Reconstructing Nonparametric Productivity Networks

Abstract

Network models provide a general representation of inter-connected system dynamics. This ability to connect systems has led to a proliferation of network models for economic productivity analysis, primarily estimated non-parametrically using Data Envelopment Analysis (DEA). While network DEA models can be used to measure system performance, they lack a statistical framework for inference, due in part to the complex structure of network processes. We fill this gap by developing a general framework to infer the network structure in a Bayesian sense, in order to better understand the underlying relationships driving system performance. Our approach draws on recent advances in information science, machine learning and statistical inference from the physics of complex systems to estimate unobserved network linkages. To illustrate, we apply our framework to analyze the production of knowledge, via own and cross-disciplinary research, for a world-country panel of bibliometric data. We find significant interactions between related disciplinary research output, both in terms of quantity and quality. In the context of research productivity, our results on cross-disciplinary linkages could be used to better target research funding across disciplines and institutions. More generally, our framework for inferring the underlying network production technology could be applied to both public and private settings which entail spillovers, including intra- and inter-firm managerial decisions and public agency coordination. This framework also provides a systematic approach to model selection when the underlying network structure is unknown.

Worldwide bilateral geopolitical interactions network inferred from national disciplinary profiles

“…Since the pioneering works, Refs. [19,20], the characteristics of the disciplinary structure at the country level has been investigated in many studies [21][22][23][24][25][26][27][28][29][30]. The analysis of the disciplinary profiles of Eastern Europe contries and Soviet Union and its evolution after the breakup of the Soviet Union has been the subject of several studies [31][32][33][34][35].…”

Entropy analysis and grey correlation coefficient cluster analysis of multiple indexes of 5 kinds of condiments

“…According to the combustion heat, thermo gravimetric analysis parameters, fat, calcium, ash, crude fiber, trace element content data for five condiments, construction of multi-index comprehensive evaluation system for condiments including cinnamon, tangerine peel, clove, licorice, amomum in Guangx, China by entropy method (Aide et al, 2021;Álvarez et al, 2020;Arch-Tirado et al, 2020;Bostian et al, 2020;Chen et al, 2020a;Cho, 2020). a large correlation coefficient in indicator X4, mainly reflecting the ash content of condiments (Zhou, 2017b).…”

Multiple indicators metrological analysis for 5 kinds of tea produced in Yunnan, China

“…Construction of a multi-index analysis and comprehensive evaluation system by entropy analysis According to combustion heat, differential thermogravimetric, fat content, crude fiber content, ash content, and trace element content data for 5 kinds of tea, multi-index comprehensive evaluation systems for 5 kinds of tea, including camphorscented palace tea, fossil glutinous rice tea, old tree white tea, Brown Mountain ancient tree tea, and pure Banzhang tea, were established by the entropy method (Aide et al, 2021;Álvarez et al, 2020;Arch-Tirado et al, 2020;Bostian et al, 2020;Chen et al, 2020;Cho, 2020). (Deng et al, 2020;Galas et al, 2021;Gupta & Chokshi, 2020;Hu et al, 2020) According to the characteristics of entropy, this paper judges the randomness and degree of disorder of an event by calculating the entropy value and judges the degree of dispersion of an index by using the entropy value.…”