Economic Cybernetics

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Contextual Citation

"Today, the whole world is going through a certain shock, and does not understand what to do with the dying economy. The governments of all countries are moving in the direction of management of the people, rather than management of the economy. They are trying to use digital technology to manipulate people and for total control in a collapsing economy. The governments do not use the enormous potential that is contained in IT technologies and in their intended use - to raise the economy for the sake of benefit of the people. The Soviet Union should be honored for its experiment called "socialist planned economy" – for the first time in the world history the task to manage production relations in order to increase the well-being of people was set. Yes, the USSR did not succeed in solving the task optimally, when this unique historical experiment took place. But thanks to the experience of the USSR in modern Russia there is a Scientific and Collective Strategic Planning, based on the principles of economic cybernetics, that teaches how to manage the economy in the direction of growth of the public wealth. Capitalism had been developing over the centuries, manipulating people by means of money, creating financial systems for redistribution of income generated in all countries of the world in order to centralize the world capital. Now, as a result, the world stands on the edge of the abyss."

- Elena Veduta [1]

The Book

* Book: Economic Cybernetics. By Nikolai Veduto. 1971.


In Russian: Veduta N. I. (1971) Ekonomicheskaia kibernetika [Economic Cybernetics]. Minsk: Nauka itekhnika


Diana Kurkovsky West :

“Nikolai Veduta’s groundbreaking book Economic Cybernetics, published in 1971, advocated for the widespread use of cybernetic methods in planning the production, management, and overall organization of the Soviet economy (Veduta, 1971). Veduta was intimately familiar with the problems of the Soviet command economy: Since the 1950s, he had worked closely with scientists from the Central Economics and Mathematics Institute (CEMI) at the Academy of Sciences of the USSR, developing a pioneering direction of research in the domain of economic cybernetics alongside famous figures like Vasily Nemchinov and Nikolai Fedorenko. Between 1962 and 1967, he headed the Central Research Institute of Technical Management (CRITM), where he was tasked with the creation of the Soviet Union’s first automatic control system for factory production. Working at the Belarus branch of the Academy of Sciences, Veduta pushed for what seemed like an extraordinary idea –namely that in order to work, the Soviet central planning agency had to take on the role of a cybernetic servomechanism, steering the direction of an otherwise complex, dynamic, and emergent system.

Veduta was hardly alone in thinking that the Soviet planned economy would benefit from cybernetic methods. To him, and to other advocates of dynamic systems in economic planning, cybernetic research opened new possibilities for taking on the Herculean task of managing a command economy. A simple system of informational feedbacks and directives was insufficient; rather, the growing complexity of the Soviet socioeconomic system required a homeostatic regulator capable of evaluating the optimal conditions inside a changing system (Veduta, 1971: 34–5). Network-driven understanding of how the various branches of the Soviet economy related to each other was key to this process (ibid.: 269–75). The cybernetization of the Soviet economy would require the use of advanced computers and cutting-edge mathematical methods for regulating these networks not from any central node, but as dynamic, emergent systems.

