Game Theory for Data Science: Eliciting Truthful Information (Synthesis Lectures on Artificial Intelligence and Machine Learning): Faltings, Boi, Radanovic, Goran, Brachman, Ronald: 9781627057295: Amazon.com: Books

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.