Game theory for data science : eliciting truthful information
著者
書誌事項
Game theory for data science : eliciting truthful information
（Synthesis lectures on artificial intelligence and machine learning, #35）
Morgan & Claypool, c2017
- : pbk
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注記
Includes bibliographical references (p. 127-133)
内容説明・目次
内容説明
Intelligent systems often depend on data provided by information agents, for example, sensor data or crowdsourced human computation. Providing accurate and relevant data requires costly effort that agents may not always be willing to provide. Thus, it becomes important not only to verify the correctness of data, but also to provide incentives so that agents that provide high-quality data are rewarded while those that do not are discouraged by low rewards.
We cover different settings and the assumptions they admit, including sensing, human computation, peer grading, reviews, and predictions. We survey different incentive mechanisms, including proper scoring rules, prediction markets and peer prediction, Bayesian Truth Serum, Peer Truth Serum, Correlated Agreement, and the settings where each of them would be suitable. As an alternative, we also consider reputation mechanisms. We complement the game-theoretic analysis with practical examples of applications in prediction platforms, community sensing, and peer grading.
目次
Preface
Acknowledgments
Introduction
Mechanisms for Verifiable Information
Parametric Mechanisms for Unverifiable Information
Nonparametric Mechanisms: Multiple Reports
Nonparametric Mechanisms: Multiple Tasks
Prediction Markets: Combining Elicitation and Aggregation
Agents Motivated by Influence
Decentralized Machine Learning
Conclusions
Bibliography
Authors' Biographies
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