数据科学的博弈论(英文版)/世界博弈论经典

智能系统通常依赖于信息智能体提供的数据， 例如传感器数据或众包计算。因为提供准确和切合 的数据需要付出代价不菲，所以智能体可能并不总 是愿意提供准确的数据。因此，不仅要验证数据的 正确性，还要提供激励机制，以便提供高质量数据 的智能体获得更多奖励。这就是本书的主题——数 据科学中的博弈论。本书研究了不同的激励机制与 各种环境设置，也考虑了声誉机制，并通过在预测 平台、社区感知和同级评分中的应用实例来补充博 弈论分析。

Preface Acknowledgments 1 Introduction 1.1 Motivation 1.1.1 Example: Product Reviews 1.1.2 Example: Forecasting Polls 1.1.3 Example: Community Sensing 1.1.4 Example: Crowdwork 1.2 Quality Control 1.3 Setting 2 Mechanisms for Verifiable Information 2.1 Eliciting a Value 2.2 Eliciting Distributions: Proper Scoring Rules 3 Parametric Mechanisms for Unverifiable Information 3.1 Peer Consistency for Objective Information 3.1.1 Output Agreement 3.1.2 Game-theoretic Analysis 3.2 Peer Consistency for Subjective Information 3.2.1 Peer Prediction Method 3.2.2 Improving Peer Prediction Through Automated Mechanism Design 3.2.3 Geometric Characterization of Peer Prediction Mechanisms 3.3 Common Prior Mechanisms 3.3.1 Shadowing Mechanisms 3.3.2 Peer Truth Serum 3.4 Applications 3.4.1 Peer Prediction for Self-monitoring 3.4.2 Peer Truth Serum Applied to Community Sensing 3.4.3 Peer Truth Serum in Swissnoise 3.4.4 Human Computation 4 Nonparametric Mechanisms: Multiple Reports 4.1 Bayesian Truth Serum 4.2 Robust Bayesian Truth Serum 4.3 Divergence-based BTS 4.4 Two-stage Mechanisms 4.5 Applications 5 Nonparametric Mechanisms: Multiple Tasks 5.1 Correlated Agreement 5.2 Peer Truth Serum for Crowdsourcing (PTSC) 5.3 Logarithmic Peer Truth Serum 5.4 Other Mechanisms 5.5 Applications 5.5.1 Peer Grading: Course Quizzes 5.5.2 Community Sensing 6 Prediction M arkets: Combining Elicitation and Aggregation 7 Agents Motivated by Influence 7.1 Influence Limiter: Use of Ground Truth 7.2 Strategyproof Mechanisms When the Ground Truth is not Accessible 8 Decentralized Machine Learning 8.1 Managing the Information Agents 8.2 From Incentives to Payments 8.3 Integration with Machine Learning Algorithms 8.3.1 Myopic Influence 8.3.2 Bayesian Aggregation into a Histogram 8.3.3 Interpolation by a Model 8.3.4 Learning a Classifier 8.3.5 Privacy Protection 8.3.6 Restrictions on Agent Behavior 9 Conclusions 9.1 Incentives for Quality 9.2 Classifying Peer Consistency Mechanisms 9.3 Information Aggregation 9.4 Future Work Bibliography Authors' Biographies