GUIDEBOOK - A REFERENCE BOOK ON THE TRIZ ECOSYSTEM AND SOME OTHER COGNITIVE TECHNOLOGIES.

   1. FACTORS causing the need to CHANGE TECHNICAL AND NON-TECHNICAL SYSTEMS in order to meet emerging new human needs.

    2. STEP-BY-STEP CYCLE OF SOLVING THE PROBLEM.

   2.1. Identification of the problem and understanding of the situation.

   2.2. Documentation, goal setting and planning.

   2.3. Decomposition of the selected task, division into separate subtasks (the output of one subtask is the input data for another subtask, etc.). The breakdown of a complex system into interconnected "elementary systems" (construction and definition of links of "chain links" of CAUSE-EFFECT RELATIONSHIPS).

   2.4. Synthesis - generation of ideas for solutions.

   2.5. Evaluation and selection of the optimal solution idea satisfying the specified requirements from a finite set of solutions found.

   2.6. To evaluate the evolutionary potential of the system based on empirical patterns of system development and to work out possible ways of further evolution of the system in question.

   2.7. Evaluate the various risks associated with the practical implementation of the solution, whether they can be reduced or completely avoided.

   3. FORMS OF IDEA MANAGEMENT (ABSTRACT CONCEPTS).

It is traditionally believed that the discovery of a new pattern is an example of inductive (heuristic) thinking, largely due to intuition.

With the development of such sciences as philosophy, logic, cognitive psychology, the psychology of creativity, as well as the study of various teaching methods and the operation of artificial neural networks, it became possible to better understand the forms of thought processes at various stages of creativity.

(Reference: Cognitive science, cognitive science (lat. cognitio "knowledge") is an interdisciplinary scientific direction that combines the theory of knowledge, cognitive psychology, neurophysiology, cognitive linguistics, non-verbal communication and the theory of artificial intelligence. Refers to transdisciplinary research, interdisciplinary science , cognitive science).

At various stages of solving a problem, methods can be used, which are various forms of managing methods of cognition, logical conclusions, ideas (abstract concepts).

DEDUCTIVE METHODS (with partial "interspersing" of INDUCTION METHODS) used in TRIZ narrow the scope of the solution search to certain limits, which allows you to quickly optimize the existing system, increase its positive effect and reduce, neutralize or eliminate negative properties (On the Altshuller H.S. scale - 1, 2, 3 levels of solution quality - inventions).

    3.1. Deduction, Deductive logic (inference, from general to particular, consequences are derived from accepted hypotheses ('something' must be));

The doctrine of how to acquire reliable knowledge was systematized by Aristotle (384-322 BC) in the form of the science of knowledge, deductive logic or "Classical Logic".

Aristotle owns the doctrine of scientific proof, set forth in his work "Organon" - a universal instrument of true knowledge.

Deduction (derived from the Latin word: deductio - conclusion).

Deduction is a logical and methodological procedure by which the transition from GENERAL to PARTICULAR is carried out in the process of reasoning. (Rational form of thinking).

A logically correct conclusion from already existing knowledge or from already existing thoughts) in the process of reasoning - from the accepted hypothesis the Consequence is deduced: “SOMETHING” must be. The general underdevelopment of the experimental sciences (empirical knowledge) of that time prevented us from recognizing such a method of knowledge as incomplete induction as the main one. Usually, it is believed that Aristotle recognized only complete induction, and underestimated the incomplete one. (See Fig. 1.)

Deduction is a logically correct conclusion from already existing knowledge or from already existing thoughts, which is often used in standard education and activities little related to creativity.
(For example, the executor works according to unambiguously specified: in education - training programs; in production - technological maps; in administrative and bureaucratic activities - a system of legislative acts and official instructions; in paramilitary structures - service regulations, instructions, orders; ...).

