Simulation of the national innovation systems development: A transnational and coevolution approach

Abstract

The current state of scientific and technological development of the world economy is quite specific, because advanced technologies already known are too complicated for simple mechanical copying and borrowing, and most of the technologies of Industry 4.0 are in the making. Thus, the development and further exploitation of all kinds of innovations today, more than ever, require an appropriate environment - an effective national innovation system (NIS), which determines the country's ability to generate innovation, which is the key to high competitiveness and world leadership. However, the formation of a full-fledged innovation system of the country is quite complicated, for at least two reasons: first, there exist purely national features of functioning and cooperation of the main agents of change, and secondly, in the modern globalized world many of the most important for innovation processes go beyond the borders of individual countries, creating a unique transnational "ecosystem" with its distinctive features, which, undoubtedly, must be considered. The article proposes the scientific approach of reliable identification of national and transnational (supranational, global) innovation systems (TNIS) and the corresponding toolkit for simulating their development in the context of the quadruple helix concept. Identification of innovative systems is based on the methods of cluster analysis, genetic algorithms and neural network training. As a result, there have been identified and qualitatively interpreted four basic types of TNIS, which have stable characteristics determining the behavioural parameters and capabilities of the NIS included. A neural network has been built to identify NIS, which simplifies the process of simulating their development within the characteristic features of basic TNIS. It is established that the NIS of Ukraine belongs to the basic type of TNIS – “developed and developing countries with mixed extractive-inclusive institutions with a strong informal component (including the post-Soviet type)”. The results of its functioning against the background of global and relevant cluster leaders are not satisfactory and necessitate the adjustment of the further development vector. In order to demonstrate the capabilities of the neural network built, four supranational associations have been identified and analysed. The proposed approaches and tools will facilitate variant analytics and forecasting studies in substantiating the optimal directions for the individual NIS further development in the context of global and cluster trends.

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