Economic informatics(computer science from French. information- information and automatique- automatic; literally “the science of automation of information processing”) - the science of information systems used to prepare and make decisions in management, economics and business, as well as the economics of these systems.

Economic informatics is a new discipline that emerged in the second half of the 20th century in connection with the rapid development of computer technology and the growth of its application in economics. In Anglo-Saxon countries, computer science is called computer science (literally “the science of computers”), and economic information science is called information systems (literally “information systems”). Modern economic informatics is, first of all, an applied discipline that systematizes the principles of development and operation of information systems (hereinafter referred to as IS) designed to solve various economic problems. Thus, it is located at the intersection of computer science itself and the subject area of ​​organization management for which the specialized systems being created were intended. Even in Anglo-Saxon countries, such specialized applied knowledge is in some cases called “computer science”, in particular, there are bioinformatics and military informatics.

Economic computer science also has a common area with economic theory. This general field is the economics of information, a discipline that studies the economic patterns of information creation and dissemination in markets and organizations. In economic computer science, it allows us to describe the value of information and the impact of markets for information goods on the value of IP.

Object and subject of economic informatics

The core of economic informatics includes, first of all, applied knowledge necessary for building IS in the economy and management of organizations in any field - business, non-profit structures and government bodies. In economic informatics, IP is understood as a system designed for collecting, transmitting, processing, storing and issuing information to consumers using computing and communications equipment, software and service personnel.

Influence information systems on the economics of organizations that implement and use them, is described in terms business processes. Implementation information systems creates new IT services, which, in turn, change parameters business processes organizations, their productivity, quality and sustainability. As a result, if implementation is successful, the organization's current profitability and/or long-term competitiveness increases. Therefore, studying business processes commercial and non-profit organizations is one of the main areas of research in economic informatics. These studies include studying the components business process, its quantitative and qualitative characteristics, the IT services it uses, the connection of the business process and its results with the structure of the organization, etc. As a result of these studies, several problems are solved at once:

Along with business processes, economic informatics studies the components of the IS itself: information technology, applications and management. Information technology - technological infrastructure that ensures implementation information processes. It includes all types of computer and telecommunications equipment, system software that controls the operation of the latter, and instrumental environments that support the operation of applications. Information technologies are considered in economic informatics as a means of improving business processes and overcoming their limitations. At the same time, the introduction of information technology does not automatically lead to improvement of business processes; for this it must be combined with the implementation of applications, changes in business processes themselves, advanced training of enterprise employees and improved management information systems. An important part of information technology is platforms - software systems that allow the development of applications.

Applications are specialized programs that directly support certain IT services as part of business processes. Applications can be separate products (business applications) or be part of certain integrated management systems (functional subsystems). Applications have now been developed for all areas of enterprise operations and management - procurement, production, marketing and sales, maintenance, human resources, technological development, finance, accounting, etc. The diversity and complexity of modern applications has made it difficult for them to work together within the same enterprise.

For a long time, this problem was solved by creating large monolithic application packages that included the above applications as functional subsystems. Nowadays, the development of integration tools based primarily on the SOA architecture has led to the opposite trend, the development of more narrowly focused applications focused on specific subject areas.

For example, SAP, the world's largest manufacturer of business software, currently releases a package of applications SAP Business Suite, which includes the ERP system SAP ERP, CRM system SAP CRM, product lifecycle management system SAP PLM, supply chain management system SAP SCM and supplier relationship management system SAP SRM. It should be emphasized that all of the above are different applications integrated through SOA services. To support SOA services, SAP has created its own integration platform, SAP NetWeaver. Other market leaders have integration platforms similar in purpose - Oracle Fusion Middleware from Oracle, IBM WebSphere from IBM, etc. Each of these platforms can work not only with the manufacturer's applications, but also with applications from other companies, which increases the flexibility of the created systems.

Finally, information systems management ensures coordination among all other IS components, as well as coordination of the development of information systems with business requirements. Enterprise information systems management includes personnel, user, quality, financial and security management, as well as operational management and IS development management. Thus, management turns out to be an extremely important component of the IS, and its improvement, corresponding to the improvement of applications and their technological foundation, is a condition for the balanced development of the system as a whole. According to modern ideas, IS management is, first of all, IT service management.

A separate task is the analysis and design of the architecture of enterprise information systems. Here the modeling apparatus is somewhat broader, along with modeling functions and data, it includes engineering methods for analyzing and predicting IS performance, statistical tools, economic analysis, etc. A special problem is the integration of IS architecture with business architecture and organizational architecture, which is solved by methods of management theory.

The problem of improving IS management is solved by methods of management theory, including methods of operations research, organizational theory, logistics, etc. Project management methods and models are of great importance. Recently, the role of project control methods that ensure the achievement of the planned economic effect during the implementation of IS has been growing.

To solve the problem of analyzing and improving the economic efficiency of information systems, various methods of economic analysis are used. Currently we are talking about neoclassical tools, new institutional economic theory and management theory. Each approach uses a variety of techniques, described in the category Economic theory. These same classes of methods are used in the economic analysis of information and markets for information goods.

Short story

Although the prehistory of computer science dates back to at least the 19th century, the history of the use of computers in economics began only in the 50s. 20th century. From this moment we will count the history of economic informatics.

In the initial period, in the 50s and 60s, the computer was a rare and expensive resource. Therefore, the first task of economic informatics was to increase the efficiency of computer use. The first steps on this path were the creation of an operating system - a software package that organizes and maintains the computing process on a computer, and high-level programming languages, as well as compilers from these languages. Already at this stage it became clear that economic problems, unlike, for example, scientific problems, require much simpler computational algorithms, but require means of processing large volumes of data with a complex structure. As a result, the COBOL language was developed, supporting complex hierarchical data structures. A further development of this approach was the development of specialized platforms that made it possible to create and maintain increasingly complex databases. These platforms are called database management systems (DBMS).

In the 70s and 80s, the next period in the history of economic informatics began, characterized by the growing penetration of computers into business. At the same time, the computers themselves and their infrastructure became more complex and diverse. New classes of computers have appeared - mini-computers and personal computers (PCs), local and global computer networks, new classes of software. As a result, computers no longer automated individual labor-intensive tasks, but entire functions of the enterprise, including such important ones as production and purchasing planning, accounting and management accounting, design work, etc. For these purposes, new classes of applications were developed - MRP and , later, MRP II, the first integrated production management systems, project management systems, etc. This, in turn, required a means of documenting the relevant business functions and describing the data used in them. The result was the first standards of the IDEF family, including the IDEF 0 function description standard, the IDEF 1X data modeling standard, and several others.

During these same years, economic computer science first encountered the so-called “productivity paradox.” It was that while business and government investments in IT were growing, there were no signs of productivity growth associated with these investments. Nobel laureate R. Solow expressed this problem in clear form: “We see the computer age everywhere except productivity statistics.” Despite the challenge of R. Solow, in the 80s. There was no evidence of a positive impact of IT investment on productivity.

The sharply more complex computing environment of an enterprise, in particular, the explosive growth in the use of personal computers, has caused an accelerated increase in costs for IP. As a result, IT management has increased its focus on cost control. To solve this problem, the Gartner Group developed a TCO model that made it possible to take into account the entire total cost of using IP throughout the entire life cycle of the latter. Although this model was a significant advance in IT cost accounting, it had a number of shortcomings, as a result of which its widespread use in some cases led to incorrect conclusions. The largest of these mistakes was the initiative to develop a network computer specifically designed to reduce the TCO of corporate IP. A number of major PC manufacturers have launched their networked computers onto the market without any success. Interestingly, later, in the 2000s. The ideas of a network computer were again in demand, and this time with much greater success. However, in the 80s. the project turned out to be premature.

90s were marked by two major technical innovations - the transition to the so-called. client-server architecture and the widespread use of the Internet. The new IS architecture meant a transition to distributed applications, one part of which carried out data processing as such and was located on computers specially dedicated for this (servers), and the other ensured the transmission of requests to servers, receiving responses from the latter and presenting the results of requests to the end user (client). It was according to this scheme that e-mail, work with databases, and provision of Internet access were organized.

The Internet became another, even more significant revolution of the 90s. It should be noted that the Internet infrastructure in the form of data networks and global computer networks was created much earlier (the first segments of the ARPAnet network, the predecessor of the Internet, were created back in 1969), the massive use of the Internet by individual users and corporations occurred precisely in the 90s gg. This was due to the emergence of the “World Wide Web” WWW - a network of hyperlinks that connected arrays of information (“pages”) located both on the same server and on different servers. At the same time, search engines appeared, allowing Internet users to quickly find the necessary information. The new technology was quickly commercialized, first for advertising, then for actual transactions. Already in 1994, the bookselling site Amazon.com appeared, and in 1995, the online auction Ebay. At the same time, in the 90s, the payment and logistics infrastructure for Internet transactions took shape. As a result, a large number of businesses have emerged that exist exclusively on the Internet - the so-called. dot-com. Inflated expectations for such businesses gave rise to the so-called “dot-com bubble” - an unjustified increase in stock prices of Internet companies. This “bubble” ended with the crash of 2000.

The rapid development of technology has posed new challenges for economic informatics. First, the pervasive nature of IT has created a need for an integrated description of the role of IT in business. This description is based on the concepts of business process and value chains. This provided a holistic view of the business process, especially important when changing the latter.

Secondly, a whole series of new classes of applications have emerged that solve newly emerging business management problems. These were, first of all, ERP systems, which became a further development of MRP II systems. In addition to them, customer relationship management (CRM), supplier relationship management (SRM) and supply chain management (SCM) systems were created.

Increased computing power, as well as data storage capacity, have made it possible to create specialized analytical systems that process data in real time (OLAP). Finally, the emergence of electronic business gave rise to a new broad class of systems that mediate electronic transactions - B2B, B2C, etc.