In the years following the death of Stalin, cybernetics came to the forefront of Soviet planning agendas. Scholars disagree, however, about the extent to which cybernetic planning truly took hold. After the field’s heyday in the late 1950s and 1960s, cybernetics became absorbed into that unique breed of ‘Soviet-speak’ that dominated the pages of all Soviet publications, producing a discourse that the anthropologist Alexei Yurchak has called ‘hypernormalized’ to the point of obscurity (Yurchak, 2006: 50). The discursive practice of cybernetic language became what Slava Gerovitch calls ‘cyberspeak’: a space of co-producing ideological utterances and the language of science that was akin to George Orwell’s Newspeak captured in his dystopian novel Nineteen Eighty-Four. The All-State Automated System (known by its Russian acronym OGAS), the most audacious cybernetic project for integrating and computerizing the command economy, never transpired (Gerovitch, 2008; Peters, 2016). Well into the late 1980s, computers remained rare and costly, and few research institutes had access to them. The Coordinating Committee for Multilateral Export Controls (CoCom) export embargo effectively ensured that the only way in which the countries of the Soviet bloc could integrate with Western developments in computing was through reverse-engineering and copying the IBM System 360 and System 370 designs. For all of these reasons, cybernetics appears to have played more of a rhetorical, rather than a practical, function during the later decades of the Soviet Union’s existence. Was Soviet cybernetics merely a lofty ideal that never gained any real applications? Despite the field’s ideology-laden discourse, cybernetics research did produce tangible results, programmes, and conceptual frameworks. In his groundbreaking work From Newspeak to Cyberspeak, Slava Gerovitch first noted the rise in self-regulating concepts in the late 1960s and 1970s, with the proliferation of mathematics-based methods in economics, particularly in the field known as economic cybernetics (also known by the names of planometrics and econometrics) (Gerovitch, 2004). Recently, Adam Leeds has demonstrated that the field had powerful military roots in the USSR, leading to the development of programmes for rocketry, nuclear research, and air defence systems (Leeds, 2016). Eglė Rindzevičiūtė’s research traces the impact of cybernetic thought on systems modelling and the use of projective analytics (Rindzevičiūtė, 2015a, 2015b, 2016). Arguing against the prevailing opinion that Soviet computers were derivative and lagged behind their Western counterparts, Ksenia Tatarchenko’s research has exposed the extent of the Soviet computerization efforts conducted in the 1970s and 1980s (Tatarchenko, 2013, 2016). In this article, I contribute to the growing understanding of late Soviet cybernetic systems by looking into the markedly understudied domain of late Soviet economic planning. I will focus on the problem of territorial-production complexes (TPCs) in Siberia, which required a means of coordinating a wide range of factors in a system of national and regional planning. To this end, the work of researchers at the Novosibirsk Institute of Economics and Organization of Industrial Production (henceforth IEOIP) – a branch of the Soviet Academy of Sciences – in dynamic mathematical modelling will be my focus. Their approach to dynamic mathematical modelling on a regional scale belied an internalization of mid-century cybernetic concepts. It echoed Veduta’s articulation of a cybernetically governed economic whole composed of moving parts, all the while espousing the traditions of the Soviet school of economic geography. It also offered a vision of the Soviet economy as a network of dynamic interrelations, which could be steered cybernetically toward a general homeostasis, but whose emergent development would not be stunted through prescriptive planning.”


Economic Cybernetics for Socialism

Diana Kurkovsky West :

“To address the question of TPC planning, I must first situate the history of Soviet economic cybernetics as a cascading history – that is, as work that not only occurred inside the central institutes in Moscow, but which also ‘cascaded’ into regional and republic-based branches of the Academy of Sciences. I will therefore begin my inquiry with an overview of the history of economic cybernetics. The idea that the Soviet Union would be managed statistically had been the central premise of early Bolshevik thought, and was the vision behind the creation of Gosplan (the state central planning agency). With the advent of cybernetic theory, managing the entire economy from a central computer suddenly seemed possible. The appeal of cybernetic thinking for the command economy was evident in Soviet plans for connecting and regulating the economy through a cybernetic network called the OGAS, which Benjamin Peters has recently discussed in his book How Not to Network a Nation. The OGAS project, spearheaded by Viktor Glushkov, would have consisted of 20,000 data centres connected to 200 intermediate processing centres, and would have introduced a high level of self-governance, bureaucratic transparency, and even something we might consider a precursor to participatory governance and digital citizenship. As Peters argues, ‘although still hierarchical, acquiescent to Moscow as the center, [the OGAS] was openly worker-oriented, antibureaucratic, and decentralizing in principle’ (Peters, 2016: 113). Although the project never fully materialized – and Peters’ book uncovers compelling explanations for the bureaucratic stalemate that became the eventual death of the OGAS – it nonetheless offered a vision that was of a piece with the cybernetic ideals persisting in the Soviet discourse on governance. Glushkov, along with a number of eminent Soviet mathematicians like Leonid Kantorovich, Vasily Nemchinov, and others, put forth a vision of the economy that looked akin to managing a complex information network, where ‘all economic relations could be modeled, optimized, and managed with sufficient help from computers and their numerate keepers’ (ibid.: 67).

By 1960, mathematical models had gained prominent advocates in the new field of economic cybernetics. The reasons for this were as much political as they were pragmatic. Stalin had grown the Soviet economy at unprecedented rates, but by unconscionable methods, including famines that had starved millions of people in the Ukraine, and the use of prison labour for the construction of massive industries, canals, and dams in inhospitable climates. With Khrushchev’s rise to power, the political impetus to overturn Stalinism’s dark legacy led the new leadership to favour notions of profound economic reform. In this sense, the explosion in computing, cybernetic theory, and the changing political climate in the USSR were happily concurrent. Much of Kantorovich’s research into linear programming for algorithmically balancing complex and competing variables – research for which he received the Nobel Prize in 1975 – had already taken place in the 1930s, and therefore cannot be said to link directly to the advent of cybernetics. Rather, it seems that the complex demands of governing a vast command economy necessitated the kind of algorithmic thinking that was upheld and reified in cybernetic theory, and would be especially well-fitted for computer-aided analysis. In fact, a number of the key actors in economic cybernetics, among them Veduta, Nemchinov, Fedorenko, and others, articulated similar ideas about the mathematical and economic research of the 1920s and 1930s, and this, along with the pragmatic concerns of those ‘on the ground’ working in Soviet industries, conditioned Soviet planning in the direction of cybernetic thought.