    3.2. Induction ,
JOHN FREDERICK WILLIAM HERSHEL ON INDUCTION (1792–1871) ,
Mill's Methods Of Induction (1806–1873) ,

      Induction - guidance, prompting, from the particular to the general, empirical testing of hypotheses or consideration of hypotheses and measuring the degree of their agreement with the facts ('something' really exists, probabilistic logic, confirmation logic).

The inductive method of cognition, inductive logic (Bacon's Method) - was introduced by the English philosopher, historian, publicist, statesman, founder of empiricism and English materialism Francis Bacon (1561-1626), in his essay "The Great Instauration" and "New Organon" (1620).

Dissatisfied with the state of the sciences of his time, Bacon attempted to update the way of studying nature, which would not only make the existing sciences and arts more reliable, but also made it possible to discover new ones, still unknown to mankind.

He contrasted the dogmatic deduction of the scholastics with the inductive method based on a rational analysis of experimental data.

"The New Organon" became the second part of the extensive work "The Great Restoration of Sciences", which, according to Bacon's idea, should consist of six parts. However, the author has finished only the first two parts.

Subsequently, a huge contribution to the development of inductive methods was made by such scientists as (See Fig. 6):
John Herschel, John Stuart Mill, William Whewell, Augustus de Morgan, William Stanley Jevons, Pierre-Simon de Laplace.

Induction (from Latin inductio - prompting (guidance)) is a term widely used in science.

The essence of inductive analysis of facts boils down to the fact that through the study of various kinds of RELATIONSHIPS phenomena ("Processes") in a real-life or thought experiment, to discover their true causal relationships and dependencies on each other.

The main task of the science of nature (similarly - technical or non-technical system) is to study the causal relationship of phenomena ("Processes"), and not just their material composition - the number and properties (parameters) of "Objects".

In inductive logic, the task is to find general forms of phenomena ("Processes"), and not their specific differences.

In this teaching, Francis Bacon adheres to the philosophy of Aristotle and by the forms of phenomena ("Processes"), he means those general laws or typical relations of phenomena, to the discovery of which all experimental science strives.

The inductive method is used when there are no GENERAL algorithmic solutions.

The inductive method is a method of research and presentation, in which from the observed PARTICULAR facts one proceeds to the allocation of principles, GENERAL provisions of the theory, the establishment of regularities.

Induction is a type of GENERALIZATION associated with anticipating the results of observations and experiments based on experience data (PARTICULAR data).

In induction, the data of experience (PARTICULAR data) "suggest" the GENERAL, therefore inductive GENERALIZATIONS are usually considered as experimental truths or empirical laws.

Induction is a term widely used in science. Induction - (introduction, guidance, from particular to general, empirical testing of hypotheses put forward or consideration of hypotheses and measuring the degree of their agreement with the facts. “SOMETHING” really exists, probabilistic logic is used, confirmation logic is used.

HEURISTIC PROBLEM SOLVING - a problem solving path that includes a practical method that is not guaranteed to be accurate or optimal, but sufficient to solve the problem, it involves the use of an INDUCTION method (an inference process based on the transition from SPECIAL to GENERAL FEATURES (See. Fig. 7.)).

Figure. 7. "ELEMENTARY" SYSTEM, NOT TECHNICAL (ES element can also be a PROCESS).

In some TRIZ methods, when solving problems, a logical inference based on incomplete induction is used.
Methods of fuzzy logic theory and the like are used for Event 2 and are designed to calculate the result (Conclusion, Consequence), the occurrence of which has the highest probability from the set of options.

The inductive method is used when there are no GENERAL algorithmic solutions.
The inductive method is a method of research and presentation, in which one moves from the observed SPECIAL facts to the selection of principles, GENERAL provisions of the theory, the establishment of patterns.
Induction is a conclusion from facts to some hypothesis (general statement, patterns).
Inductive conclusions are built on the basis of experimental data. Depending on the completeness and completeness of the experience underlying the generalizations, a distinction is made between complete and incomplete induction.