Thirdly, there has been a further complication of the tasks of IT services in enterprises. A standard model of IT service business processes, containing the main tasks of the latter and well-proven approaches to solving them, could provide important assistance in these conditions. Such a model was the ITIL model, the first version of which appeared at the turn of the 80s - 90s. Wide recognition of the model in business and government agencies led to the rapid improvement of the library, and at the turn of the 90s - 2000s. its second version was released, and in 2007 the third. Currently, the ITIL library has become the de facto standard for IP management in Europe. Another response to the increasing complexity of the tasks of the IT service was IS outsourcing - the transfer of all or part of the IS maintenance functions to an external supplier. Outsourcing became a popular solution to IT service problems in the 90s.

Finally, in the 90s. The IT productivity paradox has been resolved. A number of researchers have shown that in the presence of complementary changes in business processes firms' IP investment has a significant positive impact on productivity. At the same time, a significant contribution of investments in IP to the capitalization of the company on the stock market was discovered.

The current stage of IP development has brought new achievements. One of the most important was the SOA business application integration technology, which for the first time made it possible to ensure stable and effective interaction of applications from different suppliers. Perhaps an even more important advancement was the so-called. “cloud computing”, which is the provision of IT services over the Internet, in which the details of the IT infrastructure are hidden from the end users of the service. This eliminates most application compatibility and integration issues. Cloud computing eliminates the specific requirements that a number of IT services place on a customer's IT infrastructure, making IT services as easy to obtain as power from an electrical outlet. An important factor in the development of IT has also been the widespread use of open source software, which represents not so much a technical innovation as an alternative model of copyright.

In parallel with the development of technology, IP management and economic analysis of the latter developed. In management, the main direction of development has been the deepening of outsourcing, the transition from outsourcing of individual IS support functions to outsourcing of business processes as a whole. Outsourcing also influenced the development of the ITIL model, which in its third version is focused not so much on enterprise IT services, as before, but on outsourcing service providers.

In the economics of IP, one of the most important areas has become the economics of copyright. The development of the market for information goods, on the one hand, sharply expanded the volume of consumption of the latter, on the other hand, it limited the rights of users to consume the latter. The severe restrictions placed on users of information goods have given rise to widespread discussion of the economics of copyright in terms of the balance between incentives for innovation and monopoly rights of producers. This has deepened understanding of the institution of copyright, but has not yet led to practical recommendations in this area.

Open source software has become a real alternative to the institution of copyright in the field of software. The GPL license provides the user with four freedoms: freedom to use the software, freedom to study the software and change the source code, freedom to distribute copies of the software, and freedom to distribute modified software. The main limitation imposed by the GPL is that software obtained under the GPL must continue to be distributed under the terms of the GPL.

Economic informatics developed along a special path in the USSR. The planned economy, on the one hand, created a number of incentives for the introduction of information technologies and systems into the national economy, on the other, it imposed extremely strict restrictions on their use. As a result, the introduction of information technologies and systems into the national economy of the USSR was limited and inconsistent, although it led to a number of major successes.

The first success was the very creation of the computer technology industry in the USSR, which for several decades remained at the level of advanced Western countries. Among the creators of Soviet computer technology, S.A. should be mentioned first of all. Lebedeva, I.S. Bruka, B.I. Rameeva, V.M. Glushkov and G.P. Lopato, who created independent design schools for the development of computers and established their mass production.

The development of computer production has raised the question of their use in the national economy. Already in 1959 A.I. Berg, A.I. Kitov and A.A. Lyapunov in his report “On the possibilities of automating the management of the national economy” raised the question of the use of computers in managing the national economy. However, the technical capabilities of computers at that time did not allow the large-scale use of computers in planning - the main function of managing the national economy at that time. Serious attempts at such automation were made only in the 70s. in the form of an attempt to create an ACS system (automated control systems) with OGAS (National Automated System for collecting, storing and processing information) at the top level.

Large-scale investments in automated control systems did not bring the expected return. The use of automated control systems encountered problems with the quality of information and turned out to be incompatible with the real economic mechanisms operating in a socialist economy. In the context of shock economic reforms of the 1990s. ACS developers were unable to adapt them to new economic conditions, as a result of which ACS quickly faded away. In modern Russia, economic informatics has not received significant development, and the existing works are fragmentary.

Structure of economic informatics

In modern economic informatics, the following main directions can be distinguished.

First of all, this is the analysis and modeling of business processes. This is a complex and large-scale activity, taking into account the specifics of industries and countries. An important part of it is the description and analysis of newly emerged business processes and business models. Today, such models are based on the increasing use of IT. A feature of recent decades has been end-to-end business processes, covering a number of interconnected enterprises, united, first of all, through IP.

The complexity and, at the same time, dynamism of modern IS require special attention to the problems of IS architecture. It is the timely and accurate solution of architectural problems that allows us to ensure high quality IT services even in the face of large-scale changes. Economic informatics creates a theoretical and methodological basis for such decisions. Today, several trends can be identified in IS architecture:

Ensuring integration of IT architecture and business and organizational architecture;

Building an organization’s IT architecture based on a network of interconnected service providers that outsource business processes;

Corporate data finds itself at the center of modern IT architecture, especially in conditions of developed outsourcing;

Increasing the flexibility of IT services and ease of access to them by end users, primarily based on cloud computing.

A separate area of ​​economic informatics is the development of IP management. Today, the ITIL model dominates in this area, but the question of the boundaries of its application remains unresolved. An important area of ​​research is also the study of outsourcing, the criteria for its success and ways to achieve it. Finally, in modern conditions, measuring and ensuring the economic efficiency of IP is of particular importance, which we will discuss in more detail below.

Although the “productivity paradox” has long been resolved, research into the cost-effectiveness of IS is still an important part of economic computer science. Today, the main directions for increasing the efficiency of information systems have already been outlined, these are solving real business problems using IT, changing business processes aimed at unlocking the potential of IT, and improving staff qualifications. Along with this, IP allows you to change the company's portfolio of products and services, making it more flexible and diversified.

Finally, the increasing focus on purchased IS components and purchased services increases the importance of the market for information goods. The study of this market using economic informatics methods is increasingly important for this science.

Unresolved problems and priority areas

Despite a number of successes, a number of unsolved problems remain in economic informatics today. Here are the most important of them:

• What determines the success of IS in an organization? Despite developed recommendations for the development and implementation of information systems, projects for the development and implementation of information systems end in failure in 30-50% of cases, according to various estimates.
• How to evaluate the effectiveness of IS in specific situations? Research into the effectiveness of IS has not yet led to the development of practically valuable methods that allow assessing the effectiveness of specific projects in this area.
• Are best practices always best practices? A number of studies show that the organizations observed today belong to several different types (in the original author's terminology, configurations). Probably different configurations require different ICs and various approaches to their implementation.
• How reasonable is today's copyright law? The restrictions imposed by modern copyright on end users are seen as increasingly onerous, and reasonable alternatives are emerging.

Recommended reading

F. Webster. Theories of the information society.

M. Porter. Competition (collection of articles).

G. Mintzberg. Structure in the fist.

G. Mintzberg. Management: the nature and structure of organizations through the eyes of a guru.

Jesus Huerta de Soto. Socialism, economic calculation and the entrepreneurial function.

E. Furubotn, R. Richter, Institutions and economic theory: achievements of the new institutional economic theory.

B. Gladkikh. Computer science from the abacus to the Internet. This includes computers, servers, peripheral equipment, storage equipment, etc.

It was in the 19th century that storage of information on punched cards, Charles Babbage’s “Analytical Engine” and, finally, the tabulator, a computing device that processes data stored on punched cards, were invented

Lugachev M.I.

Moscow State University named after M.V.Lomonosova, Doctor of Economics, Professor, Head. Department of Economic Informatics, Faculty of Economics, ТП@ econ. gshi. w

Economic Informatics at the University

education in Russia

KEYWORDS

Computer science, economic computer science, applied computer science, business computer science, IT education.

ANNOTATION

We will try to present a picture of the development of economic computer science in Russia, considering the dynamics of university structures that provide extensive training for specialists in the field of computer science in general. Economic computer science was created by two closely related streams of knowledge, formed in the depths of mathematics and economics. The computers that appeared in response to the needs of science and defense departments obviously had enormous potential for their use in traditional (non-military) branches of science and the national economy. To realize this potential, a new type of specialist was needed, capable of effectively using and developing emerging computing capabilities. Prepare these

Only new institutes and faculties were capable of specialists, the curricula of which would combine the competencies of mathematicians, physicists, economists, and specialists in the field of programming - which formed the fundamental basis for the development of information technology. Looking ahead, it can be noted that such institutes and faculties have been created and are successfully solving the assigned tasks of training specialists in the field of IT and IS. The only point here is that economists have not yet proven themselves sufficiently in this activity.

The beginning of the computer era in the USSR. Mathematics, technology and economics.

As is known, work on the creation of the first computer in the USSR - a small electronic calculating machine (MESM) - was started in Kyiv by a team led by S. A. Lebedev in 1948. MESM was put into operation in December 1951.

On December 4, 1948, the State Committee of the Council of Ministers of the USSR for the introduction of advanced technology into the national economy registered the invention of I. S. Bruk and B. I. Rameev “Automatic Digital Electronic Machine” under No. 10475. This invention was brought to life at the Energy Institute of the USSR Academy of Sciences in Moscow, in a laboratory headed by I. S. Bruk in the form of an M-1 computer. In January 1952, the M-1 was put into trial operation. One of the first M-1 solved problems in nuclear research of the group of Academician S. L. Sobolev, at the Institute of I. V. Kurchatov. It was manufactured in a single copy, but its architecture and many fundamental decisions were later adopted as the basis for the development of serial vehicles “Minsk”, “Hrazdan”, etc.

