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Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing

机译:双重或一无所有:众包的激励机制

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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.
机译:众包在获取大量标记数据的机器学习应用程序中获得了极大的普及。众包既便宜又快速,但存在数据质量低下的问题。为了应对众包中的这一基本挑战,我们提出了一种简单的付款机制来激励员工仅回答他们确定的问题,而忽略其余的问题。我们令人惊讶地表明,在温和自然的“无免费午餐”要求下,该机制是唯一可能的与激励兼容的支付机制。我们还表明,在所有可能的激励兼容机制(可能满足或可能不满足免费午餐)中,我们的机制向垃圾邮件发送者支付的费用最小。有趣的是,这种独特的机制采用“乘法”形式。该机制的简单性是一个额外的好处。在涉及数百名工人的初步实验中,我们观察到在相同或较低货币支出的独特机制下,错误率显着降低。

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