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Relation Between Polling and Likert-Scale Approaches to Eliciting Membership Degrees Clarified by Quantum Computing

机译:投票与浪费率的关系,以量子计算阐明了诱导会员度程度的方法

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In fuzzy logic, there are two main approaches to eliciting membership degrees: an approach based on polling experts, and a Likert-scale approach, in which we ask experts to indicate their degree of certainty on a scale - e.g., on a scale form 0 to 10. Both approaches are reasonable, but they often lead to different membership degrees. In this paper, we analyze the relation between these two approaches, and we show that this relation can be made much clearer if we use models from quantum computing.
机译:在模糊逻辑中,引出会员度学位有两种主要方法:一种基于投票专家的方法,以及李克特级方法,我们要求专家在规模上表明他们的确定性 - 例如,在规模形式0到10.两种方法都是合理的,但他们经常导致不同的会员度。在本文中,我们分析了这两种方法之间的关系,我们表明,如果我们使用量子计算的模型,可以更清楚地更清楚。

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