But mathematics lived not only in traditional scientific and engineering calculations. In 1923-24, V.V. Leontyev formulated the problem of building an inter-industry balance, which required large computing power18. At the end of the 30s, the works of L.V. Kantorovich appeared, creating the basis for the penetration of mathematics into economic calculations. The famous “plywood trust” problem was formulated, which became the basis for the formation of an optimization approach in economic planning. In 1937, L.V. Kantorovich, at the request of engineers from the local plywood trust, solved the problem of finding the best way to process 5 types of material on 8 machines with a certain productivity of each of them for each type of material. In a seemingly simple problem, L.V. Kantorovich saw and for the first time formulated a linear programming problem and proposed a method for solving it, which significantly reduced the search for optimal solutions and assumed the necessary application

18 In 1973, V.V. Leontiev was awarded the Nobel Prize in Economics for developing the “Input-Output” method for constructing an input-output balance.

computer technology.19

An important stage in the creativity of L.V. Kantorovich was published in "Advances of Mathematical Sciences" in 1948 by his large article "Functional Analysis and Applied Mathematics", and then in 1956 "Functional Analysis and Computational Mathematics", which made functional analysis the natural language of computational mathematics. According to academician S.L. Sobolev, just a few years later it was just as impossible to imagine computational mathematics without functional analysis as without computers.

These ideas of the unity of functional analysis and computational mathematics, as well as connections with economics, were consistently embodied by L.V. Kantorovich brought to life: when organizing in 1948 the training of specialists in “computational mathematics” at the Faculty of Mathematics and Mechanics of Leningrad State University and later - in 1958 - when creating the specialty “economic cybernetics” at the Faculty of Economics of Leningrad State University In 1959, L.V. Kantorovich became one of the organizers (and teachers) of the famous “sixth course” of the Faculty of Economics of Leningrad State University. Graduates of the regular fifth year and a number of young economists were enrolled in the “sixth year” for an in-depth study of mathematical methods and computers. It should be noted that some graduates of this course had a noticeable influence on the development of Soviet and Russian economic science, in particular, academicians of the USSR Academy of Sciences: A.G. Aganbegyan, A.I.Anchishkin, N.Ya.Petrakov, S.S.Shatalin.

Naturally, the processes of development of training of specialists in the field of computational mathematics and economic and mathematical methods were not isolated. At the same time, similar processes of forming the basis for the use of computer technology in science and economics were taking place in Moscow and Moscow State University. In 1949, the Department of Computational Mathematics was created at the Faculty of Mechanics and Mathematics of Moscow State University, which in 1952-1960 was headed by Academician S. L. Sobolev, already quoted above. At that time, such outstanding specialists as A. A. Lyapunov, M. V. Keldysh, M. R. Shura-Bura and others taught at the department.

In 1958, the outstanding economist and statistician, Academician of the USSR Academy of Sciences V.S. Nemchinov, created a laboratory of economic and mathematical methods at the Academy of Sciences, and in 1962, at the Faculty of Economics of Moscow State University, the Department of Mathematical Methods of Economic Analysis (MMAE). The famous 6th year graduates of L.V. Kantorovich - S.S. Shatalin (headed the department in

19 In 1965, L.V. Kantorovich, together with V.S. Nemchinov and V.V. Novozhilov received the Lenin Prize “for the scientific development of the linear programming method and mathematical models of the economy.” In 1975, L.V. Kantorovich and T. Koopmans were awarded the Nobel Prize in Economics for creating the foundations of linear programming.

1970 -1983) and N.Ya. Petrakov - Director of the Institute of Market Problems of the Russian Academy of Sciences (from 1990 to 2014). L.V. Kantorovich himself led a scientific seminar at this department for a number of years in the 70s of the twentieth century. The need for graduates of this department was largely formed by the Central Economics and Mathematics Institute of the USSR Academy of Sciences (CEMI AS of the USSR), created in 1963 on the basis of the laboratory of the same name, which served for many years as a professional nursery for the training of specialists by the Department of MMAE of Moscow State University. CEMI of the USSR Academy of Sciences, of course, was created on the initiative and with the participation of V.S. Nemchinov. Academician N.P. Fedorenko became the first director of the institute, and in 1985 he was replaced by academician V.L. Makarov, L.V. Kantorovich’s closest student.

The 1950s and 60s added a lot to the awareness of the need to expand the training of specialists in the field of software, not only technological, but also economic processes. First of all, this was facilitated by the problems of the new science “Operations Research” generated by computational mathematics, algorithms for solving inventory management problems, as well as the formulation of scientific principles of enterprise management. There is experience in using the first business information system, Material Resource Planning (MRP), developed in the 1950s in the USA, but which began working on real business problems in the 1960s. Even those who doubted were finally convinced of the enormous possibilities of using electronic computers (computers) in the economy.

An important stage in the development of this direction at Moscow State University was the organization, under the leadership of Professor I.S. Berezin, in 1955 of a computer center, the first in the USSR university space. The Moscow State University Computer Center has become the base for training specialists at the Department of Computational Mathematics. The computing center has created a scientific and technical platform for a significant expansion of the contingent of training specialists in the field of computer software. On the basis of the Department of Computational Mathematics of the Faculty of Mechanics and Mathematics and the Computing Center (CC MSU), the Faculty of Computational Mathematics and Cybernetics (CMC) of MSU was organized. The founder of the new faculty and its first dean was Academician A.N. Tikhonov, scientific director of the Computing Center of Moscow State University and head of the Department of Computational Mathematics of the Faculty of Mechanics and Mathematics. Andrei Nikolaevich was the first who not only realized the needs of science and the national economy for a new type of specialist, but also managed to achieve the creation in the country of a system for training personnel in the field of computational mathematics and programming. In February 1970, the USSR Ministry of Higher Education issued order No. 114 approving the structure of the Faculty of Computational Mathematics and Mathematics of Moscow State University. The Faculty of Computational Mathematics and Mathematics of Moscow State University became the first in the list of almost 50 similar faculties newly created in the early 1970s in large universities of the USSR. A whole branch of training specialists in the field of computer software has emerged,

which was supposed to support major changes in Soviet policy on the creation and use of computer technology. It was about the country's transition to new standards of information technology - the introduction of the "Unified System" - a line of computers that copied the architecture of American computers of the IBM-360 series. The need for such a solution had already formed: it was dictated by the concept of the National Automated System (OGAS) developed under the leadership of V.M. Glushkov. OGAS was designed to solve the national problem of accounting and control for the unhindered application of socialist planning and management methods in the USSR,

Computer revolution, Soviet style

On March 18, 1968, Order No. 138 of the Minister of Radio Industry of the USSR was issued on the creation of NICEVT and its appointment as the parent organization for the development of the Unified Computer System (ES COMPUTER) “Ryad”. Since then, debates and discussions have not subsided about the advisability of the decision to produce EU machines by copying the architecture of the IBM S/360 mainframe.

It should be noted that until 1968, the creation of computers in the USSR was rather poorly coordinated. There were several design bureaus in different parts of the USSR, which developed wonderful second-generation computers that worked in various mathematics and met their own technological standards. The undisputed leader was the powerful BESM-6 of the design bureau of S.A. Lebedev, which used a 6-bit word. Minsk computers with a 7-bit byte were popular in the national economy (only the Minsk-32 computer designed by V.V. Przhiyalkovsky was eventually produced about 3,000 pieces). The family of vehicles of the Ural series produced in Penza, developed by B.I. Rameev, was very progressive. The Ukrainian “Mir”, the Yerevan “Nairi”, the Vilnius “Ruta-110”, and the Moscow “Setun” had their advantages. (Note that the unique computer “Setun”, which used the ternary number system, was developed at Moscow State University under the leadership of N.P. Brusentsov). It is not necessary to add that each family was provided with its own peripherals and original software. This talented and interesting variety of computers could solve local problems of various natures, but with their help it was impossible to create a national infrastructure for organizing information processes. Thus, the question of the prospects for the development of domestic electronic computer engineering sounded very relevant. In 1966, the national economic plan mentioned that new third-generation computers should be built on “a unified structural and microelectronic technological base and compatible programming systems for computer centers and automated information processing systems.”

In the official report of ITMiVT, in mid-1966, there was a clear answer as to how

S.A. Lebedev did not allow us to build “Ryad”. However, together with academician V. M. Glushkov, he expressed the opinion that copying the third generation computer IBM S/360 would mean lagging behind world standards by several years, since the S/360 series began to be produced back in 1964. If only these outstanding scientists knew how optimistic their assessment was.

In the diversity that existed in the USSR, computers of the Ural family were closest to the requirements of the third generation. Bashir Iskanderovich Rameev formulated and implemented the idea of ​​a family of computers on the principle of software and design compatibility, independently and earlier than the IBM S/360 series. However, when developing the decision of the State Commission of the Ministry of Radio Industry of the USSR in 1968, the domestic version was not considered at all. Only the American IBM and the British ICL took part in the discussion. The choice made by the commission still does not leave specialists in the field of computer technology indifferent. The debate about whether this decision was a strategic mistake and who is to blame continues. The minutes of meetings of state commissions record the objections of domestic developers Lebedev, Rameev, Glushkov, and others - but the firm position of the President of the USSR Academy of Sciences M.V. Keldysh and the Minister of Radio Industry of the USSR

V.D. Kalmykova resolved the issue in favor of copying IBM S/360.

This was a tragic decision for the Soviet computer industry, which destroyed the strategic guidelines for its development. The gigantic intellectual capital of domestic developments in the form of produced and promising computer equipment, as well as the corresponding software, became unnecessary along with its carrier - a large detachment of highly qualified specialists. Some were able to retrain, but the focus was on training new professionals. True, there remained a serious contingent of developers for military purposes, headed by a student

S.A. Lebedeva - Academician V.S. Burtsev. The computer support for the S-300 missile systems, developed under the leadership of V.S. Burtsev, still successfully solves the assigned tasks. In addition, the scientific legacy he left behind still feeds the ideas of supercomputer developers.

