Game Theory for Data Science

Eliciting Truthful Information

Nonfiction, Science & Nature, Mathematics, Game Theory, Computers, Advanced Computing, Artificial Intelligence, Computer Science
Cover of the book Game Theory for Data Science by Boi Faltings, Goran Radanovic, Ronald Brachman, Peter Stone, Morgan & Claypool Publishers
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Author: Boi Faltings, Goran Radanovic, Ronald Brachman, Peter Stone ISBN: 9781681731957
Publisher: Morgan & Claypool Publishers Publication: September 19, 2017
Imprint: Morgan & Claypool Publishers Language: English
Author: Boi Faltings, Goran Radanovic, Ronald Brachman, Peter Stone
ISBN: 9781681731957
Publisher: Morgan & Claypool Publishers
Publication: September 19, 2017
Imprint: Morgan & Claypool Publishers
Language: English

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.

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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.

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