Quality control in crowdsourcing is challenging due to the heterogeneous nature of the workers. The state-of-the-art solutions attempt to address the issue from the technical perspective, which may be costly because they function as an additional procedure in crowdsourcing. In this paper, an economics based idea is adopted to embed quality control into the crowdsourcing process, where the requestor can take advantage of the market power to stimulate the workers for submitting high-quality jobs. Specifically, we employ two sequential games to model the interactions between the requestor and the workers, with one considering binary strategies while the other taking continuous strategies. Accordingly, two incentive algorithms for improving the job quality are proposed to tackle the sequential crowdsourcing dilemma problem. Both algorithms are based on a sequential zero-determinant (ZD) strategy modified from the classical ZD strategy. Such a revision not only provides a theoretical basis for designing our incentive algorithms, but also enlarges the application space of the classical ZD strategy itself. Our incentive algorithms have the following desired features: 1) they do not depend on any specific crowdsourcing scenario; 2) they leverage economics theory to train the workers to behave nicely for better job quality instead of filtering out the unprofessional workers; 3) no extra costs are incurred in a long run of crowdsourcing; and 4) fairness is realized as even the requestor (the ZD player), who dominates the game, cannot increase her utility by arbitrarily penalizing any innocent worker.

中文翻译：

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使用顺序零决定性策略的众包质量控制

由于工人的异质性，众包的质量控制具有挑战性。最先进的解决方案试图从技术角度解决这个问题，这可能是昂贵的，因为它们是众包中的附加程序。在本文中，采用基于经济学的思想将质量控制嵌入到众包过程中，请求者可以利用市场力量来激励工人提交高质量的工作。具体来说，我们采用两个顺序游戏来模拟请求者和工作人员之间的交互，一个考虑二元策略，另一个采用连续策略。因此，提出了两种提高工作质量的激励算法来解决顺序众包困境问题。这两种算法都基于从经典 ZD 策略修改而来的顺序零行列式 (ZD) 策略。这样的修改不仅为我们的激励算法设计提供了理论依据，也扩大了经典ZD策略本身的应用空间。我们的激励算法具有以下理想特性：1）它们不依赖于任何特定的众包场景；2）他们利用经济学理论训练工人表现得更好，以提高工作质量，而不是过滤掉不专业的工人；3）长期众包不会产生额外费用；4) 实现了公平，因为即使是主导游戏的请求者（ZD 玩家）也无法通过任意惩罚任何无辜的工人来增加其效用。