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摘要翻译：
决策中使用的综合发展指标往往主观地将一套受限制的指标集合起来。利用主成分分析(PCA)和信息过滤及层次聚类等降维技术，我们发现这些综合指标遗漏了不同指标之间关系的关键信息。具体而言，在全球和地方一级的数据中没有反映出按专题对指标进行分组的情况。我们克服了这些问题，利用指标的聚类，建立了一套新的集群驱动的综合发展指标，这些指标是客观的、数据驱动的、国家之间的可比性和保持解释性的。我们讨论了它们在向政策制定者通报国家发展方面的后果，并将它们与PageRank的顶级指标作为基准进行比较。最后，我们演示了我们新的组合开发指标集在数据集重构任务上的性能优于基准测试。
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英文标题：
《A new set of cluster driven composite development indicators》
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作者：
Anshul Verma, Orazio Angelini, Tiziana Di Matteo
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最新提交年份：
2020
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分类信息：
一级分类：Economics 经济学
二级分类：General Economics 一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Physics 物理学
二级分类：Physics and Society 物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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一级分类：Quantitative Finance 数量金融学
二级分类：Economics 经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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英文摘要：
Composite development indicators used in policy making often subjectively aggregate a restricted set of indicators. We show, using dimensionality reduction techniques, including Principal Component Analysis (PCA) and for the first time information filtering and hierarchical clustering, that these composite indicators miss key information on the relationship between different indicators. In particular, the grouping of indicators via topics is not reflected in the data at a global and local level. We overcome these issues by using the clustering of indicators to build a new set of cluster driven composite development indicators that are objective, data driven, comparable between countries, and retain interpretabilty. We discuss their consequences on informing policy makers about country development, comparing them with the top PageRank indicators as a benchmark. Finally, we demonstrate that our new set of composite development indicators outperforms the benchmark on a dataset reconstruction task.
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PDF链接：
https://arxiv.org/pdf/1911.11226
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