Mathematical basis of stochastic modeling multicomponent risks in security systems
Introduction. It is shown that the development of methods for modeling multicomponent risks is a promising direction for improving information and analytical support for control in security systems. The purpose of the study is to develop new approaches to the study of natural, technogenic and anthropogenic risks based on stochastic modeling of the structure of multicomponent risks in socio-technical systems. Methods of stochastic modeling are based on a matrix representation of risk components, detailing the states of the protected object and the probabilistic characteristics of the functioning of security systems. Results and discussion. A method for analyzing multicomponent risks is presented, reflecting in-depth detailing of the states of the protected object and the probabilistic characteristics of the functioning of security systems. A stochastic model has been built that describes the structure of risk as a result of the interaction of two components, a multiplier and an accelerator, associated with various elements of the model, which, respectively, determine the possibility of occurrence of dangerous events, as well as the degree of vulnerability of protected objects. A connection is established between the indicators of expected losses in a certain territory with the presence of forces, means and systems of protection against the effects of hazardous factors and their current state. The procedures for determining the main parameters of the proposed stochastic model based on statistical and expert methods are discussed. A mathematical toolkit has been created for comparative analysis of the effectiveness of measures to reduce risks in socio-technical systems. The problem of multicriteria combinatorial optimization of planned costs and distribution of financial, material, technical and labor resources in territorial security systems is formulated. Conclusions. Methods for modeling multicomponent risks can be used to create effective algorithms for supporting risk-oriented management in security systems.