With much support from Kantorovich, the economist-mathematician Vasily Nemchinov played a key role in the development of the Soviet school of economic cybernetics. In 1958, he founded the first laboratory of economic-mathematical modelling in Moscow, which lay the groundwork for the earlier mentioned 1963 establishment of the CEMI within the Academy of Sciences. Conjoining cybernetic methods with the task of algorithmic economic optimization, Nemchinov’s research built on the idea of inter-industry balances (known by their Russian acronym MOBs, or mezh-otraslevye balansy), which also owed a great debt to the Russian-American Nobel Prize-winning economist Wassily Leontief and his input–output model of inter-sector dependencies (Leontief, 1986[1951]).

Thanks in great part to the efforts of Kantorovich, Nemchinov, Nikolai Fedorenko, and others, a large network of institutes devoted specifically to research into economic cybernetics emerged, working mainly on the development of an optimal planning system based on mathematical analysis. Concurrently, the growing system of Inter-industry balances, building on Kantorovich’s linear programming concepts, ensured that the Central Statistical Office modelled demand trends and balance of labour. At the same time, the Soviets were concerned with global modelling trends; as Eglė Rindzevičiūtė’s research demonstrates, scientists from the Computer Centre and the Institute for Systems Analysis (VNIISI), both based at the All-Union Soviet Academy of Sciences in Moscow, were actively involved in international organizations such as the United Nations and the Institute of Applied Systems Analysis (IIASA) in Austria. Fedorenko, the founder of CEMI, was also a member of the Club of Rome, and attended the 1965 economic congresses in Rome (Rindzevičiūtė, 2015b). This ensured that leading Soviet scientists were familiar with the research conducted at Massachusetts Institute of Technology (MIT) by Jay Forrester in system dynamics, as well as with the widely known and heavily criticized Limits to Growth Report, the so-called ‘curve to Doomsday’ computer model that predicted global resource depletion by 2072. Authors of the computer model and the report, among them Donella and Richard Meadows and MIT’s Forrester, were invited to tour the Soviet Union, and Meadows made over 20 subsequent trips to the USSR (ibid.: 5). I will return to Limits to Growth later in this article, as researchers at IEOIP offered a cybernetic critique of the problem of resource depletion.

Writing about the Soviet use of cybernetics in social sciences in the 1970s, David Holloway noted that the period of initial optimism about creating a highly centralized computer system for managing the economy had been short-lived, and researchers at the various institutes (including the Siberian IEOIP) had realized that the processing power necessary for the central computer to deal with the amount of information faced by Gosplan was orders of magnitude greater than what was plausible at the time. This further advanced the cybernetic steersman model of decentralized decision-making among cybernetic economists. On the one hand, central planning would be ‘required in order to make structural changes in the economy, to determine its basic proportions, and to fix the parameters which regulate the behavior of lower-level units’ (Holloway, 1976: 115). On the other hand, however, a great degree of self-governance on the lower level was necessary in order not to overwhelm the central planning apparatus. This interest in using cybernetics not to control the Soviet economy at all levels, but rather to set the general parameters for economic governance, became an important and largely unstudied feature of late Soviet planning. This sentiment is evident in the TPC optimization research, which advocated the creation of territorially optimized sub-units within the broader system of Soviet production.”

Conclusion: Planning for a cybernetic future

A late Soviet textbook on economic planning captured the challenge of the command economy as follows: ‘When approached broadly, the problem of territorial organization of production, besides the economic and social aspects, also touches technical, ecological, planning, architectural, and other questions’ (Kistanov, 1981: 5–6). For this reason, it argued, the problem of territorial organization was always a priori macroeconomic; the optimization of the entire Soviet economy, in turn, was a cybernetic challenge to meet the macroeconomic development goals. TPC optimization grappled continually with a range of local and national challenges and imperatives, working toward a system that would be capable of optimizing any configuration of factors at a given point in time. Thus, a marriage of economic cybernetics and economic geography was key for maintaining this kind of nationally solvent, yet locally alterable system. Unlike some of the earlier ideologically motivated publications on cybernetics for socialism, TPC planning did not advocate a specific teleology, posit eternal ideals, or lay claim to ultimate scientific methods. Instead, it anticipated modifications, a host of changing economic, social, and ecological factors, and sought a high degree of administrative autonomy from national ministries, councils, and central planning organs. Not geared toward a single, optimized, and perfected end goal, TPC optimization models were systems for continual input–output analysis, which would be reconfigured based on new information and research. In time, these systems would also learn, evaluating new inputs against the larger statistical body of past information received.