However, from an economic point of view, it can be said with confidence that the decisions adopted in 1968 by the State Commission of the USSR Ministry of Radio Industry did not have a fateful impact on a national scale for the national economy of the country. No option, even the best one from a technological point of view, for the development of domestic computer engineering could correct the ineffective socialist system of the national economy. The idealistic planned economy was doomed even if the OGAS project had been successfully implemented, since this economy lacked natural market mechanisms for managing the economy. Planning elements can be good and

useful if they do not claim to be universally applicable always and everywhere. Western economists, in particular L. von Mises, back in the 1920s, proved the impossibility of rational economic calculation in a system where there is no private ownership of production resources and no real (market) prices (von Mises theorem). Before technological re-equipment in the USSR, it was necessary to reform the economy - create conditions for the emergence of real economic instruments of self-regulation. So in 1968 in the USSR it was quite possible to forget about IBM, rely on the promising Ural family of computers, or leave all the existing ones - there could be fewer negative consequences for the national economy. At the same time, it is difficult to deny the significant progress that has emerged in the development of the national programming industry, whose specialists, during the transition to international standards, acquired new opportunities for organizing work and gained access to the world's accumulated program libraries. Preparation and decision-making in specific areas, including the national economy, have been enriched by access to already established databases of industry applications.

A new era of IT specialist training

So, a unified policy of computer support for scientific developments and the national economy of the USSR required adequate mass personnel support. Methodological work on organizing the all-Union training of the necessary specialists was actually headed by the Faculty of Computational Mathematics and Mathematics of Moscow State University, relying on the authority and knowledge of the highest professionals of the USSR Academy of Sciences. The “academic” provision of methods for training IT specialists could be the envy of any scientific center in the world. The regulatory component was provided by the USSR Ministry of Education.

It can be noted that in the world, control over the creation of methodological foundations for the training of IT specialists has traditionally been an area of ​​interest for professional public organizations. In the United States, this role has been taken on by the Association for Computing Machinery (ACM) and the Computer Society of the IEEE (IEEECS).

who have been doing this work since the 60s. last century . In 1965, the Education Committee of the ACM organization developed the first draft of a standard program for undergraduate courses in Computer Science, which, after revision, was published in 1968 in its final form, becoming known as Curriculum 68. There was no normative component in the developed document , it had a recommendatory nature for American universities, but de facto quite quickly turned into an international standard for training IT specialists “Computing Curriculum (CC)”. Sponsored by ACM and IEEE-CS

Peter Denning's group prepared the report "Computing as a Discipline" in 1989. In the new discipline “Computing”, two components were distinguished: “Computer Science” and “Computer Engineering”. This was later methodically embodied in the fundamental curriculum CC2001, which was developed in versions CC2005. But CC2005 already contained a fundamental difference from previous versions - it clearly indicates the need to train specialists for applied industries. Global professional organizations AIS (Association of Information Systems) and AITP (Association for Information Technology Professionals) - create IS2002. A new full member appears in the computing family - information systems. CC2005 “Computing” includes the following areas: Computer Engineering (CE), Computer Science (CS), Software Engineering (SE), Information Technology Systems (IT), Information Systems ( Information Systems - IS). Russian higher education also responds to the need for specialists for the preparation, development and operation of applications in professional university training. In 2000, a new state educational standard for specialty 351340 “Applied Informatics (by area)” appeared (order of the Russian Ministry of Education dated March 14, 2000).

The document specifies: “A computer science graduate (with qualifications in the field) must have a specialization determined by the scope of application of computer science methods and professionally oriented information systems, a list of disciplines studied in a particular field, information disciplines and final qualifying work.” At the same time, the area of ​​application of qualified knowledge is determined: “A computer scientist (with qualifications in the field) deals to a greater extent with a professionally oriented shell (which he designs, creates and applies), consisting of special software, information support and organizational measures to support the functioning of specific processes in the field of application, and to a lesser extent deals with the core of the information system (development of a complex of computing tools, operating system, database management systems, etc.).”

A little later, in 2003, another specialty standard 080500 “Business Informatics” was opened (order of the Ministry of Education of the Russian Federation dated July 8, 2003) for the training of specialists whose area of ​​professional activity “includes: Enterprise architecture design, Strategic planning for the development of IP and ICT enterprise management, Organization life cycle processes of IP and ICT enterprise management, Analytical support of processes

making decisions for enterprise management."

Thus, the Russian economy receives specialists in “Applied Informatics” to provide IT support for information processes in the industries: “economics, law, political science, psychology, sociology, political science, psychology, ecology, humanitarian-social and others, in which professional oriented information systems...", as well as specialists in "Business Informatics" to support information processes within enterprises.

Now the de facto global methodological standard for training IT specialists for applied industries is the Information Systems 2010 (IS2010) curriculum, created by the entire professional IT world using the Wiki resource. The professional field of graduates of this direction is most fully described in CC2005. It also distinguishes between the target areas of training for IS and IT specialists: “Professionals in this specialty (Information Systems) primarily deal with the information that a computer can provide to an enterprise so that it can better define and achieve its goals, as well as with the processes that the enterprise implements or improves with the help of information technology. ... Information Systems focuses on the information aspects of information technology. Information Technologies are this kind of complement: their area of ​​interest is the technologies themselves, but not the information they process. IT programs are designed to produce graduates with the right combination of theoretical knowledge and practical skills to manage an organization's information technology and the people who use it."

Didactic role of economic informatics

The presented description of curricula for national training of specialists in the field of “Applied Informatics”, “Business Informatics” and the closely related American curriculum “Information Systems - IS 2010” allows us to introduce a new direction into consideration: “Economic Informatics” in order to analyze the general and different and evaluate its prospects.

Firstly, it should be noted that “Economic Informatics” is not included in the national list of professional training specialties and one of the goals of this study is to prove the advisability of considering this issue, perhaps in the context of other areas, which will be discussed below.

Economic informatics is the science of information systems used in economics and business, as well as the economics of these systems.

This definition contains an indication of the difference between areas

applications: economic informatics deals with the comparison of costs and benefits from the use of information systems in the traditional scheme of economic analysis. Both “Applied Informatics” and “Business Informatics” and “IS -2010” are focused on training specialists in the field of application of information technology to solve problems in the subject area. Assessing the effectiveness of such decisions remains the subject of classical economics. In addition, an information product that has many non-trivial properties of pricing, consumption and development requires economic description and study. There are traditional economic issues: production and distribution of information products. Moore's law requires an economic interpretation, according to which a more productive information product has a lower cost. The market for information products is formed and develops according to its own laws: real material entities also circulate on it, but the main engine of this market is an intangible service or service that has the property of inexhaustibility and marginal costs tending to zero. Here, new industries (gaming) are created out of the “information air,” and gigantic fortunes arise “out of nothing.” Finally, information itself becomes a commodity, which classical economic models are not suitable for describing: demand does not generate supply. Information products are appearing, the economic properties of which require modern interpretation: unlimited access to cloud services provided to end users free of charge, growth in the consumer properties of information technology products without increasing their prices. The price structure of an information product is also unusual, in which marginal costs tend to zero.

Nowadays, the economics of information systems looks as natural as the economics of any branch of the national economy - for example, agricultural economics or industrial economics. But the information market has little in common with the grain market, and new research is needed to describe the market for information products.

In general, when discussing issues of general economic informatics with computing, it should be noted that these sciences have a direct connection only when considering information technologies (IT) and information systems (IS). At the same time, for economists in the term “information technology” - in the first place are “information”, “information”, services that provide information processes, and only then - “technology”. As noted above, information systems are the object of study of economic informatics and the name itself is characterized by the presence of the definition “information” and not “computing” - directly following from the basic direction of “computing”, since modern applied tasks, including

economic content - are associated primarily with the processing and analysis of meaningful information, considering the calculations themselves as a necessary accessible tool.

Speaking about the effectiveness of information systems, we can note the objective relevance of the emergence of “Economic Informatics”: today the environment for using information systems has changed qualitatively. According to some experts, traditional consulting in the field of using IP, aimed at formulating the goals and objectives of implementation and selecting the best IP option for a particular enterprise, has practically disappeared. Over decades of the active entry of information systems into the practice of planning, management and decision-making of organizations, a sufficiently qualified contingent of users has been formed, capable of independently answering the initial questions of the formulation of technical specifications. In addition, IT standardization has enabled convergence processes that have, in practice, minimized the consequences of IP type selection error. The main issue of consulting was the problem of the efficiency of the functioning of the information system, its influence on the processes of adding value to the enterprise. There is only one way to answer this question: to give approaches to assessing the costs and benefits of using computers.

It is obvious that there is no economic field of activity for IT specialists preparing according to the curricula and curricula discussed above. This is not surprising: the field of activity of IT specialists is in the nature of engineering and technological services for business. The subtleties of identifying and assessing costs and benefits belong to the field of economics and are traditionally of no interest to IT students. Moreover, the work on such assessment is not structured and cannot be reduced to a familiar business process or a well-known algorithm with a fixed number of iterations. This is a matter for economists.

What is the outcome of studying in the field of economic informatics? What will graduates who have completed the full training cycle know and be able to do?

The fundamental thing in organizing IT training for economists is the formulation of two important provisions.

The first is the correct definition of the “entry point” of IT and IS into a specific subject area of ​​economics and business. For economics and business, this role is played by the business process and the IT services that provide it, for education - the educational process, for healthcare - the medical process, etc. A distinctive feature of the main essence of a particular application is its process nature, widespread distribution in the subject area, repeatability in time and space. Specification of the main essence is the task of specialists - economists. The purpose of training these IT and IS specialists is to provide them with the knowledge, skills and abilities to describe IT services used to

automation of business processes.

The second provision is a clear definition of the goals of training future specialists in the field of IT and IS. In our opinion, good knowledge, skills and abilities in the field of IT and IP allow a graduate to gain a competitive advantage in the professional market. For classical universities and modern research institutions, it seems natural to formulate the role of IT and IS as tools for increasing the efficiency of basic business processes: scientific and educational activities. The main goal of using these tools is to improve the quality of training of specialists and ensure a high management level of operating activities and the competitiveness of the organization. Achieving the highest professional level in economics and business by universities is possible only by building a logical chain of training their own IT specialists. The elements or stages of this chain are known: bachelor's degree - master's degree - graduate school. Conventionally, we can assume that each stage has its own level of IT training. Basic - for a bachelor's degree, professional - for a master's degree, research - for a postgraduate student. The success of training will be more noticeable for young specialists, for the university, and for the entire industry if the result of each stage is a specialist in a specific subject area, rather than a technical field. For this purpose, it is necessary to create an appropriate institutional environment, an element of which will be a national education system with educational standards and a corresponding specialty - economic informatics.

