Click Here to Download: https://ouo.io/Fp1EYT Game Theory for Data Science Eliciting Truthful Information By: Boi Faltings; Goran Radanovic Publisher: Morgan & Claypool Publishers Print ISBN: 9781627057295, 1627057293 eText ISBN: 9781627056083, 1627056084 Edition: 1st Format: PDF Available from $ 51.96 USD SKU 9781627056083 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.