Full induction - when the generalization refers to a finitely visible field of facts.
The conclusion of the full induction gives certain knowledge.
In full induction the conclusion is connected with necessity, not with some probability, and follows from premises.
Thus, this "full induction" is a kind of deductive reasoning, although in its external form, in the course of thought it resembles incomplete induction.
Incomplete induction - when the generalization refers to an infinitely or finitely - boundless field of facts.
The conclusion of incomplete induction gives probabilistic knowledge.

Incomplete induction - the incompleteness of the inductive generalization is expressed in the fact that not all, but only some elements, or parts of the class are examined.
In inductive logic, the task is to find general forms of phenomena ("Processes"), and not their specific differences.
The probability of a conclusion in a given scheme, therefore, can range from very small to almost complete certainty.
Due to this fact, in incomplete inductive logic, special methods for estimating the probability of conclusions are developed.

General knowledge, in comparison with the totality of disparate knowledge about individual objects of a class, is valuable in that it can suggest that there is some connection between the objects of the class and the attribute, and thus stimulate further knowledge.

Incomplete induction is divided into two types:
a) Incomplete "popular" induction enumerative (through enumeration of similar cases) , (induction by simply listing similar cases, generalization is carried out on an insignificant basis), in the absence of a contradictory case, is not reliable, so the greater the number of premises, the higher the probability of the conclusion. Popular induction is the first step in the development of scientific knowledge. Science begins with empirical research, classification, identification of stable connections, relationships and dependencies. The first generalizations in science are due to the simplest inductive conclusions through a simple enumeration of recurring features. They perform an important heuristic function of initial assumptions, conjectures and hypothetical explanations that need further verification and clarification. In conditions where only some representatives of the class are studied, the possibility of a hasty generalization is not ruled out. Erroneous conclusions in the conclusions of popular induction may appear due to non-compliance with the requirements for accounting for contradictory cases, which make the generalization untenable. Of the many phenomena, he fixes only those that turn out to be predominant in experience, and builds a hasty generalization on their basis.

b) Scientific induction (incomplete) (the transition to general knowledge is made on the basis of identifying the necessary features (generalization is carried out on an essential basis) and the necessary connections between objects and phenomena of nature and society).

The probability of conclusions of scientific induction depends not so much on the number of subjects considered, but on the correctness of the principles of selection, on how accurately the factors influencing the presence and change of the trait under study are taken into account, how scientific the induction is:

Scientific induction is characterized by the search for causal relationships between phenomena and the desire to discover the essential features of objects that are combined into a class. Scientific induction is called inference, in which a generalization is built by selecting the necessary and eliminating random circumstances.

Scientific induction (incomplete) is divided into:

* Incomplete scientific induction "selective" - (induction on a representative sample) (from the Latin - "I choose") - in it, the conclusion about the attribute belonging to a class of objects is based on the study of samples methodically selected from different parts of this class (subset). This is a special kind of enumerative incomplete induction, which is induction through the selection of facts that exclude random generalization.

* Incomplete scientific induction "through the study of a single representative of a certain class" - (eliminative induction on a typical representative) (from Latin - exclude) - typical representatives are selected for premises, i.e. items that are fundamentally different from one another.
Elimination induction, or Eliminative induction (exclusion of different cases) is a system of inferences in which conclusions about the causes of the phenomena under study are built by detecting supporting circumstances and excluding circumstances that do not satisfy the properties of a causal connection.
This is a conclusion about the belonging of a feature to a class of objects, based on the study of typical samples without taking into account their individual characteristics. It is built not only on the basis of the study of a number of phenomena or objects included in a certain class, but also on the basis of the study of a single representative of the specified class. In this case, when reasoning about the belonging or absence of a certain feature of an object, its individual properties that distinguish it from other objects of the same class should not be used. The cognitive role of eliminative induction is the analysis of causal relationships.
Causal is such a connection between two phenomena, when one of them - the cause - precedes and causes the other - the action.
Modern logic describes five methods for establishing causal relationships:
(1) similarity method,
(2) difference method,
(3) combined method of similarity and difference,
(4) concomitant change method,
(5) residual method.


