Estimating the outcome of an active phase in the military conflict on Eastern Ukraine based on the Sugeno fuzzy integral

Abstract

A military conflict (especially its active phase) is a time of maximum exertion of all the powers of the state and society, a time that requires quick and correct decisions from state bodies. The quality of these decisions is largely determined by the estimation adequacy of the current situation. As the analysis shows, modern military conflicts start suddenly and develop rapidly. The official informing system turns out to be ineffective, what leads to numerous mistakes in decision-making. In addition, modern military conflicts are of a hybrid nature. The outcome of such military conflicts depends on many factors of a non-military nature, for example, the quality of governance, support from the population, international assistance. These factors are often formulated qualitatively (linguistically), and the conditions of the active phase of a military conflict do not give time to check the adequacy of quantitative data. Therefore, it is necessary that the method for estimating the outcome of the active phase takes into account the data uncertainty and ensures a generalization of the partial characteristics of the current situation. Based on the analysis of known approaches to the description and processing of uncertainty, the authors proposed using the methods of fuzzy integral calculus to describe partial characteristics and calculate a generalized characteristic, which is an estimation of the success of the outcome of an active phase. The authors have solved the following subproblems: identification of structure and parameters of standard for estimating; choice of the observation channel of the characteristics of the current situation; constructing the algorithm for estimations generalization. The authors demonstrated the work of the proposed algorithm by the example of estimating the results of hostilities in eastern Ukraine in July 2014.

Keywords

• military conict
• decision-making
• estimation
• algorithm
• fuzzy-integral calculus