Scientific Journal of KubSAU

The article describes the synthesis and verification of statistical and system-cognitive models of the influence of environmental factors on the quality of life of the population of the region. This stage of the ASC-analysis is performed in the system called "Eidos". As a result, we have created and validated (verification stage) all the specified systemic cognitive models. It is expected that reliability for the models of knowledge is sufficiently high for a given subject area, that is why we can state the discovery of a dependence of life expectancy and causes of death from environmental conditions. Typically, knowledge models are approximately 20% higher in accuracy than statistical models, which operate on the principle of positive pseudo-prediction. Making decisions based on the model of Abs (matrix of absolute frequencies) is not appropriate because of the different number of instances of classes (generalized categories) and dependence of the solutions of this amount. In the model called Prc2 (conditional and unconditional percentage distribution) the dependence of the model values of the number of examples in classes has been removed, but the accuracy of it is usually same low as in the Abs. In addition, for decision-making based on this model, one has to compare the values of conditional and unconditional probabilities manually, which is laborious and hardly possible for large dimensional models. The knowledge model called Inf3, based on a measure similar to the Chi-square, is the result of the automated comparison of values of conditional and unconditional probabilities presented in the model of Prc1, which is similar to Prc2, and usually has a fairly high accuracy, especially considering the high complexity of the subject area, which we simulated. Therefore, in accordance with the technology of the ASC-analysis data conversion into information, and afterwards - into knowledge, it is the model of Inf3 which is planned to be used for the solution of problems of identification, forecasting, decision-making and exploring the modeled subject area, through the study of its models

This work is devoted to revealing some opportunities of the research of a subject to modeling by a research of its model given by the ASC-analysis and the Eidos system

In their previous works, the authors solved the problem of cognitive-target structuring and formalization of the subject area, as well as the synthesis and verification of statistical and system-cognitive models. This work is devoted to the application of these models to solve the problem of forecasting the impact of the volume and direction of investment in agriculture on the economic component of the quality of life of the population of the region. This work is devoted to the use of previously created and verified models to solve the problem of decision-making on the volume and direction of investment in agriculture, improving the quality of life of the population of the region

The article considers an approach to managing the production process in agriculture based on modeling and evaluation of added value chains. The work has proposed a scheme of links for the production chain of added value creation that contains source control and financing cash flow which comes first in the link of agricultural production, then produced products are supplied to the accumulator, and from there it sequentially passes through the links of the value chain of the cyclic processing facilities, from which finished products are marketed and the resulting revenue is directed to the source of funding and management. We have given mathematical descriptions of the movement of financial and material flows in the links of the developed value chain, and mathematical models for calculating the volume of material and financial flows are proposed. Financial flows were also investigated to compensate for the cost of converting material flows and their mathematical descriptions. The article obtains a mathematical model of the economic efficiency of the production process and proposes a mathematical model for calculating the minimum price for socially significant processing products