Dynamic model of minimax control over economic security state of the region in the presence of risks

Investigation and solution of management of economic security state in the region (MESSR) requires development of a dynamic economic-mathematical model that takes into account the presence of control actions, uncontrolled parameters (risk modeling errors, etc.) and availability of information deficit. At the same time, the existing approaches to solving such problems are based primarily characteristics of the main model parameters and special conditions for the realization of the process. We should note that to use the apparatus of stochastic modeling, very strict conditions are required, which in practice are usually not feasible in advance In this paper, we propose to use a deterministic approach for modeling and solving the original problem in the form of a dynamic programming problem of minimax control (optimization of a guaranteed result) MESSR at the determined point of time, taking into account the availability of risks of deterministic and stochastic nature (combined risks model). At the same time, under the risks in the social and economic system we understand the factors that negatively catastrophically affect the results of the reviewed processes inside it. For an effective use, a technique of prediction and assessment of time rows and stochastic risks in MESSR optimization process is presented, which can serve as a basis for the development of appropriate computer software. To solve the problem of program minimax control MESSR in the presence of risks, we propose a method which is reduced to the realization of a finite number of solutions of linear and convex mathematical programming and discrete optimization problem. The proposed method makes it possible to develop efficient numerical procedures to implement computer simulation of the dynamics of the problem, build program minimax control and gain optimal guaranteed result. The results presented in this paper can be used for economic-mathematical modeling and solving other optimization problems of forecasting processes and data management in a lack of information and the availability of risks, as well as to develop appropriate software and hardware systems to support effective management decisions in practice.