.4.2 Mistakes 109
7 Financial Markets 113
7.1 Introduction 113
7.2 Treasury Auctions 113
7.2.1 Outline 113
7.2.2 How Treasury Auctions Work 115
7.2.3 Analysis 118
7.3 Double Auctions 119
7.4 Initial Public Offering 123
7.4.1 Allocation and Pricing through Contracts 124
7.4.2 Auctions for IPOs 124
8 Trading 127
8.1 Stock Markets 127
8.2 Opening and Closing 132
8.3 High-Frequency Trading 135
8.3.1 Market Structure 135
8.3.2 Market Regulation 137
X
Contents
8.3.3 Surfing on the Latency 139
8.3.4 What Is the Matter with High Frequencies? 141
8.3.5 A Flawed Market Design? 143
8.4 Alternative Market Designs 145
8.4.1 Slowing Down Markets? 145
8.4.2 Frequent Batch Auctions 147
9 The Basic Matching Model 149
9.1 The Basic Matching Model 149
9.1.1 Preferences 150
9.1.2 Matching 152
9.1.3 Stability 153
9.2 Algorithms and Mechanisms 156
9.2.1 Algorithms? 156
9.2.2 Matching Mechanism 159
9.3 Finding Stable Matchings 159
9.3.1 The Deferred Acceptance Algorithm 160
9.3.2 Deferred Acceptance and Stable Matchings 162
9.4 Preferences Over Stable Matchings 164
9.4.1 Musician-Optimal and Singer-Optimal Matchings 164
9.4.2 Proofs 165
9.5 Incentives with the Deferred Acceptance Algorithm 167
10 The Medical Match 173
10.1 History 173
10.2 The Many-to-One Matching Model 175
10.2.1 Preferences in the Many-to-One Matching Model 176
10.2.2 Matchings and Stability in a Many-to-One
Matching Model 179
10.2.3 Finding Stable Matchings 182
10.2.4 One-to-One v. Many-to-One Matchings: Similarities and
Differences 184
10.3 Why Stability Matters 186
10.3.1 A Natural Experiment 186
10.3.2 Unraveling in the Lab 187
10.4 The Rural Hospital Theorem 190
10.5 The Case of Couples and the Engineering Method 192
10.5.1 A Very Complex Problem 192
10.5.2 When Theory Fails 193
10.5.3 Fixing the NRMP 194
Contents
xi
11 Assignment Problems 197
11.1 The Basic Model 197
11.1.1 Public versus Private Endowments 198
11.1.2 Evaluating Assignments 198
11.2 Finding Efficient Assignments 199
11.2.1 Serial Dictators 199
11.2.2 Trading Cycles 200
11.2.3 Implementing Allocation Rules 205
11.2.4 Individual Rationality and the Core 210
11.3 Mixed Public-Private Endowments 212
11.3.1 Inefficient Mechanisms 213
11.3.2 Two Efficient Solutions 217
12 Probabilistic Assignments 223
12.1 Random Assignments 223
12.1.1 Preliminaries 223
12.1.2 The Birkhoff-von Neumann Theorem 225
12.1.3 Evaluating Random Assignments 227
12.2 Random Serial Dictatorship 229
12.3 The Probabilistic Serial Mechanism 231
13 School Choice 239
13.1 The Many-to-One Assignment Model 239
13.1.1 Preferences versus Priorities 239
13.1.2 The Model 241
13.1.3 Assignments 241
13.1.4 Stability and Efficiency 242
13.2 Competing Algorithms 245
13.2.1 The Role of Each Side of the Market 245
13.2.2 The Deferred Acceptance Algorithm 246
13.2.3 The Immediate Acceptance Algorithm 248
13.2.4 Top Trading Cycles 250
13.3 The Problem with the Immediate Acceptance Algorithm 256
13.4 Applications 257
13.4.1 The Boston School Match 257
13.4.2 The New York City School Match 259 14
14 School Choice: Further Developments 263
14.1 Weak Priorities 263
14.1.1 The Problem 263
14.1.2 Efficiency Loss 264
Contents
14.1.3 The Student-Optimal Assignment with
Weak Priorities 265
14.1.4 Restoring Efficiency 266
14.1.5 How to Break Ties If You Must 272
14.2 Constrained Choice 276
14.2.1 Issues 277
14.2.2 From Very Manipulable to Less Manipulable 279
15 Course Allocation 283
15.1 Preliminaries 283
15.2 Bidding for Courses 284