Similar proposals would be valid for other specialties: historical computer science, biological, medical, ... It seems that they should all be represented in the list of university specialties. But according to the draft new order of the Ministry of Education, only business, bio-, geo- and applied informatics appear on this list.

In fact, the training of such specialists is ongoing, it is often carried out intuitively, and significantly depends on subjective factors. However, decades of widespread use of information technologies and systems have already created a sufficient professional reserve of industry competencies, there are ideas about professional standards - all this should lead to the official creation of corresponding subject specializations and higher education specialties.

Conclusion

Professional groups have already formed around the world and in Russia, dealing with the problems of economics of information systems. In modern conditions, these issues become key when addressing issues of selection, implementation and operation of information systems in

enterprises and organizations.

Currently, there is no system for training specialists capable of analyzing the economic consequences of implementing information systems. The current IT education system mainly solves the problem of training technical specialists.

Ensuring the innovative development of specific applied sectors of higher education requires the creation of a system for training IT specialists within the applied humanitarian and socio-economic sectors. This requires the creation of specialties not only in applied (technical) but also in subject information science.

Literature

1. Kantorovich L.V., “Mathematical methods of organizing and planning production”, L.: Publishing house of the Leningrad State University, 1939. - 67 p.

2. Kantorovich L.V. Functional analysis and applied mathematics. "Advances in Mathematical Sciences" 1948

3. Kantorovich L.V. Functional analysis and computational mathematics, 1956. http://en.cs.msu.ru/node/62 - history of computer science until 2000.

4. Maxon: Two tragedies of Soviet cybernetics. EYE OF THE PLANET information and analytical portal^M, 02/29/2012.

5. Ludwig von Mises. Die Wirtschaftsreсhnung im sozialistischen Gemeinwesen", Archiv fuer Sozialwissenschaften und Sozialpolitik, Vol. XLVII, No. 1 (April, 1920).

6. Mises L. Human activity. Treatise on economic theory. M., Economics, 2000.

7. “Efficiency of investments in IT”, M., SoDIT, 2013, 194 pp. ISBN 978-5-4465-0104-5.

8. Sukhomlin V.A. International educational standards in the field of information technology. Applied Informatics, No. 1 (37), 2012.

9. Computing Curricula 2001 (CC2001). Computer Science, Final Report, (December 15, 2001). The Joint Task Force on Computing Curricula, IEEE Computer Society, Association for Computing Machinery.

10. Computing Curricula 2005 (CC2005). The Overview Report, covering undergraduate degree programs in Computer Engineering, Computer Science, Information Systems, Information Technology, Software Engineering. The Association for Computing Machinery (ACM), The Association for Information Systems (AIS), The Computer Society (IEEE-CS), 30 September 2005.

11. J.T. Gorgone, G.B. Davis, J.S. Valacich, H.Topi, D.L. Feinstein, H. E. Longenecker, Jr. IS 2002, Model Curriculum and Guidelines for Undergraduate Degree Programs in Information Systems. Association for Computing Machinery (ACM), Association for Information Systems (AIS), Association of Information Technology Professionals AITP.

12. H. Topi, J. S. Valacich, R. T. Wright, K. M. Kaiser, J. F. Nunamaker, Jr., J. C. Sipior, G. J. de Vreede. IS 2010, Model Curriculum and Guidelines for Undergraduate Degree Programs in Information Systems. Association for Computing Machinery (ACM), Association for Information Systems (AIS).

13. Lugachev M.I., Abramov V.G., Skripkin K.G., Tikhomirov V.V. Methodology for developing programs in the discipline “Informatics” for areas of non-core education. Max Press, M., 2006.

14. Lugachev M.I., Skripkin K.G., IT competencies as part of economic education. Bulletin of Moscow State University. Series 6, “Economics”, No. 4, 2009.

15. Draft Order of the Ministry of Education and Science of Russia “On approval of lists of specialties and areas of training in higher education” http://www.consultant.ru/law/hotdocs/26905.html

Ministry of Education of Ukraine

Kyiv National Economic University

"Economic Informatics"

Introduction.

Man has always made decisions in all areas of his activity. An important area of ​​decision making is related to production. The larger the production volume, the more difficult it is to make a decision and, therefore, the easier it is to make a mistake. A natural question arises: is it possible to use a computer to avoid such errors? The answer to this question is given by a science called cybernetics.

Cybernetics (derived from the Greek “kybernetike” - the art of management) is the science of the general laws of receiving, storing, transmitting and processing information.

The most important branch of cybernetics is economic cybernetics - a science that deals with the application of the ideas and methods of cybernetics to economic systems.

Economic cybernetics uses a set of methods for studying management processes in the economy, including economic and mathematical methods.

Currently, the use of computers in production management has reached a large scale. However, in most cases, computers are used to solve so-called routine tasks, that is, tasks related to the processing of various data, which before the use of computers were solved in the same way, but manually. Another class of problems that can be solved using a computer are decision-making problems. To use a computer for decision making, it is necessary to create a mathematical model.

Is it really necessary to use computers when making decisions?

Human capabilities are quite diverse. If we put them in order, we can distinguish two types: physical and mental. Man is so constructed that what he possesses is not enough for him. And the endless process of increasing its capabilities begins. To lift more, one of the first inventions appears - a lever; to move a load more easily - a wheel. These tools still only use the energy of man himself. Over time, the use of external energy sources begins: gunpowder, steam, electricity, atomic energy. It is impossible to estimate how much the energy used from external sources exceeds human physical capabilities today. As for the mental abilities of a person, then, as they say, everyone is dissatisfied with his condition, but is satisfied with his mind. Is it possible to make a person smarter than he is? To answer this question, it should be clarified that all human intellectual activity can be divided into formalized and informal.

Formalized activity is an activity that is performed according to certain rules. For example, performing calculations, searching in reference books, and graphic work can undoubtedly be entrusted to a computer. And like everything a computer can do, it does it better, that is, faster and better than a person.

Informalized activity is an activity that occurs using some rules unknown to us. Thinking, consideration, intuition, common sense - we do not yet know what it is, and naturally, all this cannot be entrusted to a computer, if only because we simply do not know what to entrust, what task to assign to the computer.

A type of mental activity is decision making. It is generally accepted that decision making is an informal activity. However, this is not always the case. On the one hand, we don't know how we make decisions. And explaining some words with the help of others like “we make decisions using common sense” does not give anything. On the other hand, a significant number of decision-making problems can be formalized. One type of decision-making problem that can be formalized is optimal decision-making problem, or optimization problem. The optimization problem is solved using mathematical models and the use of computer technology.

Modern computers meet the highest requirements. They are capable of performing millions of operations per second, their memory can contain all the necessary information, and the display-keyboard combination ensures a dialogue between a person and a computer. However, one should not confuse successes in the creation of computers with achievements in the field of their application. In fact, all that a computer can do is, according to a program specified by a person, to ensure the transformation of source data into results. We must clearly understand that the computer does not and cannot make decisions. The decision can only be made by a human leader who is endowed with certain rights for this purpose. But for a competent manager, a computer is an excellent assistant, capable of developing and offering a set of a wide variety of solutions. And from this set, a person will choose the option that, from his point of view, turns out to be more suitable. Of course, not all decision-making problems can be solved using a computer. Nevertheless, even if solving a problem on a computer does not end in complete success, it still turns out to be useful, since it contributes to a deeper understanding of this problem and its more rigorous formulation.

Solution stages.

1. Selecting a task

2. Modeling

3. Drawing up an algorithm

4. Programming

5. Entering initial data

6. Analysis of the obtained solution

In order for a person to make a decision without a computer, he often doesn’t need anything. I thought and decided. A person, good or bad, solves all the problems that arise before him. True, there are no guarantees of correctness in this case. The computer does not make any decisions, but only helps to find possible solutions. This process consists of the following steps:

1. Selecting a task.

Solving a problem, especially a fairly complex one, is quite a difficult task and requires a lot of time. And if the task is chosen poorly, this can lead to loss of time and disappointment in using a computer for decision making. What basic requirements must the task satisfy?

A. There must be at least one solution to it, because if there are no solution options, then there is nothing to choose from.

B. We must clearly know in what sense the sought solution should be the best, because if we do not know what we want, the computer will not be able to help us choose the best solution.

The choice of a problem ends with its meaningful formulation. It is necessary to clearly formulate the problem in ordinary language, highlight the purpose of the research, indicate the limitations, and pose the main questions to which we want to receive answers as a result of solving the problem.

Here we should highlight the most essential features of an economic object, the most important dependencies that we want to take into account when building a model. Some hypotheses for the development of the research object are formed, the identified dependencies and relationships are studied. When a problem is selected and its content is formulated, one has to deal with experts in the subject area (engineers, technologists, designers, etc.). These specialists, as a rule, know their subject very well, but do not always have an idea of ​​what is required to solve a problem on a computer. Therefore, a meaningful formulation of a problem often turns out to be oversaturated with information that is completely unnecessary for working on a computer.

2. Modeling

An economic-mathematical model is understood as a mathematical description of the economic object or process under study, in which economic patterns are expressed in abstract form using mathematical relationships.

The basic principles of creating a model come down to the following two concepts:

1. When formulating a problem, it is necessary to cover the phenomenon being modeled quite broadly. Otherwise, the model will not provide a global optimum and will not reflect the essence of the matter. The danger is that optimizing one part may come at the expense of others and to the detriment of the overall organization.

2. The model should be as simple as possible. The model must be such that it can be evaluated, verified and understood, and the results obtained from the model must be clear to both its creator and the decision maker.

