2019. No. 3 Vol.13

      The paper is devoted to fuzzy cognitive modeling, which is an effective tool for studying semi-structured socio-economic systems. The emphasis is onthe process of developing (identification) fuzzy cognitive models, which are the most complex and critical stage of cognitive modeling.
      Existing identification methods are classified as either expert or statistical, depending on the source of information used.
      Typically, when constructing fuzzy cognitive models of semi-structured systems, the system under consideration possesses both quantitative (measurable) factors and factors of a relative, qualitative nature. While statistical data on the quantitative factors may be available, the only available source of information on the qualitative factors is expert knowledge. However, each of the existing identification approaches focuses on just one source type, either expert or statistical.
      Thus, it is crucial to develop a more general approach to the development of fuzzy cognitive models for semi-structured systems to ensure reliable and consistent results by coordinated processing of information of both expert and statistical origins. We developed such an approach based on several identification methods with the subsequent coordination of intermediate results.
      To demonstrate the proposed approach, we applied it to a management problem of integrated development of rural areas. The fuzzy cognitive model we obtained can be used to predict the state of rural areas depending on initial trends and managerial actions, as well as to search and analyze effective managerial strategies for their development.

      This article deals with the formulation of a decision support system (DSS) in the field of regional development management. The review of existing approaches in this area presented here attests, on the one hand, to their diversity, and on the other hand allows us to draw conclusions about the need to address several methodological and practical issues of decision support in terms of innovative development of regions.
      Based on this, the goal of the research was to develop the concept of DSS to justify the parameters of an innovative development strategy for regional development based on adaptive mechanisms for coordinating the interests of economic agents.
      The methodology of the study is based on the synthesis of various approaches in the framework of integration into the structure of adaptive simulation models of problem-oriented knowledge bases with the mechanism of logical inference, as well as intelligent technologies for processing semi-structured information used to find solutions in the process of shaping and adjusting the parameters for managing innovative development of a region.
      The result of the study is a theoretical justification for developing problem-oriented DSS, including a description of the interrelated stages that determine the main design features of this tool. In the framework of the study, a conceptual scheme for implementing DSS in the field of managing innovative development of regions is proposed, and the key functional blocks of the proposed tools are described. In addition, the place of existing tools in the structure of the regional development management system is determined, and we show the possibilities of their use in the formation of forecast-planned assessments of the development of the region, as well as in the evaluation of the effectiveness of alternative management actions.
      The proposed tools will expand the possibilities of applying the methods of management theory and decision support, intelligent information technology, economic and mathematical methods and modern computer simulation technologies for strategic planning of socio-economic systems of macro- and meso-level. In practice, the tools may be of interest to public authorities in solving problems in in the formulation of innovative regional development strategies for Russian regions, the formation of medium-term forecasts and the justification of the parameters of social, economic and budgetary policy.

      Stress testing as an instrument of risk evaluation is actively used in many international organizations, as well as by central banks in many countries. Some organizations (including the Bank of Russia) when conducting stress testing do not publish results of the tests, though they are interesting for the business community. They are reticent so to avoid causing panic on markets which could lead to a massive outflow of deposits from the banking sector as a whole or from some individual banks in particular. As a rule, stress testing is conducted relying on huge number of unpublished reporting forms, but the business community has no access to them. Only four reporting forms are presented on the Bank of Russia’s website. In this paper we propose a simplified algorithm of credit risk stress testing of a banking cluster based on the four officially published reporting forms. The algorithm provides modelling of median values of banking variables depending on macroeconomic indicators, and subsequent retranslation of the received values for assessing the financial position of each bank included in the cluster. It is assumed that growth rates of banking indicators obtained from the econometrics models relying on median values are the same for each bank in the cluster. As of 1 January 2018, credit risk stress testing was conducted for 26 banks, nine of which are system-significant credit institutions. Within the stress testing, eight econometric time series models were developed. As a result, it was discovered that 11 out of 26 banks in the cluster will face certain difficulties regarding statutory requirements related to capital ratios or buffers.

       This paper is devoted to comparison of the capabilities of various methods to predict the bankruptcy of construction industry companies on a one-year horizon. The authors considered the following algorithms: logit and probit models, classification trees, random forests, artificial neural networks. Special attention was paid to the peculiarities of the training machine learning models, the impact of data imbalance on the predictive ability of models, analysis of ways to deal with these imbalances and analysis of the influence of non-financial factors on the predictive ability of models. In their study, the authors used non-financial and financial indicators calculated on the basis of public financial statements of the construction companies for the period from 2011 to 2017. The authors concluded that the models considered show acceptable quality for use in forecasting bankruptcy problems. The Gini or AUC coefficient (area under the ROC curve) was used as the quality markers of the model. It was revealed that neural networks outperform other methods in predictive power, while logistic regression models in combination with discretization follow them closely. It was found that the effective way to deal with the imbalance data depends on the type of model used. However, no significant impact on the imbalance in the training set predictive ability of the model was identified. The significant impact of non-financial indicators on the likelihood of bankruptcy was not confirmed.

      The paper describes a practical approach which can be used by internal IT organizations to gain their business customers’ trust. The variety of customers of the internal IT service provider is limited to internal customers only. The distinguishing feature of the proposed approach is that it is completely practice-oriented, i.e. primarily aimed at building trust among IT service providers and their customers in a particular organization. The approach is based on the idea that there are measurable prerequisites for the emergence of a customer’s trust which allow you to partially formalize the IT organization’s intention to earn its customers’ trust. A model of intra-organizational trust is proposed; it is progressively improved as the IT organization develops its trust-building capabilities. The model comprises all IT service customers in an organization along with their communications and accounts for internal organizational IT service market specifics. A high-level blueprint of the trust model is described which can serve as a starting point when developing a full-scale trust model in a particular IT organization.
      We present an approach to the trust model improvement which builds upon principles adopted from widely recognized CMMI model. With this approach, an internal IT service provider can benefit from maturity assessment methods to improve its trust building capabilities.

       The shift to digital technologies in various industries is one of the key goals in the digital agenda. Due to the essential role of interoperability of products and elements in complex systems, standardization stays in the forefront of government policy and business. In manufacturing systems, standards are of a prime importance, since they serve as a channel for modernization and innovation speedup. This paper makes a contributionto the currently rare literature on digital manufacturing standardization as a policy tool to promote digital technologies in business. By comparing five national cases of China, Germany, Japan, the Republic of Korea and the USA, we introduce national models of standardization in smart manufacturing according to the extent of state participation in standardization. In doing so, we examined initiatives in industry, digitalization, the development of a national system of standards, the reference architecture of digital production, as well as the countries’ cooperation in the field. Along with this, an overview of international initiatives in the field is presented, namely the ISO and the IEC. Taking into account the existing landscape, an assessment of the Russian case of digitalization in manufacturing and standardization is presented. Like China, Russia follows the third model of standardization. Given the results, we developed recommendations for Russia with the aim of intensifying efforts at standardization and the country’s presence in the international agenda, as well as to develop a Russian framework for digital transformation in sectors and achieve related economic effects.