General scientific methods analysis and synthesis example. Analysis and synthesis

Suppose we need to cognize, understand some system that is complex for us, that is, transfer it from complex and obscure to simple and understandable. This means that we should build a model of this system containing the information we need. Depending on what we need to know, explain how the system is structured or how it

interacts with the environment, there are two methods of cognition: 1) analytical; 2) synthetic.

The analysis procedure consists of sequentially performing the following three operations:

1) divide a complex whole into smaller parts, presumably simpler;

2) give a clear explanation of the received fragments;

3) combine the explanation of the parts into an explanation of the whole.

If some part of the system remains unclear, the decomposition operation is repeated and we again attempt to explain new, even smaller fragments. In the diagram, the explained objects are shaded. (In some cases, the analysis of a separate branch may be “delayed” without ever reaching an explainable fragment. This is a sign of the absence of knowledge that can make the fragment elementary. Positive knowledge in this case is the discovery of what kind of knowledge we lack.)

The acquired knowledge is presented in the form of models of our system. The first product of the analysis is, as can be seen from the diagram, a list of system elements, i.e. system composition model. The most serious pitfall of analysis is the danger of breaking the connections between parts during decomposition, thereby destroying the emergent properties of the system. So a correct, qualitative analysis should distinguish between parts, and not split into parts during decomposition. Otherwise, it will be impossible to perform the last operation of analysis: the explanation of the whole is impossible only through the explanation of the parts. To explain the whole means to establish its emergent properties, and for this it is necessary to establish (or restore) connections between the parts. Thus, the second product of the analysis is structure model systems. The third product of the analysis is black box model for each element of the system.

So, as a result of the analysis, we obtain information about the structure and operation of the system. All received information is “packaged” in the form of all three types of models: composition, structure, black box.

The analytical method has given remarkable results in human knowledge of the world. The entire structure of our knowledge is hierarchical: the single world is divided into separate areas chosen as the subject of research by different sciences: physics,

chemistry, history, etc. Each science also has its own analytical organization. In each field of knowledge, the matter comes down to the elements from which all the objects of its study are formed: elementary particles in physics, molecules in chemistry, phonemes in sound

and symbols in writing, cells in biology, notes in music, etc. The successes of the analytical method are so significant that there is even the impression that this is the only scientific method (often in speech the words “study” and “analyze” are used as synonyms).

However, there are questions that analysis cannot answer in principle, since the answer does not lie in the internal structure of the system. Try to find out through any (chemical, physical, artistic) analysis what the power and meaning of a banknote is. You can study human anatomy thoroughly, but you cannot explain why nature created two sexes. You can study the structure of the clock in detail, but this will not answer why they are needed. Studying the structure of a car will not answer why driving on the left is common in England.

Answers to questions of this kind are provided by synthesis.

The synthetic method consists of sequentially performing three operations:

1) selection larger system(metasystem), of which the system we are interested in is included as a part;

2) consideration of the composition and structure of the metasystem (its analysis);

3) an explanation of the role played by our system in the metasystem, through its connections with other subsystems of the metasystem (Fig. 3.4).

The final product of the synthesis is knowledge of the connections of our system with other parts of the metasystem, i.e. black box model. But in order to build it, we had to create models along the way composition and structure metasystems as by-products. And again we see that all the knowledge we have acquired is “packed” into three known forms models: black box, composition and structure. It is clear that the quality of the synthesis directly depends on the quality of the metasystem model, which should be taken special care of.

Analysis and synthesis are not opposite, but complement each other. Moreover, in analysis there is a synthetic component, and in synthesis there is an analysis of the metasystem. Which of them or in what sequence to apply them in a particular case is up to the researcher to decide.

To study systems and use this knowledge to create and manage systems, you need systems thinking, which consists in a combination of analytical and synthetic ways of thinking. The essence analysis consists of dividing the whole into parts, presenting the complex as a collection of simpler components. But in order to understand the whole, the complex, the reverse process is also necessary - synthesis . The need to combine these types of cognition follows from the emergence property of systems: the integrity of the system is violated during analysis; when the system is dismembered, not only the essential properties of the system itself are lost, but also the properties of its parts that are separated from it. The result of the analysis is only the discovery of the composition of the components, knowledge of how the system works, but not an understanding of why and why it does this. Synthetic thinking explains the behavior of a system, why the system works the way it does. In this case, the system must be considered as part of a larger whole.

