The problem of formalization. Institutional basis of lobbying activities in Russia

Today, one can very often come across the incomprehensible term “formalization”, and in a variety of fields of science and technology. For those who want their knowledge, it is advisable to understand what formalization is. The article will discuss the essence of this term and the practical application of the process.

What is formalization from a scientific point of view in the general sense?

Let's touch on the scientific aspect a little. We will start from the fact that the word formalization comes from the word “formality”, that is, it is a conditional and sometimes even abstract concept that allows us to explain the nature of a non-existent object or phenomenon and predict its properties in a certain environment under given initial conditions.

The linguistics of any modern language is completely at odds with the expression or nature of thought. Thus, logic itself is forced to use some abstract concepts to describe this or that phenomenon. This is how the relative concept of the formality of what is happening appears.

As it is not difficult to guess, the essence of formalization comes down to describing or predetermining certain properties of an object or process (even one that does not exist at the moment) and predicting its use if it appears in the real world. But this is a general idea. The very concept of formalization is much broader. First, let's focus on computer technology and consider how this concept is used in the world of electronics.

Computer formalization

If we touch on the topic of computers, a formalization method of this type is, rather, the processing of initially specified conditions, which make it possible to determine the further behavior of an object or process with a fairly high degree of accuracy.

Almost all weather services operate on this principle. Having a computer model of a cyclone, you can predict its cycle and power over land or over water.

Remember the film “The Day After Tomorrow”, in which the scientist predicted global warming based on exactly this technique. He developed a computer model that made it possible to predict future events with a certain degree of probability.

These examples clearly explain what formalization is.

Principles of modeling objects and processes

The main methods of formalization are forecasting and modeling. Such technologies are used exclusively to obtain final data about objects or processes that are unknown, but they can be assumed and calculated with high accuracy.

If you look at the types of formalization, almost all of them come down only to logical conclusions and calculations. It will not be difficult for the reader to draw a parallel between computer modeling, proof of theorems, etc. based on axioms and postulates.

Look, the same can also be interpreted as a method of formalization, because in practice it is not possible to verify the proof. In particular, this concerns the constant of light propagation, the slowing down of time at the threshold of its achievement, the increase in the gravitational mass of an object and the curvature of space. As they say, you can’t feel it with your hands and you can’t see it with your eyes.

Once upon a time these were only bold conclusions of a scientist based on simple experiments. Today all this is confirmed by official science based on the same computer modeling.

Stages of formalization

If we consider computer systems, the first stage of formalization is a description of the process. But the tools of ordinary language (letters, words, phrases, sentences) are not used here. You can create a specific one only using a certain algorithm based on the selected programming language, but only after setting a general problem.

In other words, when modeling the behavior of an object or process, the essence of what is happening must be described in purely mathematical symbols, using a mathematical algorithm.

The result of formalization is to obtain an analysis of an actual predictable event that will follow after the technology under study is applied in practice or a certain natural process enters the stage of real manifestation.

What follows is a conceptualization of the task at hand. There are two options here: in the first case, this is the definition of an approach in the form of using attributes and features; the second option involves the use of cognitive analysis, not to mention setting the problem, collecting the initial data used, conditions, etc.

After and initial conditions, existing relationships between objects and processes are studied, as well as so-called semantic relationships, which imply the use of local representation techniques.

This is followed by processing of the initial data based on the selected algorithm, after which the result is displayed indicating the percentage of error. As a rule, it does not exceed 5%, and in most cases the probability result reaches 99%. Any person or machine still leaves a “margin of safety” for it is impossible to take absolutely everything into account.

Why is all this needed?

If you look at it, such principles allow you to analyze the behavior of objects and processes. In other words, it is possible to predict how a particular process will develop.

Now it is clear what formalization is. Let's look at a simple example.

Application of formalization in practice, simple examples

Let's say some specialist has developed a new aircraft design. Taking into account the high cost of the project, building a model of the original size without a preliminary forecast of its behavior in the air is a completely impractical task. Moreover, conducting tests in the same wind tunnel on an aircraft the size of Boeing is completely unrealistic.

