鼓励高质量的群众工作
Incentivizing High Quality Crowdwork
摘要：
我们研究了财务激励对众包工作质量的因果影响。我们专注于基于绩效的薪酬（PBPs），即向生产高质量工作的工人发放奖金。我们在流行的众包平台Amazon Mechanical Turk上设计和运行随机行为实验，目的是了解PBPs何时、何地以及为什么会有帮助，确定支付的属性、支付结构以及使其最有效的任务本身。我们提供了PBPs确实提高质量的任务的例子。对于这些任务，PBPs的有效性对获得奖金所需的质量阈值并不太敏感，而奖金的大小必须足够大，以使奖励显着。我们还提出了PBPs不能提高质量的任务的例子。我们的研究结果表明，PBPs提高质量，任务必须努力响应：任务必须允许工人通过付出更多的努力来生产更高质量的工作。我们还给出了一个简单的方法来确定一个任务是否是努力响应的先验。此外，我们的实验表明，土耳其机械公司的所有报酬在某种程度上都是基于绩效的，因为工人们认为，如果他们的绩效足够差，他们的工作可能会被拒绝。最后，我们提出了一个新的工人行为模型，扩展了标准的委托代理模型从经济学，包括工人的主观信念，他的可能性被支付，并表明，这个模型的预测是符合我们的实验结果。该模型可作为众包市场激励机制理论研究的基础。
英文摘要：
We study the causal effects of financial incentives on the quality of crowdwork. We focus on performance-based payments (PBPs), bonus payments awarded to workers for producing high quality work. We design and run randomized behavioral experiments on the popular crowdsourcing platform Amazon Mechanical Turk with the goal of understanding when, where, and why PBPs help, identifying properties of the payment, payment structure, and the task itself that make them most effective. We provide examples of tasks for which PBPs do improve quality. For such tasks, the effectiveness of PBPs is not too sensitive to the threshold for quality required to receive the bonus, while the magnitude of the bonus must be large enough to make the reward salient. We also present examples of tasks for which PBPs do not improve quality. Our results suggest that for PBPs to improve quality, the task must be effort-responsive: the task must allow workers to produce higher quality work by exerting more effort. We also give a simple method to determine if a task is effort-responsive a priori. Furthermore, our experiments suggest that all payments on Mechanical Turk are, to some degree, implicitly performance-based in that workers believe their work may be rejected if their performance is sufficiently poor. Finally, we propose a new model of worker behavior that extends the standard principal-agent model from economics to include a worker's subjective beliefs about his likelihood of being paid, and show that the predictions of this model are in line with our experimental findings. This model may be useful as a foundation for theoretical studies of incentives in crowdsourcing markets.
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