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On Cost-Effective Incentive Mechanisms in Microtask Crowdsourcing

机译：微任务众包中的成本有效激励机制

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摘要

While microtask crowdsourcing provides a new way to solve large volumes of small tasks at a much lower price compared with traditional inhouse solutions, it suffers from quality problems due to the lack of incentives. On the other hand, providing incentives for microtask crowdsourcing is challenging since verifying the quality of submitted solutions is so expensive that it will negate the advantage of microtask crowdsourcing. We study cost-effective incentive mechanisms for microtask crowdsourcing in this paper. In particular, we consider a model with strategic workers, where the primary objective of a worker is to maximize his own utility. Based on this model, we first analyze two basic mechanisms and show their limitations in collecting high-quality solutions with low cost. Then, we propose a cost-effective mechanism that employs quality-aware worker training as a tool to stimulate workers to provide high-quality solutions. We prove theoretically that the proposed mechanism can be designed to obtain high-quality solutions from workers and ensure the budget constraint of the requester at the same time. Beyond its theoretical guarantees, we further demonstrate the effectiveness of our proposed mechanisms through a set of behavioral experiments.
机译：尽管微任务众包提供了一种以比传统内部解决方案低得多的价格解决大量小任务的新方法，但由于缺乏激励机制，它存在质量问题。另一方面，为微任务众包提供激励是一项挑战，因为验证提交的解决方案的质量是如此昂贵，以至于它会否定微任务众包的优势。本文研究了微任务众包的具有成本效益的激励机制。特别是，我们考虑了具有战略性工作人员的模型，其中工作人员的主要目标是最大化自己的效用。在此模型的基础上，我们首先分析两种基本机制，并说明它们在以低成本收集高质量解决方案方面的局限性。然后，我们提出了一种具有成本效益的机制，该机制采用对质量有意识的工人进行培训，以此作为刺激工人提供高质量解决方案的工具。我们从理论上证明了该机制可以被设计为从工人那里获得高质量的解决方案，并同时确保请求者的预算约束。除了其理论上的保证，我们还将通过一系列行为实验来证明我们提出的机制的有效性。

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