Formalization allows, given predetermined characteristics of the future aircraft (air resistance, side wind, height and parameters of the wind tunnel itself and other characteristics), to simulate a flight without building a model of the aircraft.

Another example is the testing of new cars carried out by automobile concerns. The main method of formalization in this case is that first they all undergo a virtual test, and after receiving positive results, prototypes are put into production for testing in real conditions.

Main results

The result of mathematical modeling in many ways (if not one hundred percent, then with a probability of up to 95%) can become a powerful argument in favor of the production of modern technology, help predict the weather, and even predict public behavior as a reaction to events in the world.

Yes Yes! in the world also obeys its own laws. It is enough to influence it in the right direction. Today, many programs have already been created that make it possible to predict the reaction of society to a particular event. And these are not all examples of formalization. If you dig deeper, we face this every day.

One of the most striking examples of formalization can be called the detection of elementary particles during collisions in the Large Hadron Collider. But previously it was believed that the existence of this particle was pure theory, and absolutely not provable by real experiments.

Conclusion

As we can see, the concept of formalization, despite the scientific complexity of the essence of the process, is easy to understand using examples. In most cases, it comes down to the use of certain logical chains that predetermine the final result.

Formalization of the concept of a problem

Formalization of the concept of a problem from the position of system analysis and system concept.

The problem is called
a) a situation characterizing the difference between the necessary (desired) output and the existing output;
b) the difference between the existing and desired system, expressed in either preventing (reducing) output or increasing output.

Existing exit created by the existing system.
Required output This is a way out, the absence of which poses a threat to the existence or development of the system.
Desired exit created by the desired system.

Existing system- this is the condition of the problem, what determines one side of the existence of the problem.
Desired system- a requirement that solves the problem.

Solution, a system that fills the gap between the existing and the desired, a design object. Problem solving is an activity that maintains or improves the performance of a system. The solution process is an iteration of operations to identify conditions, goals, and possibilities for solving a problem.

Identification consists of:
- quality identification- definition of system objects (input, output, process, feedback, limitation), properties, connections.
- quantitative identification- determination of quantitative relationships of known structures, elements, goals, capabilities.

There is no point in having powerful solution generation methods if the problem identification function is not fulfilled.

The selection of alternatives is made on the basis of comparison of price, time, efficiency, risk, taking into account the relationships between the maximum values ​​of increments of these quantities (marginal relationships).

Problem solving scheme

The reason for the unsatisfactory state of affairs and the need for a solution are caused by:
- the emergence of a new problem,
- the emergence of a new opportunity.

New problem

Solving a problem in a situation new problem consists of:
in identifying the problem,
in assessing the urgency of the problem,
in defining goals and coercive connections,
in defining criteria,
in revealing the structure of the existing system,
in identifying defective elements of the existing system that limit the achievement of a given output,
in assessing the weight of the influence of elements on the system outputs determined by the criteria,
in defining a structure for constructing a set of alternatives,
in constructing a set of alternatives,
in evaluating alternatives,
in choosing alternatives for implementation,
in agreement with the solution found,
in implementing the solution,
in assessing the results of the implementation of the decision.

New opportunity

Implementation new opportunity- this is a more complex case.
The use of an opportunity (the actualization of an opportunity) depends on the presence of an actual problem that needs such an opportunity to be resolved.
Using an opportunity outside of the problem entails, at a minimum, a loss of resources, turns into an end in itself, and leads to a deepening of problems.
To determine whether a problem needs a new capability to be solved, you should evaluate:
a) is there an alternative that includes a new possibility,
b) whether the alternative with the new feature is the best one.
One of the approaches to updating an opportunity outside of the administrative (or, according to the scientist, problem-oriented approach) is the implementation of the opportunity on the principles of self-financing or self-sufficiency.

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Now figure out what a SWOT analysis looks like in this vein.
Note that threats correspond to solving a new problem, but the opportunities are already clear.
And strengths and weaknesses generally answer the question of choice and selection of alternatives.
System analysis clearly shows the weaknesses of SWOT analysis, its lack of focus, the lack of safety nets and verification procedures that eliminate or reduce subjectivity and unprofessionalism.
But the biggest “fun” of SWOT analysis is that it manages to bring together not just one, but a whole bunch of problems, and even those that are heterogeneous in their approaches and solution procedures.
It’s no wonder that SWOT analysis has such an impressive number of failures and punctures...

