@inproceedings{shah2015double,
author = {Shah, Nihar Bhadresh and Zhou, Denny},
title = {Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing},
booktitle = {Advances in Neural Information Processing Systems 28 (NIPS 2015)},
year = {2015},
month = {December},
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. Interestingly, this unique mechanism takes a multiplicative form. The simplicity of the mechanism is an added benefit. In preliminary experiments involving over several hundred workers, we observe a significant reduction in the error rates under our unique mechanism for the same or lower monetary expenditure.},
url = {http://approjects.co.za/?big=en-us/research/publication/double-nothing-multiplicative-incentive-mechanisms-crowdsourcing/},
edition = {Advances in Neural Information Processing Systems 28 (NIPS 2015)},
}