The work on mathematical modelling and system optimization coming out of the Academy of Sciences points to the existence of an important late Soviet internalization of cybernetic ideas in Soviet economics. This work not only resonated with the research agendas of CEMI and other Moscow-based institutes, but also aimed to integrate a cybernetic agenda into the core of Soviet regional planning. It also points to a more emergent cybernetic body of operations, which not only enhances our understanding of dynamic systems within late Soviet planning, but also points to the way in which Soviet researchers tried to embed the concept of entropy into central planning through dynamic mathematical modelling.

In conclusion, I would like to return to Nikolai Veduta’s conceptualization of the cybernetic command economy as a massive, steerable whole composed of dynamic moving parts. The work of IEOIP took that challenge to another level, presenting calculations for a total vision of the Soviet economy depicted in spatial terms. The factories, railways, dams, settlements, and entire production complex of the vast Siberian territories had to be ‘hard-wired’ into the landscape, and unlike abstract economic concepts, the built environment was not easily alterable once those elements were in place. In this sense, projects like the OGAS played second fiddle to the actual tasks of computerization within each industry, region, or factory complex, which simply could not wait for a centralized network to emerge. Critical of over-determining and over-centralizing Soviet economic cybernetics, Veduta focused on loose coupling, arguing that loosely coupled systems were much more desirable than the tightly coupled systems of feedbacks usually coveted by cyberneticians. He wrote that if, in a tightly coupled system, the output was not desirable, the system would be far more difficult to change than if there was sufficiently loose coupling to allow for intervention. Thus, in building a computerized system for steering the Soviet economy, he insisted that the network be loosely coupled, localized, and distributed. ‘Practice’, he wrote, ‘always leaves room for uncertainty [sic] in the outcome of any experiment’ (Veduta, 1971: 58). For Veduta working at the Academy of Sciences in Minsk, as well as for the researchers at the Siberian IEOIP, the optimization of the Soviet economy entailed the emergence of complex industrial systems, which, though computerized, would retain a great degree of autonomy. Simply put, Soviet TPC planning was to establish general laws for the cybernetized command economy of the future, where entropy was a positive feature allowing for superior, localized governance.”


Balance-Oriented Economic-Mathematical Models for Sustainable Production Planning

“Burmatova believed that the answer to resource depletion lay in a system of continual informational feedback, performed in real time, in the future world of a fully computerized command economy.”

Diana Kurkovsky West :

“Olga Burmatova, who to this day publishes widely and teaches in Novosibirsk, received her doctorate at Novosibisk State University, studying at the faculty of economic cybernetics before landing a postdoctoral researcher position at IEOIP under the direction of Mark Bandman. Her work focused specifically on addressing ecological problems of industrialization. In a 1983 monograph, which is formally coauthored with Bandman as the head of the institute, she argued that the extant models of TPC optimization were not set up to calculate the environmental impact of industrial activities (Burmatova, 1983: 3). Through a combination of empirical research and theoretical contribution, Burmatova set out to offer an ecologically inclusive model that also served as a tacit critique of top-down decision-making in Soviet planning.

In the process, Burmatova also engaged in a polemic with another important model of the era: the quantitative model of technological growth offered in the 1972 publication The Limits to Growth Report by Donella Meadows, Dennis Meadows, Jorgen Randers, and William W. Behrens, III. Patrick McCray has argued that the Limits to Growth model and report ‘came as a culmination of growing ambivalence, confusion, and pessimism about the future and technology’s place in it’ (McCray, 2013: 20). Representing the era’s growing recognition of the planet’s limited resources and the rapid pace of resource depletion, the computer-generated model advanced in the book became popularly known as ‘a computer curve to Doomsday’ (ibid.: 32).

