What kind of ‘world order’? An artificial neural networks approach to intensive data mining
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DOI: 10.1016/j.techfore.2017.01.010
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Cited by:
- Lee, Changyong & Kwon, Ohjin & Kim, Myeongjung & Kwon, Daeil, 2018. "Early identification of emerging technologies: A machine learning approach using multiple patent indicators," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 291-303.
- Erspamer, Christopher & Della Torre, Francesca & Massini, Giulia & Ferilli, Guido & Sacco, Pier Luigi & Buscema, Paolo Massimo, 2022. "Global world (dis-)order? Analyzing the dynamic evolution of the micro-structure of multipolarism by means of an unsupervised neural network approach," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
- Pier Luigi Sacco & Alex Arenas & Manlio De Domenico, 2022. "The resilience of the multirelational structure of geopolitical treaties is critically linked to past colonial world order and offshore fiscal havens," Papers 2203.00618, arXiv.org.
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Keywords
World order; Global alliances; Conflict; Open society; Artificial Neural Networks (ANNs); Activation & Competition System (ACS);All these keywords.
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