Analysis and synthesis complement each other. So, during synthesis organizational structure it is necessary to first analyze the activities of the organization being created, identify individual processes (functions), compare organizational units with them, and then combine them into a separate whole, i.e. carry out synthesis. When choosing a method of functioning of an organization, the opposite often occurs: first, a synthetic approach is used - the activities of the organization as a whole are considered; a common goal and method of operation are selected, and then the selected method is disaggregated into individual functions.

The main content of the “Systems Analysis” discipline is complex decision-making problems, in the study of which informal procedures for representing common sense and ways of describing situations play no less a role than the formal mathematical apparatus. System analysis is a synthetic discipline. Three main directions can be distinguished in it. These three directions correspond to three stages that are always present in the study of complex systems:

1) building a model of the object under study;

2) statement of the research problem;

3) solution of the given mathematical problem.

Knowledge of systems and the use of this knowledge to create and manage systems is carried out through modeling.

The ultimate goal system analysis is the resolution of a problematic situation that has arisen in front of the object of the systemic research being carried out (usually this is a specific organization, team, enterprise, separate region, social structure, etc.). System analysis deals with the study of a problem situation, identifying its causes, developing options for eliminating it, making decisions and organizing the further functioning of the system to resolve the problem situation. Initial stage Any system research is the study of the object of the system analysis being carried out with its subsequent formalization. At this stage, problems arise that fundamentally distinguish the methodology of systems research from the methodology of other disciplines, namely, in systems analysis a dual problem is solved. On the one hand, it is necessary to formalize the object of systemic research, on the other hand, the process of studying the system, the process of formulating and solving the problem, is subject to formalization.

Let's give an example from the theory of system design. Modern theory of design of complex systems can be considered as one of the parts of systems research. According to it, the problem of designing complex systems has two aspects. Firstly, it is required to carry out a formalized description of the design object. Moreover, at this stage, the problems of a formalized description of both the static component of the system (mainly its structural organization is subject to formalization) and its behavior in time (dynamic aspects that reflect its functioning) are solved. Secondly, it is necessary to formalize the design process. Components of the design process are methods for forming various design solutions, methods of their engineering analysis and methods of decision-making for choosing the best options for implementing the system.

We will try to outline the basic procedures of the algorithm for conducting system analysis, which are a generalization of the sequence of stages of such analysis, formulated by a number of authors, and reflect its general principles. We list the main procedures for system analysis:

– study of the structure of the system, analysis of its components, identification of relationships between individual elements;

– collection of data on the functioning of the system, research of information flows, observations and experiments on the analyzed system;

– building models;

– checking the adequacy of models, uncertainty and sensitivity analysis;

– research of resource opportunities;

– defining the goals of system analysis;

– formation of criteria;

– generation of alternatives;

– implementation of choice and decision making;

– implementation of the analysis results.

Model concept

Replacing one object with another in order to obtain information about the most important properties the original object can be called using the model object modeling, i.e. modeling is the representation of an object by a model to obtain information about the object by conducting an experiment with its model.

From a philosophical point of view, modeling should be considered as an effective means of understanding nature. In this case, the modeling process presupposes the presence of an object of study, a researcher-experimenter and a model.

IN automated systems information processing and management, the object of modeling can be production and technological processes for obtaining final products; processes of movement of documents, information flows during the implementation of institutional activities of the organization; complex functioning processes technical means; processes of organization and functioning of information support of automated control systems; functioning processes software ACS.

The advantages of modeling are that it becomes possible to use relatively simple means to study the properties of the system, change its parameters, and introduce target and resource characteristics of the external environment. Typically, modeling is used at the following stages:

1) research of the system before it is designed, in order to determine its main characteristics and rules for the interaction of elements with each other and with external environment;

2) designing a system for analysis and synthesis various types structures and choice the best option implementation taking into account the formulated optimality criteria and restrictions;

3) operation of the system to obtain optimal modes functioning and projected assessments of its development.