The decision to improve an organization must grow out of its problems and match them in scale and complexity.
Existing and desired organizational systems require a more delicate assessment, since they contain self-regulating links that have will, resources and interests arising from their place and role in the design of the system.

Therefore, the first step is to evaluate the design of the organizational system.
The assessment of the design of organizational systems is based on the following criteria:

Measurability. The ability of a system to measure its characteristics.

Reliability. The output of the system appears quite systematically.

Efficiency. The ability of the system to solve the problem, the degree to which the result is actually achieved.
If a system is not measurable, it is impossible to determine its effectiveness.

Optimality. The problem is solved in the “shortest” way in the sense of spending resources, time being one of the resources. Note that if the system is immeasurable and ineffective (that is, unable to solve the problem), then assessing optimality is meaningless.

Stability. Strengthened property of reliability in application to the constancy of the effects of efficiency and optimality, resistance to destructive factors and threats.
In relation to organizational systems, the task of the top management of the organization is not to develop decisions, but to design the process of developing a decision and monitoring its action. If a manager is able to offer good solutions at the level of organizational processes, it is not a fact that he can be equally successful in management ( otherwise, the design of the crane should have been entrusted to a weightlifter - he lifts the barbells steeply).

Over the past decade, cluster policy has become one of the strategic directions of government policy to increase both national and regional competitiveness in developed and developing countries of the world. However, the governments of a number of states, when developing programs for the creation and development of clusters, often have no idea about the essence of the concept of “cluster,” not to mention its structure and functioning. Confusion of definitions and subsequent “branding” of the cluster leads to unjustified government expenses and can also mislead potential private investors. Gradually, the term “cluster” is losing its real meaning, and many regional governments are beginning to use it to attract foreign investment, change the image of the region and other goals that are understandable only to them. For example, in 1994, M. Porter’s group (the founder of the cluster approach) identified 33 clusters in Portugal, but it later turned out that the initially identified objects, called clusters, were completely uncompetitive, since they represented a cluster of unrelated enterprises. As a result, most of the financial resources and foreign investments aimed at developing Portuguese clusters were wasted. Therefore, in our opinion, it is necessary to clearly define this object of research, identifying its essential characteristics and structural components.

Before economics, the term “cluster” was widely used in most natural sciences; for example, in biology, a cluster is understood as an accumulation of mutant genes. From English “cluster” can be translated as (1) brush, bunch; (2) accumulation, concentration; (3) group. Back in the 1970s. the term “cluster” was actively used in the works of domestic economic geographers A. Gorkin, L. Smirnyagin, as well as by foreign scientists K. Fredriksson and L. Lindmark when denoting the concentration of enterprises in space. The author of the cluster approach, M. Porter, also made a significant contribution to the definition of this object. It should be noted that economists dealt with the problems of industrial concentration at the turn of the 19th and 20th centuries. Such early works include studies by A. Marshall, A. Loesch, W. Isard.

In general, the term “cluster”, which allows for several translations from English, is considered not very successful to denote a form of organization of production, but it is also very profitable and popular in the marketing environment for attracting attention to this concept (which is necessary for its developers in first of all). In the last decade of the 20th century. New directions in the field of consulting have also emerged - cluster consulting and cluster management for organizing cluster initiatives.

Today, “cluster” is interpreted by scientists in two planes. On the one hand, when defining this concept, economists focus on the geographic proximity of its structural elements. Another group of researchers emphasizes other characteristics of the cluster, giving the geographic component secondary importance.

But the lack of a clear definition and the difficulty in marking boundaries continues to be a significant disadvantage of the cluster, but this does not stop the developers of the concept of cluster policy. They, on the contrary, encourage wider dissemination of this term, taking for a cluster something that in fact is not a cluster in its original meaning. Objects completely different in their genesis, which previously had their own designations, began to be called clusters as a tribute to fashion (for example, Italian industrial districts). In table Table 1 shows the most frequently used definitions of a cluster in modern economic scientific circulation.

Table 1 – Basic definitions of a cluster