The method of mathematical induction and transfinite induction uses complete induction for infinite countable and uncountable sets of objects, respectively.
The core of mathematical induction is: dispersion , correlation , regression and variational analysis.

    3.3. Adduction - FUTURE LOGIC (bringing, attracting, attaching, attachment, designation when deduction is attached (connected) to induction);

    3.4. Traductive reasoning, (moving, analogia (other-Greek. analogia - correspondence, similarity)), from the singular to the singular, from the particular to the particular, from the general to the general. Traductive reasoning is an analogy. ('Something' should be like some kind of analog)).

    3.5. Abduction, Abductive reasoning, (syllogism, a kind of reductive inference, withdrawal, a class of plausible reasoning, search and justification, explanatory hypotheses or the study of facts and the construction of a hypothesis explaining them (assumes that 'something' can be));

    3.6. Conventionally, there are three large classes of problems that need to be solved:

    3.6.1. Tasks for optimizing a substance, product, method, process in the mastered areas of human activity (mainly ALGORITHMIC methods are used - "tools" and deductive type TRIZ methods (part of ARIZ, FA, VA/VE/VM (FSA), etc.) .d.), on the scale of H.S. Altshuller - 1, 2, 3 levels - inventions).

    3.6.2. Non-standard, complex, creative tasks - when it is necessary to resolve a contradiction (or more detailed - INCONSISTENCY OF THE PARAMETERS OF INDIVIDUAL PARTS OF A COMPLEX SYSTEM) in the system under consideration with the help of a selected (or found, created) substance, product, method, process, structure. TRIZ uses in this case partially deductive methods interspersed with inductive methods, according to the scale of H.S. Altshuller - 1, 2, 3 levels - inventions.

    3.6.3. Non-standard, complex, creative tasks - when in any field of science, when setting up an artificially created experiment or observing a natural phenomenon, reliable facts (results, consequences) appear that do not fit into the accepted scientific paradigm (such a task is called an anomaly task).
When solving an anomaly problem, the cause-and-effect relationship in the model of the "elementary system" of the current scientific paradigm changes and a new scientific paradigm arises (on the scale of H.S. Altshuller - 4, 5 levels - discoveries).

... The research carried out by the scientific community within the framework of the current paradigm is referred to as "normal science".
"Normal science" does not set itself the goal of creating new theories.
The main activity of "normal science" is "solving puzzles", that is, problems that are obviously solvable within the framework of the accepted paradigm.
The period of "normal science" ends when a problem is encountered that does not fit within the current paradigm. Thomas Kuhn calls such a task an "anomaly"...

   4. WAYS AND STRATEGIES FOR SOLVING PROBLEMS.

    4.1. ALGORITMIC - a finite set of precisely specified rules for solving a certain class of problems or a set of instructions describing the order of the executor's actions to solve a specific problem.

The algorithmic way of solving the problem involves the use of a DEDUCTIVE METHOD: when there is a consideration of a PARTICULAR case of WELL-known algorithms.

The algorithmic path is used to solve standard problems of various levels of complexity.

Algorithmic path (DEDUCTIVE METHOD, DEDUCTIONAL LOGIC, from general to particular, rational thinking, ready-made instructions, according to the algorithm) is used in TRIZ in the sequential (formal) execution of ARIZ steps (work according to a given instruction), as well as in the use of a number of TRIZ "tools" related to the assessment, comparison, analysis of the structure and processes in the system under consideration (rational thinking is used).

   4.2. HEURISTIC is an algorithm for solving a problem that includes a practical method that is not guaranteed to be accurate or optimal, but sufficient to solve the problem. Allows you to speed up the solution of the problem in cases where an exact solution cannot be found.

   Heuristic methods are logical techniques and methodological rules of scientific research and inventive creativity that can lead to goals in conditions of incomplete initial information and the absence of a clear program for managing the process of solving the problem.

