Abstract
The subject matter of this research is the processes of the spontaneous clustering in the regional economy. The purpose is the development and approbation of the modeling algorithm of these processes. The hypothesis: the processes of spontaneous clustering in the social and economic environment are supposed to proceed not linearly, but intermittently. The following methods are applied: agent imitating modeling with an application of FOREL and k-means algorithms. The modeling algorithm is realized in the Python 3 programming language. The course regularities of clustering processes in the region are revealed: 1) the clustering processes are intensifying, the production uniformity is increasing; 2) the increase of the level of production uniformity leads to the leveling of customer behavior; 3) the producers of high-differentiated production reduce the level of its differentiation or leave the cluster; 4) the stages of steady functioning are illustrative for clustering processes, their change is followed with arising of bifurcation points; 5) the activation of clustering processes in regional economy leads to the revenue increase of the cluster participants, each of producers and of consumers, and to the growth of synergetic effect values. These results testify the nonlinearity of processes of clustering and ambiguity of their effects. The following conclusions have been drawn: 1) a modeling of the processes of spontaneous clustering in regional economy has showed that they proceed not linearly, a steady progressive development is followed with leaps; 2) the clustering of regional economy leads to the growth of the efficiency indicators of activities of cluster-concerned entities; 3) initiation and activation of the clustering processes requires a certain environment.
Highlights
Modeling the algorithms for the development of regional clusters is highly relevant for the countries implementing the cluster approach
The analysis shows that the patterns and stages of cluster processes in a regional economy still remain unexplored; the mathematical apparatus for the calculation of the key indicators of the clusters arising from their members’ behaviors sufficiently undeveloped
In the terms of System Theory, regional clusters are understood as a set of interrelated and interacting elements [25]; in the Chaos Theory, they are treated like objects that are heavily dependent on the starting conditions, minor changes in the external environment leading to unpredictable consequences for them [26, 27]
Summary
Modeling the algorithms for the development of regional clusters is highly relevant for the countries implementing the cluster approach. The bibliographic survey reveals that in recent years, some attempts have been made to simulate the basic parameters of clusters [1], processes of their formation [2,3,4,5,6,7,8,9], including the problems of their self-organization [10,11,12], intracluster interactions [13,14,15,16], cluster functioning [17], the systems of intracluster objectives [6], their life cycle [18,19,20,21,22,23], entropy processes, degradation and collapse of cluster structures [7, 21, 24], etc. Shelkov the goal of the study, which is the development and application of the algorithm to clustering processes in regional economic systems
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