15.2.1 The Bidding and Allocation Process 285
15.2.2 Issues: Nonmarket Prices and Inefficiency 286
15.2.3 Deferred Acceptance with Bids 288
15.3 The Harvard Business School Method 290
15.3.1 The Harvard Draft Mechanism 290
15.3.2 Strategic Behavior 291
15.3.3 Welfare 293
15.4 The Wharton Method 295
15.4.1 Approximate Competitive Equilibrium from
Equal Incomes 296
15.4.2 The Wharton Experiment 298
16 Kidney Exchange 303
16.1 Background 303
16.2 Trading Kidneys 305
16.2.1 Trades versus Waiting List 306
16.2.2 The Kidney Exchange Algorithm 306
16.2.3 Chain Selection Rules 309
16.2.4 Efficiency and Incentives 311
16.3 On the Number of Exchanges 313
Appendix A: Game Theory 317
A.l Strategic Form Games 317
A. 1.1 Definition 317
A.l.2 Pure and Mixed Strategies 318
A.2 Extensive Form Games 319
A.2.1 Definition 320
A.2.2 Strategies 321
A.2.3 Imperfect Information 322
Contents
xiii
A.3 Solving Gaines 324
A.3.1 Dominated and Dominant Strategies 324
A.3.2 Elimination of Dominated Strategies 326
A.3.3 Nash Equilibrium 328
A. 4 Bayesian Games: Games with Incomplete Information 330
A.4.1 Introductory Example 330
A. 4.2 Definition 331
Appendix B: Mechanism Design 335
B. l Preliminaries 335
B.2 The Model 337
B. 2.1 Mechanism 337
B.2.2 Implementing Social Choice Functions 339
B.2.3 Direct versus Indirect Mechanism 340
B.3 Dominant Strategy Implementation 341
B.3.1 The Revelation Principle 341
B.3.2 The Gibbard-Satterthwaite Theorem 342
B.3.3 The Vickrey-CIarke-Groves Mechanism 343
B. 4 Bayesian Mechanism Design 344
B.4.1 Bayesian Incentive Compatibility 344
B. 4.2 Trading: The Myerson-Satterthwaite Theorem 345
Appendix C: Order Statistics 349
C. l Expected Highest Valuation 349
C. 1.1 Obtaining the Cumulative Density Function 350
C.l.2 Obtaining the Probability Density Function 350
C.l.3 Calculate the Expectation 350
C.2 Expected Second-Highest Valuation 351
C.3 Conditional Expectation of the Highest Valuation 352
C.4 Changing the Upper and Lower Bounds 353
Notes 355
References 365
Index 369
|
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| illustrated | Illustrated |
| indexdate | 2024-12-20T18:14:03Z |
| institution | BVB |
| isbn | 9780262037549 |
| language | English |
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| spellingShingle | Haeringer, Guillaume Market design auctions and matching Auktion (DE-588)4125859-9 gnd Marktstruktur (DE-588)4037647-3 gnd Gestaltung (DE-588)4157139-3 gnd Theorie (DE-588)4059787-8 gnd Markt (DE-588)4037621-7 gnd Mikroökonomie (DE-588)4039225-9 gnd |
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| title | Market design auctions and matching |
| title_auth | Market design auctions and matching |
| title_exact_search | Market design auctions and matching |
| title_full | Market design auctions and matching Guillaume Haeringer |
| title_fullStr | Market design auctions and matching Guillaume Haeringer |
| title_full_unstemmed | Market design auctions and matching Guillaume Haeringer |
| title_short | Market design |
| title_sort | market design auctions and matching |
| title_sub | auctions and matching |
| topic | Auktion (DE-588)4125859-9 gnd Marktstruktur (DE-588)4037647-3 gnd Gestaltung (DE-588)4157139-3 gnd Theorie (DE-588)4059787-8 gnd Markt (DE-588)4037621-7 gnd Mikroökonomie (DE-588)4039225-9 gnd |
| topic_facet | Auktion Marktstruktur Gestaltung Theorie Markt Mikroökonomie |
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