   HEURISTIC algorithm for solving the problem involves the use of the INDUCTIVE method: this is the process of logical inference based on the transition from PARTICULAR position to GENERAL .

             4.2.1.EDUCATION.

       The INDUCTIVE method is more preferable in the educational process, since it allows you to quickly, with the help of guiding PARTICULAR abstract situations, associations, questions and corrections of the teacher, to rediscover already known (GENERALIZED) knowledge, laws in the sciences, and students to develop creativity and a creative approach to solving problems in various spheres of human activity. Such training is close to the game situation and arouses interest on an emotional level, which allows you to memorize and comprehend new knowledge more deeply.

   4.2.2.2. The first stage of the neural network functioning is training, the INDUCTION METHOD is used.
INDUCTIVE method (guidance) - from the particular to the general or generalization, it reveals the relationship between different data (parameters). Neural network training consists in finding optimal values for all weight coefficients w (methods can be: training "with a teacher", training "without a teacher", training with reinforcement (reinforcement learning)).

Training can also be performed by three methods: stochastic method (stochastic), batch method (batch) and mini-batch method (mini-batch).

From the point of view of mathematics, neural network training is a multiparametric problem of nonlinear optimization.

A neural network in the process of tuning (training) to solve a specific problem is considered as a multidimensional nonlinear system that, in an iterative mode, purposefully searches for the optimum of some functional that quantifies the quality of the solution of the task.

There are several methods of teaching NS, the most interesting are three:

   4.2.2.2.1. The Backpropagation method.

   4.2.2.2.2. Resilient propagation or Rprop.

   4.2.2.2.3. Genetic Algorithm.

The main and most common neural network architecture, which revolutionized the teaching of the network "without a teacher" (clustering, self-learning), and also allowed the transition from academic interest to commercial use, is the back propagation network (the method uses a gradient descent algorithm) - a powerful tool for finding patterns, forecasting, qualitative analysis...

Gradient descent is a way of finding the local minimum or maximum of a function by moving along a gradient.

They got this name – back propagation networks because of the training algorithm used, in which the error propagates from the output layer to the input, i.e. in the direction opposite to the direction of signal propagation during normal network operation.

The neural network of back propagation consists of several layers of neurons, and each neuron of layer i is connected to each neuron of layer i+1, i.e. we are talking about a fully connected neural network.

In general, the task of training a neural network boils down to finding some kind of functional dependence Y=F(X), where X is the input and Y is the output vectors. Such a problem, with a limited set of input data, has an infinite set of solutions.

To limit the search space in the neural network training mode, the task of minimizing the objective function of the neural network error is set, which is found by the least squares method.

   4.2.2.3. The second stage of the functioning of the neural network is working, uses the METHOD of DEDUCTION.
DEDUCTIVE method (deduction) - from the general to the particular, it restores the original data set (signal, image) for part of the information from noisy or damaged input data ((auto)associative memory). This is a direct work of the neural network with the initial data on the search for patterns, forecasting, qualitative analysis...(or - promotion and selective change of signal parameters ("filtering"), according to an already configured and verified set of "weights" of neural connections (parallel use of multiple algorithms, "instructions"), in order to find patterns, ...).

From the point of view of the development of computing and programming, a neural network is a way to solve the problem of effective parallelism.

After solving a non-standard problem by heuristic methods, its further optimization is most often performed by algorithmic methods.

