Estimation of the Degree of Entropy of Russia's Foreign Economic Relations in the Choice of an International Industrial and Technological Partnership

  • Abstract
  • Keywords
  • References
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  • Abstract

    The article studies the orderliness of Russia's foreign economic relations in the context of industrial and technological partnership with the other countries. The research is based on a combination of the logical-probabilistic approach of complex networks. It was noted that the issue of choosing the best ways for international production and technology cooperation should be resolved from the point of view of deliberate ordering in the most significant sectors for the planned outstripping development. The most significant industries for the Russian economy are identified: coal, oil and gas, machine building and high-tech sectors of the economy. The indicators of relatively low entropy (orderliness) of foreign economic relations in these significant areas are obtained. That means that there is a high potential for expanding the production and technology partnership. Although it was noted that Russia is not an active participant in industrial and technological integration with many countries in the world. The results of the study confirmed the possibility of using entropy indicators to assess the degree of development of foreign economic relations and determine the optimal directions of industrial and technological partnership.



  • Keywords

    Entropy of foreign economic relations, industrial and technological partnership, information processing theory, complex systems.

  • References

      [1] Sopilko N, Kurashova A, Shatalova I, Bogacheva T & Kutlyeva GM (2017), Modeling the development of trade ties of Russia within the framework of regional integration based on the theory of gravity. Espacios, 38, pp: 625–634.

      [2] Cowell RG, Dawid P, Lauritzen SL & Spiegelhalter DJ (1999), Probabilistic networks and expert systems. Berlin: Springer, 313 p.

      [3] Liu Y, Slotine J & Barabási A (2011), Controllability of complex networks. Nature, 473, pp: 167–173.

      [4] Newman MEJ (2006), Modularity and community structure in networks. Proceedings of the National Academy of Sciences of the United States of America, 103(23), pp: 8577–8696.

      [5] Zhengzhong Y, Chen Zh, Zengru D, Wen-Xu Wang & Ying-Cheng L (2013), Exact controllability of complex networks. Nature Communications, 4, pp: 24–47.

      [6] Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D & Alon U (2002), Network motifs: simple building blocks of complex networks. Science, 298(5594), pp: 824–827.

      [7] Kullback S & Leibler R (1951), On information and sufficiency. The Annals of Mathematical Statistics, 22, pp: 79–86.

      [8] Blondel V, Guillaume J-L & Lambiotte R (2008), Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 10, pp: 1–12,

      [9] Sopilko NYu, Nawrotskaya NA, Kovaleva EA, Orlova AF & Grigoryeva AV (2017), Dynamics factors and slow-response characteristics of Russian trade ties. Journal of Advanced Research in Law and Economics, 8, 2(24), pp: 625–634.

      [10] Broeck G, Mohan К, Choi А, Darwiche А & Pearl J (2015), Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data. UCLA Cognitive Systems Laboratory, Technical Report (R-441-UAI), Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, pp: 161–170.

      [11] Cowell R (2005), Local Propagation in Conditional Gaussian Bayesian Networks. Machine Learninig Research, 6, pp: 1517–1550.

      [12] Amador J & Cabral S (2017), Networks of Value-added Trade. The World Economy, 40(7), pp: 1291–1313.

      [13] Inshakova E, Inshakov O & Orlova A (2017), Global and Russian nanotechnology product market development: Comparison of trends and impact of sanctions. International Journal of Trade and Global Markets, 10 (2-3), pp: 226–235.

      [14] Cesare I, Piergiuseppe M & Renna F (2015), Innovation and Exporting: Does Quality Matter? The International Journal, 29,
      pp: 273–290.

      [15] Fatima S (2017), Globalization and technology adoption: evidence from emerging economies. Journal of International Trade and Economic Development, 26(6), pp: 724–758.

      [16] Jeffs P (2013), High-reactive PUCB binders in theory and practice. Foundry Trade Journal International, 187 (3702), pp: 48–52.

      [17] Temiz D, Gökmen A, Nakip M & Azari NM (2017), The impact of foreign trade issues on economic growth in some developing countries including Iran and Turkey. Journal of Transnational Management, 22(3), pp: 171–202.

      [18] Uprety D (2017), The impact of international trade on emigration in developing countries. Journal of International Trade and Economic Development, 26(8), pp: 907–923.

      [19] Fakher A (2016), New patterns of world trade and foreign direct investment growth in Egypt. International Journal of Trade and Global Markets, 9(1), pp: 18–32.

      [20] Banday U & Ismail S (2017), Does tourism development lead positive or negative impact on economic growth and environment in BRICS countries? A panel data analysis. Economics Bulletin, 37(1), pp: 553–567.

      [21] Yi Q & Bu W (2017), The impact of FDI on international industrial competitiveness. 4th International Conference on Industrial Economics System and Industrial Security Engineering, IEIS 2017, 8078622, Kyoto, Japan.

      [22] Murray P (2018), Australia’s engagement with the European Union: partnership choices and critical friends. Australian Journal of International Affairs, 72(3), pp: 208–223.

      [23] Capaldo J & Izurieta A (2018), Macroeconomic Effects of 21st Century Trade and Investment Agreements: The Case of the Trans-Pacific Partnership. Development and Change, 49(4), pp: 951–977.




Article ID: 24617
DOI: 10.14419/ijet.v7i4.38.24617

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