In this case, the same system can be described various types models. For example, the transport network of a certain area can be modeled electrical diagram, hydraulic system, mathematical model using graph theory apparatus.

The following types of models are widely used to study systems: physical (geometric similarity, electrical, mechanical, etc.) and symbolic (substantive and mathematical). A mathematical model is understood as a set of mathematical expressions that describe the behavior (structure) of a system and the conditions (disturbances, restrictions) under which it operates. In turn, mathematical models, depending on the mathematical apparatus used, are divided, for example, into:

· static and dynamic;

· deterministic and probabilistic;

· discrete and continuous;

· analytical and numerical.

Static models describe an object at any point in time, while dynamic models reflect the behavior of an object over time. Deterministic models describe processes in which random factors are absent (not taken into account), while probabilistic models reflect random processes - events. Discrete models characterize processes described by discrete variables, continuous models - by continuous ones. Analytical models describe the process in the form of certain functional relationships and/or logical conditions. Numerical models reflect the elementary stages of calculations and the sequence of their implementation. If natural language (the language of communication between people) is used to describe a system, then such a description is called a content model. Examples of meaningful models are: verbal statements of problems, programs and plans for the development of systems, trees of organizational goals, etc. Content models have independent value in solving problems of research and management of systems, and are also used as a preliminary step in the development of mathematical models. Therefore, the quality of a mathematical model depends on the quality of the corresponding mathematical model.

Natural language (the language of communication between people), diagrams, tables, flowcharts, and graphs are used as linguistic means for describing meaningful (verbal) models. Complex systems are called complex because they are difficult to formalize. For them, it is advisable to use content models. Content models are irreplaceable in the early stages of designing complex systems, when the concept of the system is being formed. Systems analysis methods using decomposition approach, allow us to identify an ordered set of subsystems, elements, system properties and their connections. An integrated content model of the system allows you to present the overall picture, create a generalized description in which the main entities are emphasized and the details are hidden. The main thing in such a model is brevity and clarity. Such a model can serve as the basis for constructing more detailed models that describe individual aspects and subsystems. Thus, a meaningful model can serve as a framework for building other models, including mathematical ones. It also serves to structure information about an object.

The multiplicity of models of one object is due, in particular, to the fact that for different purposes it is required to build (use) different models. One of the grounds for classifying models may be the correlation of model types with types of goals. For example, models can be divided into cognitive and pragmatic.

Cognitive models are a form of organization and representation of knowledge, a means of connecting new knowledge with existing knowledge. Therefore, when a discrepancy is detected between the model and reality, the task arises of eliminating this discrepancy by changing the model by bringing the model closer to reality.

Pragmatic models are a means of management, a means of organizing practical actions, a way of presenting exemplary right actions or their result. Therefore, when a discrepancy is detected between the model and reality, the task arises of eliminating this discrepancy by changing reality so as to bring it closer to the model.

Thus, pragmatic models are normative in nature, playing the role of a standard, a model, to which both the activity itself and its result are “adapted”. Examples of pragmatic models include plans, action programs, charters of organizations, codes of laws, algorithms, working drawings and templates, selection parameters, technological tolerances, examination requirements, etc.

There are physical and abstract models.

Physical models are formed from a set of material objects. To construct them, various physical properties objects, and the nature of the material elements used in the model is not necessarily the same as in the object under study. An example of a physical model is a layout.

Information (abstract) model is a description of the research object in any language. The abstractness of the model is manifested in the fact that its components are concepts rather than physical elements (for example, verbal descriptions, drawings, diagrams, graphs, tables, algorithms or programs, mathematical descriptions).

Information models describe the behavior of the original object, but do not copy it. An information model is purposefully selected information about an object, which reflects the most significant properties of this object for the researcher. Among the information (abstract) models there are: – descriptive, visual and mixed; – epistemological, infological, cybernetic, sensory (sensual), conceptual, mathematical.