      4.2.3.7.Creative, heuristic, inductive TRIZ "tools" and methods can be used inside deductive, algorithmic methods, for example: in some steps of ARIZ or within some stages of the analysis of the structure and processes in the system under consideration.)
      TRIZ creator - H.S. Altshuller sought to transform (find common features, generalize, classify), as far as possible, heuristic (induction, "guiding") methods of thinking into algorithmic (deductive, "according to the algorithm", "according to instructions") methods of thinking, so that the search for new solutions would be more accessible, logically justified, and carried out for the maximum amount of time short time, by any interested researcher of the problem.
      H.S. Altshuller created procedures for finding the "root" contradiction (or more detailed - INCONSISTENCY OF THE PARAMETERS OF INDIVIDUAL PARTS OF A COMPLEX SYSTEM) in the system and eliminating it using algorithms (instructions - deductive methods).
      But, some INDUCTIVE "Tools" and methods TRIZ that activate creative, imaginative (eidetic) thinking have not yet been converted from INDUCTIVE methods to DEDUCTIVE (algorithmic, according to the instructions).
      To solve this problem, non-algorithmic TRIZ "Tools" and methods must be classified (typified) in a certain hierarchy and associated with certain logical and mathematical operators (formalized).
      Then, when bringing these "Tools" and methods to uniquely defined algorithms, it becomes possible to simulate them programmatically.
      4.2.3.7.1. DEDUCTIONAL METHODS (with partial inclusion of INDUCTIVE METHODS) narrow the scope of the search for a solution to certain boundaries, which allows you to quickly optimize the existing system, increase its positive effect and reduce, neutralize or eliminate negative properties (According to the Altshuller scale - 1, 2, 3 quality levels solutions - inventions).
      4.2.3.7.2. INDUCTION METHODS, and the most radical of them is the "Trial and Error Method" (TaEM), with the correction of the intermediate result by the "Error" or the "Experimental Method", expand the boundaries of the search for possible solutions and have been used in science for centuries. These methods usually require large material and time costs, but lead to higher results (According to the Altshuller scale - 4, 5 levels of solution - discovery quality).)

      4.2.3.8. TRIZ can be an alternative to TaEM in science, but not when searching for a solution within the considered outdated complex system (since the task is not to optimize the considered complex system, but to replace part of its "elementary systems" with new ones, and such a transition is necessary already at the initial stage of solving the problem ).
      In such new "elementary systems", the positive effect specified earlier in the requirements for obsolete "elementary systems" should be produced. The negative effect previously identified in obsolete "elementary systems" should be absent or insignificant. Such a transition to new "elementary systems" eliminates the appearance of contradictions (or more detailed - INCONSISTENCY OF THE PARAMETERS OF INDIVIDUAL PARTS OF A COMPLEX SYSTEM) formulated in an outdated complex system.
      Thus, during a scientific discovery, obtaining new knowledge requires transferring the consideration of the problem to "supersystems", "subsystems" or "alternative" systems of the same level, relatively obsolete "elementary systems".
       The problem is that there can be many such "supersystems", "subsystems" or "alternative" systems with respect to obsolete "elementary systems", including those that are still unknown, therefore their description and choice is a separate task.
      An analysis of the evolution of systems in accordance with empirical patterns and lines of development, used in TRIZ, and is intended to search for new “elementary” Systems. (See Fig. 12, 13).

      4.2.3.9. And here a natural question arises in relation to Artificial Intelligence (AI) systems created on the basis of neural networks (NN):
what needs to be done so that the NN in the process of solving the problem "goes" beyond the limits of the task in the outdated "elementary system" and creates (designs) a new "elementary system" on other principles of functioning using the resources of the "supersystem", "subsystem" or "alternative" system, relative to the level of the obsolete "elementary system"? (An analogy from thermodynamics - "phase transition" ("phase transformation")).
      Or is this property (“privilege”) inherent only in the NS of natural origin in some scientists-researchers (human intellect)?
      And will this form of "information processing" in AI systems become the moment of the birth of a full-fledged, “strong” intelligence (the hypothesis of a technological singularity, called the “intellectual explosion” by the British mathematician and cosmologist Irving John Good.

      This may be the very moment when AI will be able to put into practice the search (or construction) of unknown (hidden) new "elementary systems" on other principles of functioning using resources of "supersystems", "subsystems" or "alternative" systems, relative to the level of the obsolete "elementary system".
      Perhaps, with such a transition to the new "elementary system", new causal relationships will appear, which may differ from the relationships in the outdated "elementary system".