In practice, these concepts often conflict, primarily because there is a human element involved in collecting and entering data, checking errors, and interpreting results, which limits the size of the model that can be analyzed satisfactorily. The size of the model is used as a limiting factor, and if we want to increase the breadth of coverage, we have to reduce the detail and vice versa.

Let's introduce the concept of a hierarchy of models, where the breadth of coverage increases and the detail decreases as we move to higher levels of the hierarchy. At higher levels, in turn, restrictions and goals are formed for lower levels.

When building a model, it is also necessary to take into account the time aspect: the planning horizon generally increases with the growth of the hierarchy. While the long-term planning model of an entire corporation may contain few day-to-day, day-to-day details, the production planning model of an individual division consists mainly of such details.

When formulating a problem, the following three aspects must be taken into account:

1. Factors to be Investigated: The objectives of the study are fairly loosely defined and largely depend on what is included in the model. In this regard, it is easier for engineers, since the factors they study are usually standard, and the objective function is expressed in terms of maximum income, minimum costs, or perhaps minimum consumption of some resource. At the same time, sociologists, for example, usually set themselves the goal of "social utility" or the like, and find themselves in the difficult position of having to attribute a certain "utility" to various actions, expressing it in mathematical form.

2. Physical boundaries: The spatial aspects of the study require detailed consideration. If production is concentrated at more than one point, then it is necessary to take into account the corresponding distribution processes in the model. These processes may include warehousing, transportation, and equipment scheduling tasks.

3. Time Limits: The time aspects of the study pose a serious dilemma. Usually the planning horizon is well known, but a choice must be made: either model the system dynamically in order to obtain time schedules, or model static functioning at a certain point in time.

If a dynamic (multi-stage) process is being modeled, then the size of the model increases according to the number of time periods (stages) under consideration. Such models are usually conceptually simple, so the main difficulty lies more in the ability to solve the problem on a computer in an acceptable time than in the ability to interpret a large volume of output data. c It is often sufficient to build a model of the system at a given point in time, for example, at a fixed year, month, day, and then repeat the calculations at certain intervals. In general, the availability of resources in a dynamic model is often estimated approximately and determined by factors beyond the scope of the model. Therefore, it is necessary to carefully analyze whether it is really necessary to know the time dependence of the model characteristics, or whether the same result can be obtained by repeating the static calculations for a number of different fixed moments.

3. Drawing up an algorithm.

An algorithm is a finite set of rules that allow a purely mechanical solution to any specific problem from a certain class of similar problems. This means:

¨ the initial data can change within certain limits: (massiveness of the algorithm)

¨ the process of applying rules to the initial data (the path to solving the problem) is uniquely defined: (determinism of the algorithm)

¨ at each step of the process of applying the rules, it is known what to consider as the result of this process: (the effectiveness of the algorithm)

If the model describes the relationship between the initial data and the desired quantities, then the algorithm is a sequence of actions that must be performed in order to move from the initial data to the desired quantities.

A convenient form of writing an algorithm is a block diagram. It not only quite clearly describes the algorithm, but is also the basis for drawing up a program. Each class of mathematical models has its own solution method, which is implemented in an algorithm. Therefore, it is very important to classify problems according to the type of mathematical model. With this approach, problems of different content can be solved using the same algorithm. Algorithms for decision-making problems are, as a rule, so complex that it is almost impossible to implement them without the use of a computer.

4. Drawing up a program.

The algorithm is written using ordinary mathematical symbols. In order for it to be read by a computer, it is necessary to create a program. A program is a description of an algorithm for solving a problem, specified in a computer language. Algorithms and programs are united by the concept of “mathematical software”. Currently, the cost of software is approximately one and a half times the cost of a computer, and a further relative increase in the cost of software is constantly occurring. Already today, the subject of acquisition is precisely mathematical software, and the computer itself is just a container, packaging for it.

Not every task requires an individual program. Today, powerful modern software tools have been created - application software packages (APP).

PPP is a combination of model, algorithm and program. Often, you can choose a ready-made package for a task that works great and solves many problems, including ours. With this approach, many problems will be solved quickly enough, because there is no need to engage in programming.

If it is impossible to use the PPP to solve a problem without changing it or the model, then you need to either adjust the model to the PPP input, or modify the PPP input so that the model can be entered into it.

This procedure is called adaptation. If a suitable PPP is in the computer memory, then the user’s job is to enter the necessary required data and obtain the required result.

5. Entering initial data.

Before entering the initial data into the computer, it is naturally necessary to collect it. Moreover, not all the initial data available in production, as is often attempted, but only those that are included in the mathematical model. Consequently, collecting initial data is not only advisable, but also necessary, only after the mathematical model is known. Having the program and entering the initial data into the computer, we will obtain a solution to the problem.

6. Analysis of the obtained solution

Unfortunately, quite often mathematical modeling is mixed with a one-time solution to a specific problem with initial, often unreliable data. To successfully manage complex objects, it is necessary to constantly rebuild the model on a computer, adjusting the initial data taking into account the changed situation. It is inappropriate to spend time and money on drawing up a mathematical model in order to perform one single calculation on it. An economic-mathematical model is an excellent means of obtaining answers to a wide range of questions that arise during planning, design and production. A computer can become a reliable assistant in making everyday decisions that arise in the course of operational production management.

DESCRIPTIVE LIMITATIONS

These constraints describe the functioning of the system under study. They represent a special group of balance equations related to the characteristics of individual blocks, such as mass, energy, costs. The fact that in a linear programming model the balance equations must be linear excludes the possibility of representing such fundamentally nonlinear dependencies as complex chemical reactions. However, those changes in operating conditions that allow a linear description (at least approximately) can be taken into account in the model. Balance ratios can be entered for some complete part of the flowchart. In static (one-stage) models such relationships can be

present in the form:

Input + output = 0

The dynamic (multistage) process is described by the relations:

Input + output + accumulation = 0,

where savings is understood as net growth for the period under review.

LIMITATIONS ON RESOURCES AND FINAL CONSUMPTION

With these restrictions the situation is quite clear. In its simplest form, resource constraints are upper bounds on variables representing the consumption of resources, and final product consumption constraints are lower bounds on variables representing the production of a product. Resource restrictions are as follows:

A i1 X 1 + ... + A ij X j + ... + A in X n Bi,

where A ij is the consumption of the i-th resource per unit X j, j = 1 ... n, and Bi is the total volume of the available resource.

CONDITIONS IMPOSED EXTERNALLY

DEFINITION OF THE TARGET FUNCTION

The model's objective function usually consists of the following components:

1) Cost of the product produced.

2) Capital investments in buildings and equipment.

3) Cost of resources.

4) Operating costs and equipment repair costs.

Classification of economic and mathematical models

An important stage in the study of the phenomena of objects of processes is their classification, which acts as a system of subordinate classes of objects, used as a means for establishing connections between these classes of objects. The classification is based on the essential characteristics of objects. Since there can be a lot of signs, the classifications performed can differ significantly from each other. Any classification must achieve its goals.

The choice of classification purpose determines the set of characteristics by which objects to be systematized will be classified. The purpose of our classification is to show that optimization problems, completely different in content, can be solved on a computer using several types of existing software.

Here are some examples of classification characteristics:

1 area of ​​use

3. Mathematical model class

The most common optimization problems arising in economics are linear programming problems. Their prevalence is explained by the following:

1) With their help, they solve problems of resource allocation, to which

a very large number of very different tasks are reduced

2) Reliable methods for solving them have been developed and implemented in the supplied software

3) A number of more complex problems are reduced to linear programming problems

Mathematical modeling in management and planning

One of the powerful tools available to people responsible for managing complex systems is modeling. A model is a representation of a real object, system or concept in some form different from the form of its actual real existence. Typically, a model serves as a tool to aid in explanation, understanding, or improvement. Analysis of mathematical models provides managers and other leaders with an effective tool that can be used to predict the behavior of systems and compare the results obtained. Modeling allows you to logically predict the consequences of alternative actions and shows quite confidently which of them should be preferred.

The enterprise has some types of resources, but the total supply of resources is limited. Therefore, an important task arises: choosing the optimal option that ensures achievement of the goal with minimal expenditure of resources. Thus, effective production management implies such an organization of the process in which not only the goal is achieved, but also the extreme (MIN, MAX) value of some efficiency criterion is obtained:

K = F(X1,X2,...,Xn) -> MIN(MAX)

Function K is a mathematical expression of the result of an action aimed at achieving a goal, and therefore it is called the target function.

The functioning of a complex production system is always determined by a large number of parameters. To obtain an optimal solution, some of these parameters must be turned to the maximum, and others to the minimum. The question arises: is there even a solution that best satisfies all the requirements at once? We can confidently answer - no. In practice, a solution in which any indicator has a maximum, as a rule, does not turn other indicators into either a maximum or a minimum. Therefore, expressions like: produce products of the highest quality at the lowest cost are simply a solemn phrase and are essentially incorrect. It would be correct to say: to obtain products of the highest quality at the same cost, or to reduce the cost of production without reducing its quality, although such expressions sound less beautiful, but they clearly define the goals. Choosing a goal and formulating a criterion for achieving it, that is, an objective function, represents the most difficult problem of measuring and comparing heterogeneous variables, some of which are in principle incommensurable with each other: for example, safety and cost, or quality and simplicity. But it is precisely such social, ethical and psychological concepts that often act as motivation factors in determining the goal and criterion of optimality. In real production management problems, it is necessary to take into account that some criteria are more important than others. Such criteria can be ranked, that is, their relative importance and priority can be established. In such conditions, the optimal solution must be considered to be one in which the criteria with the highest priority receive maximum values. The limiting case of this approach is the principle of identifying the main criterion. In this case, one criterion is taken as the main one, for example, the strength of steel, the calorie content of the product, etc. Based on this criterion, optimization is carried out; the rest are subject to only one condition: that they be no less than some specified values. It is impossible to carry out ordinary arithmetic operations between ranked parameters; it is only possible to establish their hierarchy of values ​​and a scale of priorities, which is a significant difference from modeling in the natural sciences.