Epistemological models aimed at studying the objective laws of nature (for example, models solar system, biosphere, world ocean, catastrophic natural phenomena).

Infological model (narrow interpretation) – a parametric representation of the information circulation process, subject to automated processing.

Sensual models– models of some feelings, emotions, or models that influence a person’s feelings (for example, music, painting, poetry).

Conceptual model is an abstract model that identifies cause-and-effect relationships inherent in the object under study and essential within the framework of a particular study. The main purpose of the conceptual model is to identify a set of cause-and-effect relationships, the consideration of which is necessary to obtain the required results. The same object can be represented by different conceptual models, which are built depending on the purpose of the study. Thus, one conceptual model may reflect the temporal aspects of the system’s functioning, while another may reflect the impact of failures on the system’s performance.

Mathematical model– an abstract model presented in the language of mathematical relations. It takes the form of functional dependencies between the parameters taken into account by the corresponding conceptual model. These dependencies specify the cause-and-effect relationships identified in the conceptual model and characterize them quantitatively.

Thus, model is a special object that in some respects replaces the original. In principle, there is no model that would be a complete equivalent of the original. Any model reflects only some aspects of the original. Therefore, in order to obtain large gaps about the original, it is necessary to use a set of models. The complexity of modeling as a process lies in the appropriate selection of a set of models that replace a real device or object in the required respects. For example, a system of differential equations that describes switching processes in the elements of a digital device can be used to evaluate their performance (switching time), but it is inappropriate to use for constructing tests or timing diagrams of the device’s operation. Obviously, in recent cases it is necessary to use some other models, for example, logical equations

As already noted, these methods are direct manifestations of social dialectics, or dialectics social cognition. They are called general scientific because they are used in the knowledge of all phenomena of reality, therefore, in all sciences, including political science.

These methods were formed during centuries cognitive activity people and improve in the process of its development.

It should be said that general scientific methods, being methods of understanding reality , are simultaneously methods of thinking of researchers ; on the other hand, the methods of thinking of researchers act as methods of their cognitive activity.

Let's give brief description basic general scientific methods for studying political phenomena and processes.

Analysis and synthesis

When studying political problems, scientists subject them to scientific analysis , i.e. mental division of political phenomena into their elements , to study each of them. But any of these elements functions only in connection and interaction with other elements. Therefore, the analysis of elements involves simultaneously understanding their relationships and interactions what constitutes the content synthesis.

Thus, analysis and synthesis are two interconnected aspects mental activity people and, accordingly, two interrelated methods of understanding reality, in this case political.

When analyzing political phenomena, one comprehends specific features their elements and their role in the functioning of these phenomena. In the course of scientific synthesis, a holistic understanding of these phenomena, their content and laws of development is formed.

In the process of analytical and synthesizing thinking activity, a transition occurs from initial speculative (and therefore superficial) judgments about the political phenomena being studied to more or less deep and holistic ideas about them. The emergence of new knowledge about them indicates the creative (heuristic) nature of analysis and synthesis.

Inductive and deductive methods of cognition

Inductive method (induction ), used in political research, is a way of understanding political phenomena that goes from recording experimental (empirical) data and their analysis to their systematization, generalizations and general conclusions drawn on this basis. This method also consists in the transition from some ideas about certain political phenomena and processes to others - more general and often deeper. The basis for the functioning of the inductive method of cognition in all cases is empirical (experienced) data.

However, inductive generalizations will be completely flawless only if all scientifically established facts on the basis of which these generalizations are made are thoroughly studied. It's called complete induction. But most often it is very difficult, and sometimes impossible, to do this.

Therefore, in cognitive activity, including in the study of political phenomena and processes, the method is used incomplete induction: the study of some part of the phenomena under study and the extension of the conclusion to all phenomena of this class. Generalizations obtained on the basis of incomplete induction, in some cases can be of a completely definite and reliable nature, in others - more probabilistic in nature.

