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From outside to hyper-globalisation: an Artificial Neural Network ordinal classifier applied to measure the extent of globalisation

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Abstract

Globalisation has become a key concept in the social sciences to understand the accelerating changes occurred in modern societies during recent decades. As a consequence, measuring the influence of globalisation on the economic, social and political aspects of nations has been a requirement. There are many indices at present to calculate the extent of globalisation reached by each country. However, most of the methods used to build those indices suffer certain methodological limitations that hinder the wider dissemination and usefulness of their results. As an alternative, in this paper, we propose a methodology for ordinal ranking of countries associated with their globalisation level, which gives us an easier and more useful information about the different levels where countries are regarding to this criteria. Among Computational Intelligence techniques, Artificial Neural Networks (ANNs) have become dominant modelling paradigm. We have built a novel non-linear ordinal classifier by combining the Proportional Odd Models (POM) with ANNs that is able to classify countries according to their level of globalisation in six classes, which range from hyperglobalised countries to countries that remain outside the process of globalisation. The results could not be more encouraging. Our experiments yield robust results and show better outcomes than alternative linear and non-linear ordinal classifiers, which raises the possibility of developing a model of classification that might overcome some of the limitations of the indices currently employed to measure globalisation.

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Notes

  1. See http://globalisation.kof.ethz.ch/papers/ for an extensive list of articles using the index.

  2. It must be noted that, sometimes, rating techniques have been accused to dull the provocative effect of ranking (Roodman 2008). However, after 10 years of development of a dozen indices of globalisation and no one finding real dissemination at social or political levels, it could be the right moment to try a new focus. Classifications techniques can also provide clear and swift information to policy and decision makers, helping countries as well in the search for strategic allies in the international arena.

  3. http://www.uco.es/ayrna.

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Acknowledgments

This work has been partially subsidised by the TIN2011-22794 project of the Spanish Ministry of Economy and Competitiveness (MINECO), FEDER funds and the P2011-TIC-7508 project of the “Junta de Andalucía” (Spain).

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Authors and Affiliations

  1. Department of Computer Science and Numerical Analysis, University of Cordoba, Campus de Rabanales, C2 building, 14071, Cordoba, Spain

    Manuel Dorado-Moreno & César Hervás-Martínez

  2. ETEA Foundation for Development and Cooperation, Loyola University Andalusia, Escritor Castilla Aguayo 4, 14004, Cordoba, Spain

    Antonio Sianes

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  1. Manuel Dorado-Moreno
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  2. Antonio Sianes
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  3. César Hervás-Martínez
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Correspondence to Manuel Dorado-Moreno.

Appendix

Appendix

See Table 7.

Fig. 2
figure 2

Classification of all the countries in the database

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Table 5 Composition of the KOF Index of globalisation, 2012 edition
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Table 6 Countries corresponding to their classes
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Table 7 For 147 countries
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Table 8 Ranking given by the HANNPOM model compared to the theoretical one
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Dorado-Moreno, M., Sianes, A. & Hervás-Martínez, C. From outside to hyper-globalisation: an Artificial Neural Network ordinal classifier applied to measure the extent of globalisation. Qual Quant 50, 549–576 (2016). https://doi.org/10.1007/s11135-015-0163-7

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