When designing complex technical systems, when managing large-scale production or directing military operations, that is, in situations where it is necessary to make responsible decisions, practical experience is of great importance, making it possible to identify the most significant factors, cover the situation as a whole and choose the optimal path to achieve the goal. . Experience also helps to find similar cases in the past and, if possible, avoid erroneous actions. Experience means not only the decision maker’s own practice, but also other people’s experience, which is described in books, summarized in instructions, recommendations and other guidance materials. Naturally, when the solution has already been tested, that is, it is known which solution best satisfies the set goals, the problem of optimal control does not exist. However, in reality, situations are almost never exactly the same, so decisions and management always have to be made under conditions of incomplete information. In such cases, they try to obtain the missing information using guesswork, assumptions, the results of scientific research, and especially studying using models. A scientifically based control theory is in many ways a set of methods for replenishing missing information about how the control object will behave under the selected influence.

The desire to obtain as much information as possible about controlled objects and processes, including the features of their future behavior, can be satisfied by studying the properties of interest to us on models. A model provides a way to represent a real object, which makes it possible to easily and cost-effectively explore some of its properties. Only the model allows us to study not all properties at once, but only those of them that are most significant for a given consideration. Therefore, models allow you to form a simplified idea of ​​the system and obtain the desired results easier and faster than when studying the system itself. The model of the production system is first created in the mind of the employee performing management. Using this model, he mentally tries to imagine all the features of the system itself and the details of its behavior, to anticipate all the difficulties and provide for all the critical situations that may arise in various operating modes. He makes logical conclusions, carries out drawings, plans and calculations. The complexity of modern technical systems and production processes means that different types of models have to be used to study them.

The simplest are scale models in which natural values ​​of all sizes are multiplied by a constant value - the modeling scale. Large objects are represented in a reduced form, and small ones in an enlarged form.

In analogue models, the processes under study are not studied directly, but by analogous phenomena, that is, by processes that have a different physical nature, but which are described by the same mathematical relationships. For such modeling, analogies between mechanical, thermal, hydraulic, electrical and other phenomena are used. For example, oscillations of a weight on a spring are similar to fluctuations in current in an electrical circuit, and the movement of a pendulum is similar to fluctuations in voltage at the output of an alternating current generator. The most common method of scientific research is the use of mathematical modeling. A mathematical model describes the formal relationship between the values ​​of the parameters at the input of the modeled object or process and the output parameters. In mathematical modeling, one abstracts from the specific physical nature of the object and the processes occurring in it and considers only the transformation of input quantities into output ones. Analyzing mathematical models is easier and faster than experimentally determining the behavior of a real object in various operating modes. In addition, analysis of the mathematical model allows us to highlight the most essential properties of a given system, which should be paid special attention to when making a decision. An additional advantage is that with mathematical modeling it is not difficult to test the system under study under ideal conditions or, conversely, in extreme conditions, which for real objects or processes are costly or associated with risk.

Depending on what information the manager and his

employees preparing decisions, the decision-making conditions and the mathematical methods used to develop recommendations change.

The complexity of mathematical modeling under conditions of uncertainty depends on the nature of the unknown factors. Based on this criterion, problems are divided into two classes.

1) Stochastic problems, when unknown factors are random variables for which the laws of probability distribution and other statistical characteristics are known.

2) Uncertain problems, when unknown factors cannot be described by statistical methods.

Here is an example of a stochastic problem:

We decided to organize a cafe. We don’t know how many visitors will come to it per day. It is also unknown how long service will continue for each visitor. However, the characteristics of these random variables can be obtained statistically. An efficiency indicator that depends on random variables will also be a random variable.

In this case, as an indicator of efficiency, we take not the random variable itself, but its average value and choose such a solution when

at which this average value becomes a maximum or a minimum.

Conclusion.

Computer science plays an important role in modern economic science, which has led to the identification of a separate direction in the development of science - economic computer science. This new direction combines economics, mathematics and computer science, and helps economists solve problems of optimizing the activities of enterprises, make strategically important decisions on industrial development and manage the production process.

The developed software base is based on mathematical models of economic processes and provides a flexible and reliable mechanism for predicting the economic effect of management decisions. With the help of computers, analytical problems that cannot be solved by humans can be quickly solved.

Recently, the computer has become an integral part of the workplace of a manager and economist.

Bibliography.

1. Figurnov. PC for beginners. M.: VSh – 1995.

2. Oseiko N. Accounting using a PC. Third edition. K.: SoftArt, 1996.

3. Information systems in economics. M.: VSh – 1996.

4. Richard B. Chase, Nicholas J. Aquilano. Production And Operations Management: A Life Cycle Approach. Fifth Edition. Boston, MA: Irwin – 1989.

5. Ventzel E.S. Operations research. M: VSh – 1983

6. Minu Mathematical programming M: Radio and communications 1978

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Basic concepts of economic informatics

Plan

• 4. Information technology
• Conclusion

1. Basic concepts of economic informatics

Object, subject, methods and tasks of economic informatics

The intensive introduction of information technologies into economics has led to the emergence of one of the directions in computer science - economic informatics, which is an integrated applied discipline based on interdisciplinary connections between computer science, economics and mathematics.

The theoretical basis for the study of economic informatics is computer science. The word “informatics” (informatique) comes from the merger of two French words: information (information) and automatique (automation), introduced in France to define the field of activity involved in automated information processing.

There are many definitions of computer science. Computer science is the science of information, methods of collecting, storing, processing and presenting it using computer technology. Computer science is an applied discipline that studies the structure and general properties of scientific information, etc. Computer science consists of three interrelated components: computer science as a fundamental science, as an applied discipline and as a branch of production.

The main objects of computer science are:

information;

computers;

Information Systems.

General theoretical foundations of computer science:

information;

number systems;

coding;

algorithms.

Structure of modern computer science:

1. Theoretical computer science.

2. Computer technology.

3. Programming.

4. Information systems.

5. Artificial intelligence.

Economic informatics is the science of information systems used to prepare and make decisions in management, economics and business.

The object of economic informatics is information systems that provide solutions to business and organizational problems that arise in economic systems (economic objects). That is, the object of economic informatics is economic information systems, the ultimate goal of which is the effective management of the economic system.

An information system is a set of software and hardware, methods and people that provide collection, storage, processing and delivery of information to ensure preparation and decision-making. The main components of information systems used in economics include: hardware and software, business applications and information systems management. The purpose of information systems is to create a modern information infrastructure for company management.

The subject of economic informatics is technology, methods of automating information processes using economic data.

The task of economic informatics is to study the theoretical foundations of computer science and acquire skills in using applied systems for processing economic data and programming systems for personal computers and computer networks.

2. Data, information and knowledge

Basic concepts of data, information, knowledge

The basic concepts used in economic informatics include: data, information and knowledge. These concepts are often used interchangeably, but there are fundamental differences between these concepts.

The term data comes from the word data - fact, and information (informatio) means explanation, presentation, i.e. information or message.

Data is a collection of information recorded on a specific medium in a form suitable for permanent storage, transmission and processing. Transformation and processing of data allows you to obtain information.

Information is the result of data transformation and analysis. The difference between information and data is that data is fixed information about events and phenomena that is stored on certain media, and information appears as a result of data processing when solving specific problems. For example, various data are stored in databases, and upon a certain request, the database management system provides the required information.

There are other definitions of information, for example, information is information about objects and phenomena of the environment, their parameters, properties and state, which reduce the degree of uncertainty and incomplete knowledge about them.

Knowledge is processed information recorded and verified by practice, which has been used and can be reused for decision making.

economic informatics software information

Knowledge is a type of information that is stored in a knowledge base and reflects the knowledge of a specialist in a specific subject area. Knowledge is intellectual capital.

Formal knowledge can be in the form of documents (standards, regulations) regulating decision-making or textbooks, instructions describing how to solve problems. Informal knowledge is the knowledge and experience of specialists in a certain subject area.

It should be noted that there are no universal definitions of these concepts (data, information, knowledge), they are interpreted differently. Decisions are made based on the information received and existing knowledge.

Decision making is the selection of the best, in some sense, solution option from a set of acceptable ones based on available information.

To solve the problem, fixed data is processed on the basis of existing knowledge, then the information received is analyzed using existing knowledge. Based on the analysis, all feasible solutions are proposed, and as a result of the choice, one decision that is best in some sense is made. The results of the solution add to knowledge.

Depending on the scope of use, information can be different: scientific, technical, management, economic, etc. For economic informatics, economic information is of interest.

3. Economic information and information technology

Economic information is a transformed and processed set of information that reflects the state and course of economic processes. Economic information circulates in the economic system and accompanies the processes of production, distribution, exchange and consumption of material goods and services. Economic information should be considered as one of the types of management information.

Economic information can be:

manager (in the form of direct orders, planned tasks, etc.);

informing (in reporting indicators, performs a feedback function in the economic system).

Information can be considered as a resource similar to material, labor and monetary resources. Information resources are a set of accumulated information recorded on tangible media in any form that ensures its transmission in time and space to solve scientific, production, management and other problems.

4. Information technology

Collection, storage, processing, transmission of information in numerical form is carried out using information technology. The peculiarity of information technologies is that in them both the subject and product of labor is information, and the tools of labor are computers and communications. The main goal of information technology is the production of information necessary for the user as a result of targeted actions for its processing.

It is known that information technology is a set of methods, production and software-technological tools combined into a technological chain that ensures the collection, storage, processing, output and dissemination of information.

From the point of view of information technology, information requires a material carrier as a source of information, a transmitter, a communication channel, a receiver and a recipient of information.

A message from a source to a recipient is transmitted through communication channels or through a medium.

Information is a form of communication between managed and control objects in any control system. In accordance with the general theory of control, the control process can be represented as the interaction of two systems - the control and the controlled.

