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The Importance of Worker Reputation Information in Microtask-Based Crowd Work Systems

机译：基于微任务的人群工作系统中工人声誉信息的重要性

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摘要

This paper presents the first systematic investigation of the potential performance gains for crowd work systems, deriving from available information at the requester about individual worker reputation. In particular, we first formalize the optimal task assignment problem when workers' reputation estimates are available, as the maximization of a monotone (submodular) function subject to Matroid constraints. Then, being the optimal problem NP-hard, we propose a simple but efficient greedy heuristic task allocation algorithm. We also propose a simple “maximum a-posteriori” decision rule and a decision algorithm based on message passing. Finally, we test and compare different solutions, showing that system performance can greatly benefit from information about workers' reputation. Our main findings are that: i) even largely inaccurate estimates of workers' reputation can be effectively exploited in the task assignment to greatly improve system performance; ii) the performance of the maximum a-posteriori decision rule quickly degrades as worker reputation estimates become inaccurate; iii) when workers' reputation estimates are significantly inaccurate, the best performance can be obtained by combining our proposed task assignment algorithm with the message-passing decision algorithm.
机译：本文提出了对人群工作系统潜在绩效提升的首次系统调查，该调查是根据请求者关于单个工人声誉的可用信息得出的。特别是，当工人的声誉估计可用时，我们首先将最优任务分配问题形式化，因为受Matroid约束的单调（子模）函数的最大化。然后，作为NP问题的最优问题，我们提出了一种简单而有效的贪婪启发式任务分配算法。我们还提出了一个简单的“最大后验”决策规则和基于消息传递的决策算法。最后，我们测试并比较了不同的解决方案，表明系统性能可以极大地受益于有关工人声誉的信息。我们的主要发现是：i）在任务分配中甚至可以有效地利用对工人声誉的不准确估计来极大地提高系统性能； ii）最大的后验决策规则的性能随着工人声誉估计的不准确而迅速下降； iii）当工人的声誉估计非常不准确时，可以通过将我们提出的任务分配算法与消息传递决策算法相结合来获得最佳性能。

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