The validity of inductive generalizations can be tested by applying deductive research method. Its essence is to derive from any general provisions, which are considered reliable, certain consequences, some of which can be verified empirically. If the consequences arising from inductive generalizations are confirmed by practical experience (experiment or real political processes), then these generalizations can be considered reliable, i.e. corresponding to reality.

Analogy

This is a certain type of comparison of phenomena and processes, including those occurring in the political life of society: having established the similarity of some properties of certain political phenomena (processes), a conclusion is drawn about the similarity of other properties. In this case, it is necessary to take into account the specific features of the development of political phenomena. There is no need to reduce their research only to the search for analogies. In addition, the analogy method is most often used along with other general scientific methods. At the same time, the scientific effectiveness of using the analogy method is quite high.

Modeling

This is the reproduction in a specially created object (model) of the properties of the phenomenon being studied, including the political one. The use of this method is, as a rule, creative in nature, revealing something new. In particular, when analyzing the model itself, properties are discovered that are absent in its individual parts and their simple sum. This demonstrates the principle: “The whole is greater than the sum of its parts.” The acquired knowledge about a political phenomenon or process as a whole is used for their further study.

When studying processes public life, including political ones, the so-called Causal models. They help to identify objective cause-and-effect relationships and interdependencies between social phenomena, the generation of some of them by others, as well as the emergence of new properties in them. However, such models do not always allow us to draw conclusions about the phenomenon being studied as a whole, since, while revealing its objective aspects, they do not record subjective factors relating to the consciousness of people, whose actions directly determine the content and direction of any social phenomena and processes.

This difficulty is resolved by political scientists as follows: when analyzing political processes, occurring throughout society, i.e. at the macro level, cause-and-effect models are used that identify objective factors of people’s activities and behavior, and when analyzing processes occurring in individual teams, i.e. at the micro level, along with cause-and-effect models, they use so-called cognitive models of interactions between individuals, with the help of which the motives, beliefs and goals of subjects of political activity are identified.

Cognition - this is a specific type of activity aimed at understanding the world around us and oneself in this world.

Analysis (Greek decomposition) – dividing an object into its component parts for the purpose of self-study. Analysis task: from various types of data, create an overall holistic picture of the process, identify its inherent patterns and trends. From the perspective of dialectics, analysis is considered as a special technique for studying phenomena and developing theoretical knowledge about these phenomena. The main cognitive task of dialectical analysis is to isolate its essence from the variety of aspects of the subject being studied, not by mechanically dividing the whole into parts, but by isolating and studying the sides of the main contradiction in the subject, to discover the basis that connects all its sides into a single integrity, and to bring it to on this basis is the pattern of the developing whole. Types of analysis: mechanical dismemberment; determination of dynamic composition; identification of forms in/action of elements of the whole.

Synthesis (Greek connection) - a real or mental unification of various aspects, parts of an object into a single whole. Synthesis is considered as a process of practical or mental reunification of a whole from parts or the connection of various elements, sides of an object into a single whole, a necessary stage of cognition. Modern science is characterized not only by intra- but also interdisciplinary synthesis. The result of synthesis is a completely new formation, the properties of which are not only an external combination of the properties of the components, but also the result of their internal interconnection and interdependence.

Induction ) is a logical method of research associated with the generalization of observations and experiments and the movement of thought from the individual to the general. Inductive conclusions always have a probabilistic character. Types of inductive generalizations: A) Induction is popular, when regularly repeated properties observed in certain representatives of the set (class) under study and fixed in the premises of inductive inference are transferred to all representatives of the set (class) under study - including its unstudied parts. (for example, the fact of the presence of black swans). b) Induction is incomplete– all representatives of the set under study belong to the property “n” on the grounds that “n” belongs to some representatives of this set. For example, some metals have the property of electrical conductivity, which means that all metals are electrically conductive. V) Full induction, in which the conclusion is made that all representatives of the set under study belong to the property “n” on the basis of information obtained during experimental research that each representative of the set under study belongs to the property “n”. G) Scientific induction, in which, in addition to the formal justification of the generalization obtained inductively, a meaningful additional justification for its truth is given, including using deduction.

