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The article discusses the problem of the influence of the research goals on the structure of the multivariate model of regression analysis (in particular, on the implementation of the procedure for reducing the dimension of the model). It is shown how bringing the specification of the multiple regression model in line with the research objectives affects the choice of modeling methods. Two schemes for constructing a model are compared: the first does not allow taking into account the typology of primary predictors and the nature of their influence on the performance characteristics, the second scheme implies a stage of preliminary division of the initial predictors into groups, in accordance with the objectives of the study. Using the example of solving the problem of analyzing the causes of burnout of creative workers, the importance of the stage of qualitative analysis and systematization of a priori selected factors is shown, which is implemented not by computing means, but by attracting the knowledge and experience of specialists in the studied subject area. The presented example of the implementation of the approach to determining the specification of the regression model combines formalized mathematical and statistical procedures and the preceding stage of the classification of primary factors. The presence of this stage makes it possible to explain the scheme of managing (corrective) actions (softening the leadership style and increasing approval lead to a decrease in the manifestations of anxiety and stress, which, in turn, reduces the severity of the emotional exhaustion of the team members). Preclassification also allows avoiding the combination in one main component of controlled and uncontrolled, regulatory and controlled feature factors, which could worsen the interpretability of the synthesized predictors. On the example of a specific problem, it is shown that the selection of factors-regressors is a process that requires an individual solution. In the case under consideration, the following were consistently used: systematization of features, correlation analysis, principal component analysis, regression analysis. The first three methods made it possible to significantly reduce the dimension of the problem, which did not affect the achievement of the goal for which this task was posed: significant measures of controlling influence on the team were shown. allowing to reduce the degree of emotional burnout of its participants.

Technological development determines the emergence of highly detailed data in time and space, which expands the possibilities of analysis, allowing us to consider consumer decisions and the competitive behavior of enterprises in all their diversity, taking into account the context of the territory and the characteristics of time periods. Despite the promise of such studies, they are currently limited in the scientific literature. This is due to the range of problems, the solution of which is considered in this paper. The article draws attention to the complexity of the analysis of depersonalized high-frequency data and the possibility of modeling consumption changes in time and space based on them. The features of the new type of data are considered on the example of real depersonalized data received from the fiscal data operator “First OFD” (JSC “Energy Systems and Communications”). It is shown that along with the spectrum of problems inherent in high-frequency data, there are disadvantages associated with the process of generating data on the side of the sellers, which requires a wider use of data mining tools. A series of statistical tests were carried out on the data under consideration, including a Unit-Root Test, test for unobserved individual effects, test for serial correlation and for cross-sectional dependence in panels, etc. The presence of spatial autocorrelation of the data was tested using modified tests of Lagrange multipliers. The tests carried out showed the presence of a consistent correlation and spatial dependence of the data, which determine the expediency of applying the methods of panel and spatial analysis in relation to high-frequency data accumulated by fiscal operators. The constructed models made it possible to substantiate the spatial relationship of sales growth and its dependence on the day of the week. The limitation for increasing the predictive ability of the constructed models and their subsequent complication, due to the inclusion of explanatory factors, was the lack of open access statistics grouped in the required detail in time and space, which determines the relevance of the formation of high-frequency geographically structured data bases.

High frequency algorithmic trading became is a subclass of trading which is focused on gaining basis-point like profitability on sub-second time frames. Such trading strategies do not depend on most of the factors eligible for the longer-term trading and require specific approach. There were many attempts to utilize machine learning techniques to both high and low frequency trading. However, it is still having limited application in the real world trading due to high exposure to overfitting, requirements for rapid adaptation to new market regimes and overall instability of the results. We conducted a comprehensive research on combination of known quantitative theory and reinforcement learning methods in order derive more effective and robust approach at construction of automated trading system in an attempt to create a support for a known algorithmic trading techniques. Using classical price behavior theories as well as modern application cases in sub-millisecond trading, we utilized the Reinforcement Learning models in order to improve quality of the algorithms. As a result, we derived a robust model which utilize Deep Reinforcement learning in order to optimise static market making trading algorithms’ parameters capable of online learning on live data. More specifically, we explored the system in the derivatives cryptocurrency market which mostly not dependent on external factors in short terms. Our research was implemented in high-frequency environment and the final models showed capability to operate within accepted high-frequency trading time-frames. We compared various combinations of Deep Reinforcement Learning approaches and the classic algorithms and evaluated robustness and effectiveness of improvements for each combination.

