Crowd-sourcing deals with solving problems by assigning them to a large number of non-experts called crowd using their spare time. In these systems, the final answer to the question is determined by summing up the votes obtained from the community. The popularity of using these systems has increased by facilitation of access to community members through mobile phones and the Internet. One of the issues raised in crowd-sourcing is how to choose people and how to collect answers. Usually, the separation of users is done based on their performance in a pre-test. Designing the pre-test for performance calculation is challenging; The pre-test questions should be chosen in a way that they test the characteristics in people related to the main questions. One of the ways to increase the accuracy of crowd-sourcing systems is to pay attention to people's cognitive characteristics and decision-making model to form a crowd and improve the estimation of the accuracy of their answers to questions. People can estimate the correctness of their responses while making a decision. The accuracy of this estimate is determined by a quantity called metacognition ability. Metacoginition is referred to the case where the confidence level is considered along with the answer to increase the accuracy of the solution. In this paper, by both mathematical and experimental analysis, we would answer the following question: Is it possible to improve the performance of the crowd-sourcing system by knowing the metacognition of individuals and recording and using the users' confidence in their answers?
翻译:众包通过向社区成员提供方便,通过移动电话和互联网向社区成员提供方便,使这些系统的普及程度有所提高。众包中提出的问题之一是如何选择人和如何收集答案。通常,用户的分离是根据他们在测试前的性能来决定的。设计业绩计算测试前测试的数量是具有挑战性的;测试前的问题应当以他们测试与主要问题有关的人的特点的方式加以选择。提高众包系统准确性的方法之一是关注人们的认知特征和决策模式,形成人群,并改进对问题答案准确性的估计。人们可以评估其答复的正确性,同时做出决定。这一估计的准确性取决于数量,即所谓的元化认知能力。代谢性指的是信任度的答案与主要问题有关的人们特征的测试。提高众包系统的准确性的方法之一是关注人们的认知特征和决策模式,形成人群,并改进对问题回答的准确性能。通过理解性能分析,我们可以通过数学系统来提高用户的准确性能。