Deduction – firstly, the transition in the process of cognition from the general to the particular, the derivation of the individual from the general; secondly, the process logical inference, that is, the transition, according to certain rules of logic, from certain given sentences - premises to their conclusions. Deduction prevents the imagination from falling into error; only it allows, after establishing new starting points by induction, to derive consequences and compare conclusions with facts. Deduction can provide testing of hypotheses.

Analogy - a method of scientific knowledge in which similarities are established in certain aspects, qualities and relationships between non-identical objects. Inference by analogy is a conclusion drawn on the basis of such similarity. That is, when inferring by analogy, the knowledge obtained from the consideration of an object is transferred to another, less studied and less accessible object for research. Analogy does not provide reliable knowledge. To increase the likelihood of conclusions by analogy, it is necessary to strive to ensure that: a) the internal, rather than external, properties of the compared objects are captured; b) these objects were similar in the most important and essential characteristics, and not in random and secondary ones; c) the range of matching features was as wide as possible; d) not only similarities were taken into account, but also differences - so that the latter were not transferred to another object.

Modeling as a method of scientific knowledge is the reproduction of the characteristics of some object on another object specially created for their study

. Model - an object that is similar in some respects to the prototype and serves as a means of describing and/or explaining and/or predicting the behavior of the prototype. The need for modeling arises when researching the object itself is impossible, difficult, and expensive. There must be a certain similarity between the model and the original, which allows the information obtained as a result of studying the model to be transferred to the original. At physical (subject) modeling of a specific object, its study is replaced by the study of a certain model that has the same physical nature as the original (aircraft models). Under ideal (sign) modeling models appear in the form of diagrams, graphs, drawings. Ideal modeling includes “mental simulation”: 1) Visual modeling is carried out on the basis of the researcher’s ideas about a real object by creating a visual model that displays the phenomena and processes occurring in the object. Visual modeling: 1.1. At hypothetical simulation a hypothesis is laid about the patterns of processes in a real object, which reflects the researcher’s level of knowledge about the object and is based on cause-and-effect relationships between the input and output of the object being studied. 1.2 Analog Modeling is based on the use of analogies at various levels. 1.3. Breadboard simulation associated with creating a model of a real object on a certain scale and studying it. 2) Symbolic modeling- this is an artificial process of creating a logical object, which replaces the real one and expresses its basic properties using a certain system of signs and symbols. Symbolic modeling is usually divided into linguistic and symbolic. 3) Mathematical modeling based on the description of a real object using a mathematical apparatus.

Classification- partitioning a set (class) of objects into subsets (subclasses) according to certain characteristics. In scientific classification, the properties of an object are placed in a functional connection with its position in a certain system. There are artificial and natural classification: in contrast to artificial (it is based on non-essential similarities and differences of the object, for systematization of objects (alphabetical catalog), in natural classification, according to the maximum number of essential features of the object, its position in the system is determined (for example, natural system organisms, Mendeleev's periodic table of elements). Classification is usually called the division of objects that are the objects of study of a particular science.

General scientific methods (analysis and synthesis, analogy and modeling)

The empirical level of cognition is the process of mental - linguistic - processing of sensory data, in general information received through the senses. Such processing may consist of analysis, classification, generalization of material obtained through observation. Here concepts are formed that generalize observed objects and phenomena. In this way, the empirical basis of certain theories is formed.

What is characteristic of the theoretical level of cognition is that “here the activity of thinking is included as another source of knowledge: theories are constructed that explain observed phenomena, revealing the laws of the area of ​​reality that is the subject of study of this or that theory.”

General scientific methods used both at the empirical and theoretical levels of knowledge are methods such as: analysis and synthesis, analogy and modeling.

Analysis is a method of thinking associated with the decomposition of the object being studied into its component parts, aspects, development trends and methods of functioning with the aim of studying them relatively independently. Such parts can be some material elements of the object or its properties, characteristics.

It occupies an important place in the study of objects of the material world. But it constitutes only the initial stage of the process of cognition.

The analysis method is used to study components subject. Being a necessary method of thinking, analysis is only one of the moments in the process of cognition.

