MODELING OF THE INVESTMENT AND CONSTRUCTION TREND IN RUSSIA

The research results are especially relevant under conditions of the current systemic cyclical crisis caused by the change long waves of economic development and technological structures. Such processes have the ability to generate a new order, not forced by the exogenous force but having a spontaneous character as a result of the endogenous factors transformation. The fluctuations occurring in the socio-economic environment, instead of fading, are amplifying, and the socio-economic system develops in the direction of arbitrary self-organization. Taking into account the fact that complicated self-organized systems cannot be imposed with the way for their development, new approaches are required to trend forecasting and system management taking into account natural patterns and properties revealed in the process of economic and mathematical modeling. This allows shifting the bifurcation diagram at a certain period of time, bypassing the system critical point which leads to an undesirable outcome. Timely and qualitative forecasting of crisis points, administrative effects modeling with the purpose of transition to a new favorable way of the economic system development is reasonable and justifiable. Since there are compelling reasons to regard the investment and construction sphere of Russia as a field of the synergistic patterns action and its evolution cyclical nature revealed, the trend can be set with a certain degree of accuracy by the differential equation systems, which allows identifying existing alternatives of system behavior and obtaining more complete information about the future. The article presents a new approach to the modeling and evolution forecasting of complex cyclic and stochastic economic systems. Based on the qualitative and quantitative analysis of the Russian investment and construction industry statistical data the main order parameters and system control variables are identified. A model is developed, which is based on a second-order differential equation that makes it possible to use statistical data and forecast in the long term the system behavior depending on managerial effects. The dynamics is identified of the control variables impact on system order parameter within different time periods.