This paper proposes a method for generating robust ranks of Delphi projections, which are particularly suitable as input for clustering algorithms. The resulting clusters can be used for the construction of Delphi-based scenarios. The method is very flexible and can be applied to the classification of any variable derived from subjective judgments. In the analysis and interpretation of the results of a Delphi, a series of problems emerge related to the use of the concept of distance. The use of robust ranks allows us to overcome these problems.

The proposed method is also robust with respect to the expertise of panel members, which is a feature that creates many problems both at the moment of measurement and in the subsequent use of those measurements. This opens up important reflections on a crucial aspect of any Delphi study: the dependence of the results on the expertise of the panelists. One of the outputs of the method proposed here is constituted by the uncertainty intervals, which can be used as a monitoring system for the quality of the Delphi projections.

By applying the method to the future of families in the north-east of Italy, we will show its validity, reproducibility, and practicality.

本文提出了一种生成鲁棒性 Delphi 投影等级的方法，该方法特别适合作为聚类算法的输入。生成的集群可用于构建基于 Delphi 的场景。该方法非常灵活，可以应用于任何由主观判断得出的变量的分类。在对Delphi结果的分析和解释中，出现了一系列与距离概念的使用有关的问题。使用强大的排名使我们能够克服这些问题。

所提出的方法在专家组成员的专业知识方面也是稳健的，这是一个在测量时和随后使用这些测量时都会产生许多问题的特征。这开启了对任何 Delphi 研究的一个关键方面的重要反思：结果对小组成员专业知识的依赖。这里提出的方法的输出之一是由不确定区间构成，它可以用作德尔菲投影质量的监测系统。

通过将该方法应用于意大利东北部家庭的未来，我们将展示其有效性、可重复性和实用性。