The means of analysis is the manipulation of abstractions in consciousness, i.e. thinking.

To comprehend an object as a whole, one cannot limit oneself to studying only its component parts. In the process of cognition, it is necessary to reveal objectively existing connections between them, to consider them together, in unity. To carry out this second stage in the process of cognition - to move from the study of individual components of an object to the study of it as a single connected whole - is possible only if the method of analysis is complemented by another method - synthesis.

In the process of synthesis, the components (sides, properties, characteristics, etc.) of the object under study, dissected as a result of analysis, are brought together. On this basis, further study of the object takes place, but as a single whole.

Analysis mainly captures what is specific that distinguishes parts from each other. Synthesis reveals the place and role of each element in the system of the whole, establishes their interrelation, that is, it allows us to understand the common features that connect the parts together.

Analysis and synthesis are in unity. In essence, they are “two sides of a single analytical-synthetic method of cognition.” “Analysis, which involves the implementation of synthesis, has at its core the selection of the essential.”

Analysis and synthesis originate in practical activities. Constantly dissecting in its practical activities various items into their component parts, man gradually learned to separate objects mentally. Practical activity consisted not only of dismembering objects, but also of reuniting parts into a single whole. The thought process arose on this basis.

Analysis and synthesis are the main methods of thinking, which have their objective basis both in practice and in the logic of things: the processes of connection and separation, creation and destruction form the basis of all processes in the world.

At the empirical level of knowledge, direct analysis and synthesis are used for the first superficial acquaintance with the object of study. They generalize observed objects and phenomena.

At the theoretical level of knowledge, recurrent analysis and synthesis are used, which are carried out by repeatedly returning from synthesis to re-analysis. They reveal the deepest, most significant aspects, connections, patterns inherent in the objects and phenomena being studied.

These two interrelated research methods receive their own specification in each branch of science. From a general technique they can turn into a special method, so there are specific methods of mathematical, chemical and social analysis. The analytical method has also been developed in some philosophical schools and directions. The same can be said about synthesis.

Analogy is “a plausible probable conclusion about the similarity of two objects in some characteristic based on their established similarity in other characteristics.” Analogy lies in the nature of the very understanding of facts, connecting the threads of the unknown with the known. The new can be meaningful and understood only through the images and concepts of the old, known. The first airplanes were created by analogy with the way birds, kites and gliders behave in flight.

Despite the fact that analogies allow us to draw only probable conclusions, they play huge role in cognition, since they lead to the formation of hypotheses, i.e. scientific guesses and assumptions that, with additional research and evidence, can turn into scientific theories. An analogy with what is known helps to understand what is unknown. An analogy with something that is relatively simple helps us understand something that is more complex. Thus, by analogy with the artificial selection of the best breeds of domestic animals, Charles Darwin discovered the law natural selection in the animal and flora. The most developed area where analogy is often used as a method is the so-called similarity theory, which is widely used in modeling.

One of the characteristic features of modern scientific knowledge is the increasing role of the modeling method.

Modeling is based on similarity, analogy, common properties of various objects, and the relative independence of form.

Modeling is “a research method in which the object of interest to the researcher is replaced by another object that is in a relationship of similarity to the first object.” The first object is called the original, and the second is called the model. Subsequently, the knowledge gained from studying the model is transferred to the original based on analogy and similarity theory. Modeling is used where studying the original is impossible or difficult and is associated with high costs and risk. A typical modeling technique is to study the properties of new aircraft designs using scaled-down models placed in a wind tunnel. Modeling can be subject, physical, mathematical, logical, symbolic. It all depends on the choice of the character of the model.

A model is a means and way of expressing the features and relationships of an object taken as the original. A model is a system objectified in reality or mentally represented that replaces the object of cognition.

Modeling is always and inevitably associated with some simplification of the modeled object. At the same time, it plays a huge role, being a prerequisite for a new theory.

The basis of such a research technique, which is now very widespread in science, as modeling, is inference by analogy. In general, modeling, due to its complex nature, can rather be classified as a class of research methods or techniques.