Note: Can some works of abstract art influence the researcher in "constructing" a new system?
Before the advent of photography, the purpose of creating works of art was to capture various characters, moments of life, history, mythology, religion ... using various expressive forms.
When affordable photography, video, printing, 3D printing appeared, then many functions of art became possible to quickly reproduce in these technologies.
What functions have modern art created by man, and not by technology? What is the purpose of contemporary art?
The following answer suggests itself: machine technologies cannot go beyond the limits set, determined by their design and programs, they can combine them, mix them, distort them in different ways, brighten / darken, filter, connect / separate, repeat, change the scale, etc.
And some artists can go beyond their "everyday rationalism", "program of rational existence", "psychological inertia" imposed by the living environment and the surrounding society in their works of abstract art.

      4.2.3.10. Thus, there is a need to search and describe the hypothetical conditions of existence, properties, features and principles of functioning of such unknown (hidden) "elementary systems" of various levels, relative to the level of the outdated "elementary system".

      4.2.3.10.1. The minimum specified requirements for the properties of the new "elementary system" are defined as:
      - should produce a positive effect (similar to the effect of the obsolete "elementary system");
      - there should be no negative effect (similar to the effect of the outdated "elementary system");

      4.2.3.10.2. Principles of operation suitable for use in the new "elementary system" may be in another area of knowledge and for transfer they can use the method of thinking - traduction (movement, analogy).

      4.2.3.10.3. Principles of operation suitable for use in the new "elementary system" can be generated by hypotheses using the method of thinking - abduction (a cognitive procedure for putting forward hypotheses - from the first premise, which is a conditional statement, and the second premise follows from the conclusion (syllogisms have: 3 rules of terms, 4 rules of premises, 4 figures, 256 modes, 24 correct (reliable) conclusions, 232 incorrect (probabilistic) The semantic verification of the syllogism is carried out using three-dimensional diagrams - Euler circles).

      4.2.3.10.4. Principles of operation suitable for use in a new "elementary system" can be generated by empirical testing of hypotheses put forward or by consideration of hypotheses and measuring the extent to which they agree with the facts. The method of thinking used is induction (guidance, from particular to general, search for common properties, signs, and finally - generalization of experimental data)...

      4.2.3.10.5. In the new “elementary system”, when using a new principle of functioning and establishing a new cause-and-effect relationship, a change of Subject and Object must occur.
      To select a new Subject and Object, it is necessary to use real-field resources (VFR) located inside and around the considered "elementary system".

Table 9. Su-Field Resources (SFR) are divided:

      4.2.3.11. Identification of heuristic potential, through various stages of human thinking, to search and explore the properties of a new system.
      The founder of the research tradition of studying higher psychological functions, Lev Semyonovich Vygotsky (1896-1934) and the creator of The Theory of Cognitive Development, Genetic Epistemology, Operational Theory of Intelligence, Jean William Fritz Piaget ( 1896-1980) , single out "BEFORE CONCEPT" and "CONCEPT" periods of thinking as carriers of the heuristic potential of human thinking.

       4.2.3.11.1. The “BEFORE CONCEPTUAL” period has an independent value; PROJECTIVITY or PROBABILITY. The research process is built according to the laws of game improvisation, and the researcher's thinking grid is a PROBABILISTIC grid that forms the “manifestation” of the initial contours of the new system.

      4.2.3.11.2. First, undifferentiated thinking (“SYNCRETIC” period) is described. The phenomenon of "SYNCRETISM" consists in the desire to lump together the most diverse elements that do not have an internal connection, bringing them into an undivided, fused image, i.e. to replace the lack of objective connections with an overabundance of subjective connections and to take the connection of impressions and thoughts for the connection of things.
      And in this overabundance of subjective ideas and assumptions lies the heuristic potential that has always distinguished the thinking of those people whom we call outstanding and brilliant. There is no generalization and systematization of the surrounding world at this stage of thinking, pure subjectivism is manifested.

