Economic tomography: the possibility to anticipate and respond to socio-economic crises

Aleksandr A. Kuklin, Gennadiy P. Bystray, Sergey A. Okhotnikov, Elena V. Vasilyeva


The article discusses an approach based on an original hypothesis related to the peculiarities of Russia’s development (on the one hand, its scale, the Russian mentality and a certain closeness of the economy; on the other hand, a significant dominant resource and human potential, and, as a consequence, a genuine role in the global economic community), the diagnosis of which (at the level of the well-being of individuals and inhabited areas) can be used to identify crises, provide an early assessment of threats to socio-economic development of regions as well as help to evaluate the state of the region over a 3 to 5 year period. In other words, in order to ensure that executives have enough time to mount a sufficiently rapid response to the crises and administrative errors and to reduce the impact of emerging threats. The aim of this paper is to present theoretical and methodological tools for the recognition of the early stages of emerging threats, allowing fewer losses to be experienced during the crisis period.

Simulation experiments were carried out for the purpose of classifying previously occurring social and economic crises (9 possible variants were reviewed) and mathematically processed trajectories of change in the main indicators for the well-being of individuals and inhabited areas, taking the influence of various factors into account. On the basis of the authors’ proposed approach (referred to as economic tomography) an attempt is made to comprehensively assess the state of sample representative regions of Russia.


economic tomography; welfare of individuals and inhabited areas; autocorrelation of parameter function shearing; crisis classification; system of non-linear non-homogeneous diflerential equations

Full Text:



Kuklin, A. A., Naydenov, A. S., Nikulina, N. L. & Tarasyeva, T. V. (2014). Transformatsiya teoretiko-metodologicheskikh podkhodov i metodicheskogo instrumentariya diagnostiki blagosostoyaniya lichnosti i territorii prozhivaniya. Ch. I. Ot rasprostranennykh do alternativnykh podkhodov k diagnostike. Istoriya voprosa [Transformation of theoretical and methodological approaches and methodological tools for the diagnosis of the welfare of individuals and inhabited areas. Part I. From generally accepted to alternative approaches to diagnosis. Background]. Ekonomika regiona [Economy of region], 3, 22–37.

Kuklin, A. A. & Chichkanov, V. P. (Eds). (2015). Kompleksnaya metodika diagnostiki blagosostoyaniya lichnosti i territorii prozhivaniya [Complex technique for diagnosing the welfare of individuals and inhabited areas]. Ekaterinburg: Institute of Economics of UB RAS, 136.

Eliseyeva, I. I. & Yuzbashev, M. M. (2002). Obshchaya teoriya statistiki: uchebnik [General theory of statistics: textbook]. In: I. I. Eliseyeva (Ed.). 4-e izd. pererab. i dop [4th revised and enlarged edition]. Moscow: Finansy i statistika [Ser. Finances and Statistics], 480.

Suslov, V. I., Ibragimov, N. M., Talysheva, L. P. & Tsyplakov, A. A. (2005). Ekonometriya [Econometrics]. Novosibirsk: Siberian Branch of RAS, 744.

Arnold, V. I. (2003). Matematicheskie metody klassicheskoy mekhaniki [Mathematical methods of classical mechanics]. 5-e izd., stereot. [5th reprint edition]. Moscow: Editorial URSS Publ., 416.

Ilyina, V. A. & Silaev, P. K. (2004). Chislennyye metody dlya fizikov-teoretikov. T. 2 [Numerical methods for theoretical physicists. Vol. 2]. Moscow; Izhevsk: Institute of Computer Research, 16–30.

Feder, E. (1991). Fraktaly: per. s angl. [Fractals: trans. from English]. Moscow: Mir Publ., 254.

Mandelbrot, B. (1972). Statistical Methodology for Non-Periodic Cycles: From the Covariance to R/S Analysis. Annals of Economic Social Measurement, 1(3), 259–290.

Rogers, L. C. G. (1997). Arbitrage with fractional Brownian motion. Mathematical Finance, 7, 95–105.

Khaken, G. (1991). Informatsiya i samoorganizatsiya: makroskopicheskiy podkhod k slozhnym sistemam [Information and selforganization: a macroscopic approach to complex systems]. Moscow: Mir Publ., 240.

Lifshits, E. M. & Pitaevskiy, L. P. (1979). Fizicheskaya kinetika. T. 10. [Physical kinetics. Vol. 10]. Moscow: Nauka Publ., 527.

Samarskiy, A. A., Kurdyumov, S. P., Akhromeeva, T. S. & Malinetskiy, G. G. (1987). Modelirovanie nelineynykh yavleniy v sovremennoy nauke [Simulation of nonlinear penomena in modern science]. Informatika i nauchno-tekhnicheskiy progress [Informatics and scientific-technical progress]. Moscow: Nauka Publ., 69–91.

Kuklin, A. A. & Gurban, I. A. (2012). Regionalnyye osobennosti demograficheskoy sostavlyayushchey chelovecheskogo kapitala [Regional characteristics of the demographic component of human capital]. Naradonaselenie [The population], 4 (58), 35–50.

Chereshnev, V. A., Kuklin, A. A. & Cherepanova, A. V. (2010). Teoretiko-metodologicheskiy podkhod k prognozirovaniyu sotsialno-demograficheskogo razvitiya regiona [Theoretical and methodological approach to forecasting regional socio-economic development]. Ekonomika regiona [Economy of region], 2, 38–46.


Copyright (c) 2018 Aleksandr A. Kuklin, Gennadiy P. Bystray, Sergey A. Okhotnikov, Elena V. Vasilyeva

Сertificate of registration media Эл № ФС77-80764 от 28.04.2021
Online ISSN 2412-0731