Global world (dis-)order? Analyzing the dynamic evolution of the micro-structure of multipolarism by means of an unsupervised neural network approach

Christopher Erspamer, Francesca Della Torre, Giulia Massini, Guido Ferilli, Pier Luigi Sacco and Paolo Massimo Buscema

Technological Forecasting and Social Change, 2022, vol. 175, issue C

Abstract: We make use of an advanced artificial neural network (Auto-CM) to model the structure of the current world order as a data-driven reconstruction of the implicit relationships between countries and of their time evolution, as derived from a database of publicly observable socioeconomic and political variables. Building on previous research, we analyze 93 variables derived from dozens of key indicators for 128 countries and trace their evolution along a period of eight years, 2007–2014. We find evidence of an increasing structural instability that seems to signal a transition toward a new, as yet undetermined, multipolar world order.

Keywords: Auto contractive map; Artificial adaptive systems; Global world order; Socioeconomic indicators; Multipolarism (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0040162521007824
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:175:y:2022:i:c:s0040162521007824

DOI: 10.1016/j.techfore.2021.121351

Access Statistics for this article

Technological Forecasting and Social Change is currently edited by Fred Phillips

More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Catherine Liu ().