The enterprise management system operates on the basis of information about the state of the facility, its inputs (material, labor, financial resources) and outputs (finished products, economic and financial results) in accordance with the goal (to ensure the production of the necessary products).

Management is carried out by submitting management influence (production plan) taking into account feedback - the current state of the managed system (production) and the external environment - the market, higher management bodies.

The purpose of the control system is to form such influences on the controlled system that would induce the latter to accept the state determined by the control goal.

In relation to an industrial enterprise, with some degree of convention, we can assume that the goal of management is the implementation of the production program within the framework of technical and economic restrictions; control influences are work plans for the department, feedback data on the progress of production: production and movement of the product, the condition of the equipment, stocks in the warehouse, etc.

Obviously, plans and feedback content are nothing more than information. Therefore, the processes of forming control actions are precisely the processes of transforming economic information. The implementation of these processes constitutes the main content of management services, including economic ones. The following requirements are imposed on economic information: accuracy, reliability, efficiency.

The accuracy of the information ensures its unambiguous perception by all consumers. Reliability determines the permissible level of distortion of both incoming and resulting information, at which the efficiency of the system’s functioning is maintained. Efficiency reflects the relevance of information for the necessary calculations and decision-making in changing conditions.

Computer Science and Information Systems

The word "system" comes from the Greek systema, which means a whole made up of parts or many elements. A system is a collection of interconnected elements that function to achieve a specific goal.

Main characteristics of systems: purpose, inputs, outputs, feedback and external environment. The systems differ significantly from each other both in composition and in their main goals. Systems include computer hardware and software, telecommunications, life support systems, education systems, etc.

Economic systems include: industrial enterprises, trade organizations, commercial banks, government agencies, etc.

So, the object of economic informatics is economic information systems, the ultimate goal of which is the effective management of the economic system. Thus, the main purpose of the information system is to create a modern infrastructure for managing an enterprise, organization, or institution.

The variety of problems solved with the help of information systems has led to the emergence of many different types of systems, differing in the principles of construction and the rules of information processing embedded in them. Information systems can be classified according to a number of different characteristics.

Classification of information systems based on the structure of tasks.

There are three types of tasks for which information systems are created:

structured (formalized);

unstructured (unformalizable);

partially structured.

A structured (formalized) task is a task where all its elements and the relationships between them are known.

An unstructured (unformalizable) task is a task in which it is impossible to identify elements and establish connections between them.

Information systems for semi-structured tasks. Information systems used to solve semi-structured problems are divided into two types: those that create management reports and those that are primarily focused on data processing; developing possible solution alternatives.

Classification of the information systems market by system scale:

Local systems (1C, BEST, Info - Accountant, etc.)

Small integrated systems (Skala, Parus, Galaktika and others)

Medium integrated systems (MFG-PRO and others)

Large integrated systems (SAP/R3 others)

Classification of systems, which is based on the classification of business problems.

Principles of classification of management information systems:

1. Level of strategic management (3 - 5 years)

2. Level of medium-term management (1 - 1.5 years)

3. Level of operational management (month - quarter - half year)

4. Level of operational management (day - week)

5. Real-time control layer

There are other types of classification of information systems. Special programs have been developed abroad

Standards for enterprise management information systems: MRP, MRP-II, ERP, ERPII systems.

MRP is a system for planning the requirements for material resources (provides the required amount of remaining materials in the warehouse).

RP-II - designed for planning production resources, i.e. resources used to produce products.

ERP - designed for planning and managing material, production and human resources. SAP R/3 is an ERP (Enterprise Resource Planning) system for enterprise resource management or SAP ER. ERP II - designed for managing resources and external relations of enterprises.

Information systems used to plan and manage various resources are called integrated management systems or enterprise information systems.

The main components of information systems used in economics include: hardware and software, business applications and information systems management.

1. Hardware and software of information systems:

technical means of information processing (computers and peripheral devices);

system and service software (operating systems and utilities)

Office application software (MS Office);

computer networks (communications equipment, network software and network applications);

databases and data banks.

2. Business applications (application programs):

local information systems (1C: Accounting, Infin, Parus, etc.);

small information systems (1C: Enterprise, Parus, Galaktika, etc.);

medium information systems (PEOPLE SOFT, BAAN, SCALA, etc.);

integrated management systems (ERP).

3. Information systems management is intended to manage and support enterprise information processes (personnel management, development, quality, safety, operational management, etc.)

Thus, information systems that are considered in economic informatics consist of three main components:

information technology (computer hardware and software, telecommunications, data);

functional subsystems (production, accounting and finance, sales, marketing, personnel) and business applications (application programs for solving business problems);

information systems management (personnel, users, IS development, finance)

Currently, the most appropriate way to build an economic information system is to use ready-made solutions, which are implemented in the form of ready-made application programs.

Conclusion

Computer science plays an important role in modern economic science, which has led to the identification of a separate direction in the development of science - economic computer science. This new direction combines economics, mathematics and computer science, and helps economists solve problems of optimizing the activities of enterprises, make strategically important decisions on industrial development and manage the production process.

The developed software base is based on mathematical models of economic processes and provides a flexible and reliable mechanism for predicting the economic effect of management decisions. With the help of computers, analytical problems that cannot be solved by humans can be quickly solved. Recently, the computer has become an integral part of the workplace of managers and economists.

Bibliography

1. Information systems in economics. Karminsky A.M., Chernikov B.V. Moscow: Finance and Statistics 2006. 320 p.

2. Economic informatics: Textbook for universities. Konyukhovsky P.V. St. Petersburg: Peter 2001. 560 p.

3. Economic informatics. Kosareva V.P., Eremina L.V. - Moscow: Finance and Statistics, 2002, 592 p.

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Economic informatics is the science of information systems used to prepare and make decisions in management, economics and business. The object of economic informatics is information systems that provide solutions to business and organizational problems that arise in economic systems (economic objects). That is, the object of economic informatics is economic information systems, the ultimate goal of which is the effective management of the economic system.

1. EI is specific in its presentation form. It is certainly reflected on tangible media in the form of primary and summary documents; to increase reliability, the transfer and processing is carried out only of legally formalized information, that is, if there is a signature on traditional or electronic documents (requires special means and organizational measures).

2. EI is volumetric. High-quality management of economic processes is impossible without detailed information about them. Improving management and increasing production volumes in the material and non-material spheres are accompanied by an increase in the accompanying information flows (requires increasing productivity of processing tools and communication channels).

Z. EI is cyclical. Most production and economic processes are characterized by repeatability of their constituent stages and information reflecting these processes (once created information processing programs can be reused and replicated).

4. EI reflects the results of production and economic activities using a system of natural and cost indicators. In this case, quantitative quantities and digital values ​​are used (they are convenient to process).

5. EI is specific in terms of processing methods. The processing process is dominated by arithmetic and, first of all, logical (for example, sorting or selection) operations, and the results are presented in the form of text documents, tables, charts and graphs (making it possible to limit oneself to a certain range of problem-oriented software tools).

Economic information is a transformed and processed set of information reflecting the state and course of economic processes. Economic information circulates in the economic system and accompanies the processes of production, distribution, exchange and consumption of material goods and services. Economic information should be considered as one of the types of management information. Economic information can be:

· manager (in the form of direct orders, planned targets, etc.);

· informing (in reporting indicators, performs a feedback function in the economic system).

Information can be considered as a resource similar to material, labor and monetary resources. Information resources are a set of accumulated information recorded on tangible media in any form that ensures its transmission in time and space to solve scientific, production, management and other problems.

8 .Information product. Information resources.

Information product- documented information prepared in accordance with the needs of users and presented in the form of a product. Information products are software products, databases and data banks and other information. The result of information activity is an information product, which appears on the market in the form of information goods and services.

Let us note the main features of the information product, which fundamentally distinguish information from other products.

Firstly, information does not disappear when consumed, but can be used repeatedly. An information product retains the information it contains, no matter how many times it is used.

Secondly, an information product undergoes a kind of “obsolescence” over time. Although information does not wear out when used, it can lose its value as the knowledge it provides ceases to be relevant.

Thirdly, different consumers of information goods and services are comfortable with different ways of providing information, because consuming an information product requires effort. This is the property of addressing information.

Fourthly, the production of information, unlike the production of material goods, requires significant costs compared to the costs of replication. Copying a particular information product is usually much cheaper than producing it. This property of an information product - the difficulty of production and the relative ease of replication - creates, in particular, many problems in connection with the determination of property rights within the scope of information activity.

Informational resources- this is accumulated information about the surrounding reality, recorded on material media that ensures the transfer of information in time and space between consumers to solve specific problems.

It should be noted that an information resource is all accumulated information, including:

· unreliable information (“defectological”);

· information that has lost its relevance;

· information presented by false statements and ineffective approaches;

· incomparable data accumulated using non-standard methods;

· information that has lost its specificity as a result of subjective interpretations;

· deliberate “disinformation”.

Depending on the information media, information resources are divided into three main classes:

· personnel who have knowledge and qualifications;

· documents of all types and their collections on all types of media;

· collections of objects of inanimate and living nature (industrial designs, formulations and technologies, standard samples, etc.);

Among the features of information resources are:

· inexhaustibility - as society develops and the consumption of knowledge grows, its reserves do not decrease, but grow;

· intangibility - which ensures the relative ease of their reproduction, transmission, and distribution compared to other types of resources. Information resources are individual documents and separate sets of documents in information systems data warehouses: libraries, archives, funds, databases, and other types of data warehouses.

· Classification of information resources:

· State (national) information resources. State information resources are information resources received and paid for from the federal budget. Contents of state information resources (examples): activities of state authorities, legal information, stock exchange and financial information, commercial information.

· Information resources of enterprises. Information resources of enterprises are information resources created or accumulated at enterprises and organizations. Contents of enterprise information resources (examples): information support for economic activities, planning and operational management of enterprise activities, business plans, foreign economic activity.

· Personal information resources. Personal information resources are information resources created and managed by a person and containing data related to his personal activities