System Models for Strategic Spatial Planning and Regional Development
In recent years, the strategic spatial planning of regional development is becoming increasingly important when making critical decisions in management and economics. One of the advantages of scientific direction devoted to solving problems related to strategic management of spatial economic systems is its interdisciplinary nature and ability to take advantage of system analysis and synergetic effect in the study of a range of different fields of knowledge. Another distinguishing feature is the synthesis of conceptual categories and methodologies of public sciences, sociology, humanities and technical sciences. Frequently, such decisions involve using adequate system models and appropriate methodological approaches. Hereby, the role and importance of modeling are increasing, particularly in the creation of interdisciplinary databases and forecasting. It is theoretical and empirical study in equal measure. The scientific methodology of the research is system approach and comparative analysis, dynamic principle, and comprehensive consideration of the processes of spatial strategic planning and hybrid modeling. This paper deals with the issues of system modeling implementation and how system models can be used to support the process of spatial strategic planning (SSP) in the context of theory of regional economic growth and development. The main goal of this paper is analysis the main domains of system models application. The paper focuses on a particular group of models and modeling systems – hybrid intelligent models and systems that allow in conditions of uncertainty, incomplete initial data and complex interdependence between elements of investigated spatial regional economics system to evaluate the implications of realization of various scenarios of strategic spatial development. The original contribution of this work is the classification (typology) of models, used in spatial strategic planning of regions. This typology is determined by the models’ complexity and variety of application areas.