Game Theory for Data Science - Eliciting Truthful Information (Paperback)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Synthesis Lectures on Artificial Intelligence and Machine Learning
Release date:	September 2017
First published:	2017
Authors:	Boi Faltings • Goran Radanovic
Dimensions:	235 x 191mm (L x W)
Format:	Paperback
Pages:	135
ISBN-13:	978-3-03-100449-0
Languages:	English
Subtitles:	English
Categories:	Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematical modelling Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	3-03-100449-3
Barcode:	9783031004490

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