Table of Contents

Chapter 1 Introduction 1
1.1 Mechanism Design 2
1.1.1 Social Choice Function 2
1.1.2 Mechanism 2
1.1.3 Implementation 3
1.1.4 Revelation Principle 4
1.1.5 Efficient Mechanisms 5
1.2 Auctions 7
1.3 Why AI-Driven 11
1.3.1 Challenges in Auction Design 11
1.3.2 The AI-Driven Framework 12
1.4 Organization of the Book 13
References 14
Chapter 2 Multi-Dimensional Mechanism Design via AI-Driven Approaches 16
2.1 Recovering Optimal Mechanisms with Simple Neural Networks 16
2.1.1 Background 17
2.1.2 Setting 19
2.1.3 Revisiting the Na"i ve Mechanism 21
2.1.4 Network Structure of MenuNet 24
2.1.5 Recovering Known Results 27
2.2 Discovering Unknown Optimal Mechanisms 30
2.2.1 Experiment Results 31
2.2.2 Theoretic Analysis and Formal Proofs 34
2.3 Performance 52
References 56
Chapter 3 Dynamic Mechanism Design via AI-Driven Approaches 59
3.1 Dynamic Cost-Per-Action Auctions with Ex-Post IR Guarantees 60
3.1.1 Background 60
3.1.2 Our Contributions 62
3.1.3 Related Works 63
3.1.4 Setting and Preliminaries 64
3.1.5 Mechanisms 70
3.1.6 Truthfulness and Implementation 74
3.1.7 Impossibility Result 80
3.2 Dynamic Reserve Pricing via Reinforcement Mechanism Design 80
3.2.1 Background 81
3.2.2 Settings and Preliminaries 86
3.2.3 Bidder Behavior Model 88
3.2.4 Dynamic Mechanism Design as Markov Decision Process 93
References 103
Chapter 4 Multi-Objective Mechanism Design via AI-Driven Approaches 109
4.1 Balancing Objectives through Approximation Analysis 110
4.1.1 Background 110
4.1.2 Settings and Preliminaries 113
4.1.3 Generalized Virtual-Efficient Mechanisms 114
4.1.4 Experiments 126
4.2 Balancing Objectives through Machine Learning 128
4.2.1 Background 129
4.2.2 Market Clearing Loss 132
4.2.3 Theoretical Guarantees 138
4.2.4 Empirical Evaluation 140
References 146
Chapter 5 Summary and Future Directions 151
References 153