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Titlebook: Game Theory for Data Science; Eliciting Truthful I Boi Faltings,Goran Radanovic Book 2017 The Editor(s) (if applicable) and The Author(s),

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樓主
發(fā)表于 2025-3-21 17:52:09 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Game Theory for Data Science
副標題Eliciting Truthful I
編輯Boi Faltings,Goran Radanovic
視頻videohttp://file.papertrans.cn/381/380499/380499.mp4
叢書名稱Synthesis Lectures on Artificial Intelligence and Machine Learning
圖書封面Titlebook: Game Theory for Data Science; Eliciting Truthful I Boi Faltings,Goran Radanovic Book 2017 The Editor(s) (if applicable) and The Author(s),
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
doihttps://doi.org/10.1007/978-3-031-01577-9
isbn_softcover978-3-031-00449-0
isbn_ebook978-3-031-01577-9Series ISSN 1939-4608 Series E-ISSN 1939-4616
issn_series 1939-4608
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 23:58:55 | 只看該作者
板凳
發(fā)表于 2025-3-22 01:30:13 | 只看該作者
Agents Motivated by Influence,er case is . agents, who want to insert fake data to influence the aggregate for ulterior motives, for example to hide pollution or to influence a decision that is taken based on the data. Following the practice widely adopted in fault-tolerant computing, we consider faulty agents as malicious as well in order to obtain worst-case guarantees.
地板
發(fā)表于 2025-3-22 07:45:07 | 只看該作者
Book 2017te 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 lo
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發(fā)表于 2025-3-22 09:34:37 | 只看該作者
1939-4608 We complement the game-theoretic analysis with practical examples of applications in prediction platforms, community sensing, and peer grading..978-3-031-00449-0978-3-031-01577-9Series ISSN 1939-4608 Series E-ISSN 1939-4616
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發(fā)表于 2025-3-23 00:59:02 | 只看該作者
Mechanisms for Verifiable Information,. This case is actually quite frequent. It includes forecasts of weather, product sales, election outcomes and many other phenomena. Sometimes environmental measurements, such as pollution, can be verified on a cumulative basis at low cost. Crop diseases eventually become apparent.
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