| 书目名称 | Game Theory for Data Science | | 副标题 | Eliciting Truthful I | | 编辑 | Boi Faltings,Goran Radanovic | | 视频video | | | 丛书名称 | Synthesis Lectures on Artificial Intelligence and Machine Learning | | 图书封面 |  | | 描述 | .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.. | | 出版日期 | Book 2017 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-01577-9 | | isbn_softcover | 978-3-031-00449-0 | | isbn_ebook | 978-3-031-01577-9Series ISSN 1939-4608 Series E-ISSN 1939-4616 | | issn_series | 1939-4608 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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发表于 2025-3-21 17:52:09
