In recent times, composite indicators have gained astounding popularity in a wide variety of research areas. Their adoption by global institutions has further captured the attention of the media and policymakers around the globe, and their number of applications has surged ever since. This increase in their popularity has solicited a plethora of methodological contributions in response to the substantial criticism surrounding their underlying framework. In this paper, we put composite indicators under the spotlight, examining the wide variety of methodological approaches in existence. In this way, we offer a more recent outlook on the advances made in this field over the past years. Despite the large sequence of steps required in the construction of composite indicators, we focus particularly on two of them, namely weighting and aggregation. We find that these are where the paramount criticism appears and where a promising future lies. Finally, we review the last step of the robustness analysis that follows their construction, to which less attention has been paid despite its importance. Overall, this study aims to provide both academics and practitioners in the field of composite indices with a synopsis of the choices available alongside their recent advances.

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On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness

Unsupervisedly learned word vectors have proven to provide exceptionally effective features in many NLP tasks. Most common intrinsic evaluations of vector quality measure correlation with similarity judgments. However, these often correlate poorly with how well the learned representations perform as features in downstream evaluation tasks. We present QVEC—a computationally inexpensive intrinsic evaluation measure of the quality of word embeddings based on alignment to a matrix of features extracted from manually crafted lexical resources—that obtains strong correlation with performance of the vectors in a battery of downstream semantic evaluation tasks.1

/pdf/evaluation-of-word-vector-representations-by-subspace-29g27isi5v.pdf

Evaluation of Word Vector Representations by Subspace Alignment

Non-compensatory composite indicators for the evaluation of urban planning policy: The Land-Use Policy Efficiency Index (LUPEI)

Leader-driven community detection algorithms (LdCD hereafter) constitute a new trend in devising algorithms for community detection in large-scale complex networks. The basic idea is to identify some particular nodes in the target network, called leader nodes, around which local communities can be computed. Being based on local computations, they are particularly attractive to handle large-scale networks. In this paper, we describe a framework for implementing LdCD algorithms, called LICOD. We propose also a new way for evaluating performances of community detection algorithms. This consists on transforming data clustering problems into a community detection problems. External criteria for evaluating obtained clusters can then be used for comparing performances of different community detection approaches. Results we obtain show that our approach outperforms top state of the art algorithms for community detection in complex networks.

/pdf/licod-a-leader-driven-algorithm-for-community-detection-in-4k45ezrv92.pdf

LICOD: A Leader-driven algorithm for community detection in complex networks

Accurate algorithms for identifying the median ranking when dealing with weak and partial rankings under the Kemeny axiomatic approach

/pdf/mathematics-without-numbers-an-introduction-to-the-study-of-1sqsjd3myn.pdf

Patricia Rose Gomes de Melo Viol Martins

Papers

Mathematics without numbers: an introduction to the study of logic