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Game Theory for Data Science : Eliciting Truthful Information / by Boi Faltings, Goran Radanovic.
- Format:
- Book
- Author/Creator:
- Faltings, Boi., Author.
- Radanovic, Goran., Author.
- Series:
- Synthesis Lectures on Artificial Intelligence and Machine Learning, 1939-4616
- Language:
- English
- Subjects (All):
- Artificial intelligence.
- Machine learning.
- Neural networks (Computer science).
- Artificial Intelligence.
- Machine Learning.
- Mathematical Models of Cognitive Processes and Neural Networks.
- Local Subjects:
- Artificial Intelligence.
- Machine Learning.
- Mathematical Models of Cognitive Processes and Neural Networks.
- Physical Description:
- 1 online resource (XV, 135 p.)
- Edition:
- 1st ed. 2017.
- Place of Publication:
- Cham : Springer Nature Switzerland : Imprint: Springer, 2017.
- Summary:
- 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.
- Contents:
- 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 .
- ISBN:
- 9783031015779
- 3031015770
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