The Limits to Growth used five variables to posit a model of exponential growth in resource depletion in the face of finite resources available in the world: world population, industrialization, pollution, food production, and resource depletion (Meadows et al., 1972). By developing what they called the exponential reserve index, the authors argued that, assuming a growth in consumption at a constant rate of 2.6% every year, the world would run out of resources in roughly 100 years. Burmatova contended, however, that The Limits to Growth offered a scenario that was too dire and did not adequately address the dynamism inherent in the global resource system. Instead, she suggested, problems of resource depletion could be prevented completely in a planned economy through precise economic and mathematical modelling and a scientific approach to balancing the environmental impact of TPCs (Burmatova, 1983: 16).

Burmatova advocated the use of ‘balance-oriented economic-mathematical models’ (balansovye ekonomiko-matematicheskie modeli) to consider the ecological aspect of TPC planning. Although still a young scholar, she critiqued both Leontief and the American Walter Isard for not being sufficiently comprehensive: To her mind, their notions of planning did not account sufficiently for the dynamic processes inherent in the emergence, growth, and obsolescence of certain industries, products, and plants. In fact, she contended, the very notion of a TPC as an integrated planning unit allowed for the kind of comprehensive, dynamic analysis most foreign models missed:

- At the same time, a large range of problems…can be solved only on a local or regional level.…Precisely on the local level of analysis concerning the relationship of economic and ecologic systems gives one the opportunity to more closely study and consider the specificities of individual plants, natural resources, and conditions of a given territory from the point of view of nature conservation and protection. (Burmatova, 1983: 32)

She argued, in turn, that a multifaceted analysis of TPC planning would consider local particularities as part of the total system of analysis. The end result would be a model of emergent, dynamic planning that would be optimized and customizable to each specific scenario, and open to the inclusion of environmental variables.

This research on ecological planning served to critique the prioritization of national economic imperatives, and Burmatova admitted that ‘the cleanliness of the environment represents a kind of limiting factor to economic growth’ (Burmatova, 1983: 189). The environmental considerations privileged local needs above the national, putting public health ahead of economic output agendas. Thus, her vision exposed the limits of central planning. On the one hand, a centrally planned economy could anticipate and prevent the environmental damage caused by the profit-driven industrial boom, uncontrolled population growth, resource depletion, and other factors outlined in the Limits to Growth model. On the other hand, its regional goals were beholden to the central apparatus and its priorities in optimizing the economic output of a national system of TPCs.

Although Burmatova’s work did not explicitly express its adherence to cybernetic ideas, it would be safe to say that the institutionalization of systems-driven approaches in the Novosibirsk research institutes had a profound impact on the modelling ideas behind her work. The kinds of calculations her research advocated were possible only when one received continually updated information regarding the state of the environment in a given area. ‘The current informational system of activities for environmental protection is inadequate, and there are no sufficient studies regarding numerous types of conservation objects’ (Burmatova, 1983: 218). Calling for more research, Burmatova argued that these kinds of mathematical analyses would yield positive results only if more information were acquired and fed back into the TPC planning system. Ultimately, the problems of environmental degradation would be solved through a mechanism that would continually re-evaluate the goals of TPC optimization vis-a-vis their environmental impact, which would balance production costs against the costs to the environment. Building on IEOIP’s ongoing engagement with economic cybernetics for TPC optimization, Burmatova believed that the answer to resource depletion lay in a system of continual informational feedback, performed in real time, in the future world of a fully computerized command economy.”


The Concept

Elena Veduta:

"Economic cybernetics - as the science of information management in the economy. This science allowed him to create a dynamic model of the IIB, which is a system of algorithms for calculating the cost / release plan using modern digital technologies. This model is based on the knowledge of the objective economic laws that are contained in “Capital”. When you describe the behavior of the system with the help of mathematical algorithms based on the laws of economic development, you get in the virtual world a cyber-system — a reflection of a really functioning economy." (


  • Andrey Fefelov: the direction of economic cybernetics was removed in the 1980s, and this was done consciously. Now we are facing a very difficult task of revivification of those developments. How can this direction work and be useful today in the national economy?

Elena Veduta. In fact, everything is much simpler than it seems to be. If we speak in popular language the model can be described very simply. Whom does the economy work for? For the final consumers. What are they? They are: households that spend their cash income at the consumer market and change it for the products offered; a state that finances the defense costs, health care, education, space exploration programs, etc. at the expense of its budget; exporters who supply products in accordance with international contracts.

