Overview
Part of the book series: Synthesis Lectures on Artificial Intelligence and Machine Learning (SLAIML)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (9 chapters)
-
Front Matter
-
Back Matter
About this book
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.
Authors and Affiliations
About the authors
Bibliographic Information
Book Title: Game Theory for Data Science
Book Subtitle: Eliciting Truthful Information
Authors: Boi Faltings, Goran Radanovic
Series Title: Synthesis Lectures on Artificial Intelligence and Machine Learning
DOI: https://doi.org/10.1007/978-3-031-01577-9
Publisher: Springer Cham
eBook Packages: Synthesis Collection of Technology (R0), eBColl Synthesis Collection 7
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2017
Softcover ISBN: 978-3-031-00449-0Published: 19 September 2017
eBook ISBN: 978-3-031-01577-9Published: 31 May 2022
Series ISSN: 1939-4608
Series E-ISSN: 1939-4616
Edition Number: 1
Number of Pages: XV, 135
Topics: Artificial Intelligence, Machine Learning, Mathematical Models of Cognitive Processes and Neural Networks