Often decisions in social groups are made by consensus. This applies, for example, to the examination in the technical committee for standardization (TC) before the approval of the national standard by Rosstandart. The standard is approved if and only if the secured consensus in the TC. The same approach to standards development was adopted in almost all countries and at the regional and international level. Previously published works of authors dedicated to the construction of a mathematical model of time to reach consensus in technical committees for standardization in terms of variation in the number of TC members and their level of authoritarianism. The present study is a continuation of these works for the case of the formation of coalitions that are often formed during the consideration of the draft standard to the TC. In the article the mathematical model is constructed to ensure consensus on the work of technical standardization committees in terms of coalitions. In the framework of the model it is shown that in the presence of coalitions consensus is not achievable. However, the coalition, as a rule, are overcome during the negotiation process, otherwise the number of the adopted standards would be extremely small. This paper analyzes the factors that influence the bridging coalitions: the value of the assignment and an index of the effect of the coalition. On the basis of statistical modelling of regular Markov chains is investigated their effects on the time to ensure consensus in the technical Committee. It is proved that the time to reach consensus significantly depends on the value of unilateral concessions coalition and weakly depends on the size of coalitions. Built regression model of dependence of the average number of approvals from the value of the assignment. It was revealed that even a small concession leads to the onset of consensus, increasing the size of the assignment results (with other factors being equal) to a sharp decline in time before the consensus. It is shown that the assignment of a larger coalition against small coalitions takes on average more time before consensus. The result has practical value for all organizational structures, where the emergence of coalitions entails the inability of decision-making in the framework of consensus and requires the consideration of various methods for reaching a consensus decision.

When making decisions in various fields of human activity, it is often required to create text documents. Traditionally, the study of texts is engaged in linguistics, which in a broad sense can be understood as a part of semiotics — the science of signs and sign systems, while semiotic objects are of different types. The method of rank distributions is widely used for the quantitative study of sign systems. Rank distribution is a set of item names sorted in descending order by frequency of occurrence. For frequency-rank distributions, researchers often use the term «power-law distributions».

In this paper, the rank distribution method is used to analyze the Criminal Code of various countries. The general idea of the approach to solving this problem is to consider the code as a text document, in which the sign is the measure of punishment for certain crimes. The document is presented as a list of occurrences of a specific word (character) and its derivatives (word forms). The combination of all these signs characters forms a punishment dictionary, for which the occurrence frequency of each punishment in the code text is calculated. This allows us to transform the constructed dictionary into a frequency dictionary of punishments and conduct its further research using the V. P. Maslov approach, proposed to analyze the linguistics problems. This approach introduces the concept of the virtual frequency of crime occurrence, which is an assessment measure of the real harm to society and the consequences of the crime committed in various spheres of human life. On this path, the paper proposes a parametrization of the rank distribution to analyze the punishment dictionary of the Special Part of the Criminal Code of the Russian Federation concerning punishments for economic crimes. Various versions of the code are considered, and the constructed model was shown to reflect objectively undertaken over time by legislators its changes for the better. For the Criminal Codes in force in the Federal Republic of Germany and the People’s Republic of China, the texts including similar offenses and analogous to the Russian special section of the Special Part were studied. The rank distributions obtained in the article for the corresponding frequency dictionaries of codes coincide with those obtained by V. P. Maslov’s law, which essentially clarifies Zipf’s law. This allows us to conclude both the good text organization and the adequacy of the selected punishments for crimes.

Efficiency and completeness of the numerical simulation in oceanography and hydrometeorology are entirely determined by algorithmic features of the construction of an interactive computer simulations in the scale of the oceans with adaptive coated closed seas and coastal waters refined mathematical models, with the possibility of specifying software parallelization calculations near the concrete — the protected areas of the sea coast. An important component of the research is continuous graphical visualization techniques in the course of calculations, including those undertaken in parallel processes with shared RAM or test points on the external media. The results of computational experiments are used in the description of hydrodynamic processes near the coast, which is important in keeping the organization of sea control services and forecasting marine hazards.

The technique of ensuring of interoperability for Grid-systems and systems of cloud computing is provided. The technique is constructed on a basis of the uniform approach of ensuring interoperability for systems of the wide class offered by authors and recorded in the national Russian Federation standard.

In the design of a distributed library system, there are a lot of organizational problems, ideas, solutions which are the subject of this article. The article presents the approaches on crowd funding platforms that are used as a tool for attracting participants and financing project.

The paper discusses the design and verification stages in the development of complex numerical algorithms to create direct computational experiments in fluid mechanics. The modeling of physical fields and nonstationary processes of continuum mechanics, it is desirable to rely on strict rules of construction the numerical objects and related computational algorithms. Synthesis of adaptive the numerical objects and effective arithmetic- logic operations can serve to optimize the whole computing tasks, provided strict following and compliance with the original of the laws of fluid mechanics. The possibility of using ternary logic enables to resolve some contradictions of functional and declarative programming in the implementation of purely applied problems of mechanics. Similar design decisions lead to new numerical schemes tensor mathematics to help optimize effectiveness and validate correctness the simulation results. The most important consequence is the possibility of using interactive graphical techniques for the visualization of intermediate results of modeling, as well as managed to influence the course of computing experiment under the supervision of engineers aerohydrodynamics– researchers.