@article{shah2016double,
author = {Shah, Nihar B. and Zhou, Denny},
title = {Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing},
year = {2016},
month = {September},
abstract = {Crowdsourcing has gained immense popularity in machine learning applications for obtaining large amounts of labeled data. Crowdsourcing is cheap and fast, but suffers from the problem of low-quality data. To address this fundamental challenge in crowdsourcing, we propose a simple payment mechanism to incentivize workers to answer only the questions that they are sure of and skip the rest. We show that surprisingly, under a mild and natural no-free-lunch requirement, this mechanism is the one and only incentive-compatible payment mechanism possible. We also show that among all possible incentive- compatible mechanisms (that may or may not satisfy no-free- lunch), our mechanism makes the smallest possible payment to spammers. We further extend our results to a more general setting in which workers are required to provide a quantized confidence for each question. Interestingly, this unique mechanism takes a multiplicative form. The simplicity of the mechanism is an added benefit. In preliminary experiments involving over 900 worker-task pairs, we observe a significant drop in the error rates under this unique mechanism for the same or lower monetary expenditure.},
publisher = {JMLR},
url = {http://approjects.co.za/?big=en-us/research/publication/double-or-nothing-multiplicative-incentive-mechanisms-for-crowdsourcing/},
pages = {1-52},
journal = {Journal of Machine Learning Research},
volume = {17},
number = {165},
}