In order to satisfy the needs of the final consumers, the production of the final product according to their orders and demands must be arranged. For example, the Ministry of Defense says that it needs a defense order: exact number of tanks, missiles, etc. Ministry of Health requires exact amount of materials, hospitals, etc. People need exact amount of milk, meat ... To optimize the structure of the final product for the consumer market, we use feedback information about the dynamics of equilibrium market prices, that provide the demand equal to supply. Thus, we have formed orders for the final product, which meets the needs of final consumers. Then the final product should be produced. How to produce it? To answer this question we need the calculation of production chains with possible adjustment of orders that takes into account the real production possibilities. It means that a plan is calculated for the proportional development of the economy in the direction of growth of public wealth. Production chains are consecutive in time and in space, producers’ interconnections (“input / output balance”) is used for the production of final product ordered. If Russia is a country that respects itself, it needs to rely on its production chains, rather than tail as a raw materials appendage of the global economy with an outstretched hand: please, give us money (foreign investment).

The method of calculation is very simple: to produce something, we calculate how much resources we needed for the production of the final product. These resources do not fall from the sky, they must be also produced. By taking consecutive steps from the final product to the calculations of resources for its production, taking into account the intermediate products, we are gradually approaching the final calculation of all costs necessary to produce the final product.

Since the production capacity (to produce all these products) may not be enough, we must provide in the calculations for the production of additional capacity (enterprises, infrastructure, etc.).

The “input / output balance” is calculated in the same information field. If there are inventions, new technologies at some enterprises, it is necessary to choose the most effective ones that will give the greatest return in terms of the movement of the whole economy of the country in the direction of growth of public wealth. To implement it a block that helps to choose effective technologies is put in this model.

Calculations in the model continue until the optimal balance between production and use of resources is obtained to fulfill the order dictated by the final consumers. We will actually produce the maximum of the desired final product, since we provide the introduction of new technologies that reduce the cost of producing a unit of the final product. Our needs, which we dictate by the final product, are linked to our production capabilities. In this model, the managing parameter is the state production investment, which is destined to the development of industries and the introduction of new technologies in accordance with the requirements of an optimal balance.These investments are not just money begged from foreign investors or from the Central Bank. In this case, the production investment is ensured by material and labor resources that rules out the start of inflation.

The participation of small and medium business is assumed in this cyber-economic model. If we introduce cyber-economy (economy managed with the help of digital technologies), we will get an impulse for development, because the economy will start the sustained development, inflation will stop.

This will turn to be opposite to the current situation, when our corporations are built into the global chains as a raw materials appendage, and today we don’t make any calculations because we don’t need them. We just agree that we can borrow the incidence of crisis development of the civil society.

Using economic cybernetics as the science of management of information processes in the economy, our government will get an effective tool for the economy and state management that moves it forward along the trajectory of the social benefit – the growth of real incomes of citizens (the real solvency of national currency), the strengthening of national security and strengthening of the competitiveness of the state in the global community."

* Andrey Fefelov. Is it true that the model of economic cybernetics is supported by digital capabilities of modern technology?

Elena Veduta. Yes, it is the material and technical basis for the functioning of cyber economics, knocking on all doors today – just the same way the machine had come to our life and had changed the system of feudalism into the system of capitalism. The invention of computer and digital technology shows similar process. Modern digital technologies, with their colossal possibilities for collecting, storing and processing information, make it possible to organize a national economy based on a dynamic model of IIB.

Andrey Fefelov. Recently, I have been constantly talking about digital socialism, digital science as a way of life. I mean the digital economy as a part of digital science in general, because it includes education, government, elections, and anything you like. By the way, the digital economy abolishes corruption completely, since it is a completely transparent system.

Elena Veduta. Of course, even if you concealed the reliable information, the cyber-economic model will reveal everything. However, it is necessary to agree on conditions: the model allows the multi-level access rights to data which is loaded, used, and obtained in the course of operation at different stages, depending on the rights of the user (an ordinary employee of a certain industry or enterprise, the head, the controlling bodies), legal or state secrecy, etc. All such characteristics of the cyber-economic model should be discussed with the leaders of the country, industries, business, security agencies, supervisory authorities ... and then given to programmers for implementation in the current model. At the same time, of course, the model is alive and can be updated and changed in the course of its operation."

More information


*eBook: Solving the Global Crisis Requires the Approach of Economics Cybernetics. By Elena Veduta.

URL = [2]

"The book researches the mythology and practice of UN National Accounting Systems, the economic planning of the USSR and the World Bank. It contains a presentation of the dynamic model of interbranch-intersectoral balance as a system of algorithms which coordinates the orders of final customers (state, households, exporters) with producers' production possibilities. Only this system of algorithms can be used as a cybernetic digital platform for efficient decision making of state and global management."