2020. No. 4 Vol.14

      This work is devoted to the highly topical problem of structuring costs for contextual and targeted advertising on the Internet. The choice of the ad campaign financing structure is considered from the point of view of violating the principle of symmetry of user interest in ads. The purpose of this work is to develop a methodology for structuring advertising campaign costs based on cluster analysis, taking into account the asymmetry of user interest in advertising. The key feature of the research is the description of the possibility of using the asymmetry of user interest in application solutions, such as online advertising. The Gini coefficient is used as an indicator of the degree of imbalance in the manifestation of a feature in clustering, and the features of using the lift coefficient and the Lorentz curve to evaluate the effectiveness of contextual and targeted advertising for various groups of customers are also considered. Using the Gini index and cluster analysis, you can analyze the possibilities of increasing ad revenue and compare it with the absence of any policy for structuring advertising costs. Identifying such patterns in consumer groups allows you to identify the main directions of product development and customer interest in it. The method described here should be used to improve the effectiveness of banner advertising and clustering algorithms. This approach does not improve banner clickability, but allows you to implement an individual approach to advertising products with the current number of clicks and more effectively structure the cost of various types of advertising.

      This article describes the analysis of the quality of microservice architectures, which are one of the main approaches to the creation and maintenance of modern information systems capable of quickly respond to changes in business demands. The implementation of continuous delivery of software components for dynamic business processes of information systems can be carried out by various sets of microservices, the optimal choice of which is a complex multi-alternative task. The paper presents a review of existing approaches to solving the problem, which showed that the development of models for assessing the quality of microservices of information systems requires further elaboration in terms of accounting for uncertainty in the initial data and modes of operation. The authors have proposed an approach to solving the problem of analyzing the quality of a microservice architecture which is implemented on the basis of a fuzzy production network model. The model allows for comprehensive accounting of various parameters (qualitative and quantitative).The article shows the implementation process of the fuzzy production network that was developed to analyze the functional quality of the microservice architecture for processing customer orders using fuzzy modeling software.The results of the analysis will allow managers and system architects to make an informed choice of the microservice architecture of the information system, as well as use it in their reports when arguing the need for scaling the system and increasing the availability of microservices.

      Scientific research of any socio-economic and managerial process can be represented as a chain of reflections on the causes and consequences of this or that phenomenon's occurrence. At the same time, the authors can try not only to answer the question “why?” but also to study and understand the nature of cause-and-effect relationships, to find out the mechanisms of their occurrence, and also to get the answer to the question posed as accurately and reasonably as possible. Each author, using the accumulated experience, offers both qualitative and quantitative methods that allow him to obtain one or another assessment of causality. However, there are not enough articles devoted to a comprehensive review of the methods and technologies of cause-and-effect relationships in socio-economic processes. This article discusses three well-known conceptual approaches to the assessment of causation in socio-economic sciences: successionist causation, configurational causation and generative causation. The author gives his own interpretation of these approaches, builds graphic interpretations, and also offers such concepts as a linear sequence of factors, the causal field, and the causal space of factors in socio-economic processes. Within the framework of these approaches, a classification of mathematical and instrumental models for assessing the causality of relationships in socio-economic processes is given, and trends in the development of these and new models are formulated, taking into account the global transition to a digital format. All of these trends are based on the use of digital technologies in different formats and include descriptions of such formats. The article contains specific author’s examples of causality model implementation in scientific research related to economics and management.

      Text analysis with machine learning support can be implemented for studying experts’ relations to the Bank of Russia. To reach macroeconomic goals, the communication policy of the bank must be predictable and trustworthy. Surveys addressing this theme are still insufficient compare to the theoretical studies on the subject of other bank tools. The goal of this research is to analyze the perception of uncertainty by economic agents. For that purpose, we built an uncertainty indicator based on news sources from the Internet and on textual analysis. The dynamics of the indicator reflect unexpected statements of the Bank of Russia and events affecting monetary policy. Financial theory links monetary policy and stock prices, so we used this fact to examine the impact of the uncertainty indicator on the MOEX and RTS indices.We tested the hypothesis that our indicator is significant in GARCH models for chosen financial series. We found out several specifications in which our indicator is significant. Among the specifications considered, the uncertainty indicator contributes the most to explaining variances of the RTS index. The obtained uncertainty indicator can be used for forecasting of different macroeconomic variables.

      Transport and communication infrastructure plays an important role in ensuring economic growth, also in the context of the Wuhan coronavirus (SARS-CoV-2) spread worldwide. The role of the communication component increases with the epidemic and the associated restrictive measures, which replace, to a certain extent, the transport component. We offer an econometric study of the macroeconomic production function in the Russian Federation with transport and communication infrastructure (the fixed assets average annual value of the Russian transport and communications sectors) for 1990–2018. The arguments for this function are the average annual value of fixed assets in constant 1990 prices, the average annual rate of the use of production capacities in Russian industry, the average annual number of people employed in the national economy, the average annual value of fixed assets of transport and communications in constant 1990 prices. Our research demonstrates that in 2010–2018 the GDP elasticity to production infrastructure was decreasing. We explain this by the reduction in the volume of capital investments in the infrastructure sector’s fixed assets. In addition, we offer an analytical modification of the macroeconomic production function for 2020 in the context of the spread of the Wuhan coronavirus among the Russian population by introducing into this function the average annual rates of labor and infrastructure capacity use, which, along with the average annual rate of fixed assets capacity use are functions of the predicted values of the daily number of the infected Russian citizens. These predicted values are calculated by the time dependent Gaussian quadratic exponent estimated by the least squares. We present the accuracy of the forecast results for the 2020 spring trends of the daily number of Russian and Moscow population infected with the Wuhan coronavirus. The average APE forecast error for 30 days ahead for Russia is 10.4% and the same for five weeks for Moscow is 10%. Moreover, we make forecasts of the officially published daily number of infected Russian population for fall 2020 – spring 2021.