Improving the decision-making efficiency in defense planning based on the capabilities of military forces and means for performing purposeful tasks requires new methodological approaches and their implementation in the form of software information-analytical tools. Given the complex information environment of defense planning, it is appropriate for the variants of capabilities development options to be chosen by experts on the methodological basis of multicriterial analysis. The research result is the development of a procedure, in which it is proposed to generate criteria and evaluate alternative options by integrating ontology, the Analytical Hierarchy Process, and the method of directed graphs. The ontological representation of the data ensures the construction of the hierarchical taxonomy of a domain and the formation of the criteria vector. The Analytical Hierarchy Process is used to conduct an expert evaluation of capabilities by their pairwise comparison against determined criteria. Experts' judgments are visualized and controlled using directed graphs. Application of the procedure will make it possible to ensure efficiency, versatility, and simplicity of technical implementation of a procedure of decision making support. The procedure was tested on the example of choosing a capability to conduct reconnaissance for the benefit of ground artillery. It was shown that the evaluation process in the expert activity is considerably simplified due to the graph visualization. The proposed procedure introduces an innovative tool to achieve strategic goals and accomplish the basic tasks of the defense reform, which is relevant for many countries. The versatility of the procedure creates the basis for its application not only in defense but also in other force departments

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The problem of modeling and forecasting possible financial loss in the form of market risk using stochastic measurements is considered. The sequence of operations directed towards risk estimation includes data preparing to model building with selected filters: exponential smoothing, optimal Kalman filter and probabilistic Bayesian filter. A short review of the possibilities for data filtering is proposed, and then some of them are selected for specific practical application to process financial data in the form of prices for selected stock instrument. After preprocessing the data is used for constructing forecasting models for the financial process itself and dynamic of its conditional variance. In the first case regression models with polynomial trend are hired, and to describe dynamic of conditional variance GARCH and EGARCH models are constructed. Further on the results of variance prediction are used for computing possible market loss hiring VaR approach. Adequacy analysis of the models constructed and back testing of risk estimates performed indicate that there is improvement of quality of the final results.

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