      4.2.3.11.3. "COMPLEXES" - it lays the fundamental foundations for a creative-variative attitude to the object of study).
      At this stage of thinking, there is an attempt to generalize and systematize the world around us based on some objective connections; however, these generalizations each time have individual variability and ambiguity, the ability to subjectively arrange objective connections is formed, which means that thinking does not just follow one or another objective regularity, but also enters into a creative dialogue with these regularities.
      Each element of the "COMPLEX" can be associated with the whole, expressed in the "COMPLEX", and with the individual elements included in its composition, with a variety of connections.
      In "CONCEPT" these connections are basically the relation of the general to the particular and the particular to the particular through the general.

      4.2.3.11.3.1. "ASSOCIATIVE COMPLEX" - any associative connection with any of the features noticed by the researcher in the object that in the experiment is the core of the future complex. It is possible to build a whole complex around this core, including in it the most diverse elements, united by some identical feature. Any "lateral", "incorrect", random associative connection between the core and the element of the complex turns out to be a sufficient reason for referring the object to the group selected by the researcher and for designating this object with a common family name (type, class).

      4.2.3.11.3.2. "COLLECTION COMPLEX" - various specific objects are combined on the basis of mutual complementation according to any one attribute and form a single whole, consisting of heterogeneous, complementary parts. It is the heterogeneity of the composition, mutual complementation and unification on the basis of the collection that characterizes this stage in the development of thinking. However, the researcher selects them not chaotically and not randomly, but on the basis of their difference and addition to the main feature contained in the sample and taken as the basis for association. The principle of variability and the principle of complementarity are used.

      4.2.3.11.3.3. "CHAIN COMPLEX" - is built on the basis of a dynamic, branching chain of associations. For example, a researcher to a sample - an object with a feature (property, parameter) "A" and "B", selects objects with similar features, and then, if the last of the selected objects turns out to be with a feature (property, parameter) "A" without a feature ( property, parameter) "B", the researcher selects other objects with the attribute (property, parameter) "A" and "C", but without the attribute (property, parameter) "B". This again turns out to be sufficient to approach a new attribute (property, parameter) and pick up items further according to the attribute (property, parameter) "C".
      During the formation of a "chain COMPLEX", a transition is made from one feature (property, parameter) to another. Thus, in the "chain COMPLEX" the structural center may be completely absent. Particular concrete elements can enter into a relationship with each other, bypassing the central element or pattern, and therefore may not have anything in common with other elements, but nevertheless belong to one "chain COMPLEX", since they have a common feature with some another element, and this, the other, in turn, is connected with the third, and so on.
      The transition from one structurally organizing center of thinking to another takes place according to a completely random, probabilistic logic, and it is at the point of this probabilistic transition (or in a series of these poorly motivated, probabilistic transitions) that what we call scientific discovery.

      4.2.3.11.3.4. “DIFFUSE COMPLEX” is a sign that associatively combines individual specific elements and “COMPLEXES”, as it were, diffuses, becomes indefinite, spilled, vague, as a result of which a complex is formed that unites, with the help of diffuse, indefinite connections, visually - specific groups of images or objects.
       The researcher, for example, to a given sample - an object with a feature (property, parameter) "A" - selects not only objects with a feature (property, parameter) "A", but also objects with similar features (properties, parameters) "-A +", as they remind him of the features (properties, parameters) "A" of the modified initial object. Further to the features (properties, parameters) "-A+" of the object, derivative features (properties, parameters) "--A++", "---A+++", etc. can adjoin.
      Just as here the form, taken as the main feature, spills and becomes indefinite, different features (properties, parameters) also sometimes merge. But after all, the ability to think in blurry, approximate, fuzzy outlines is the ability that fundamentally distinguishes heuristic thinking from thinking focused on the available forms of knowledge and understanding, and in this way the fundamental unlimited possibilities of expansion and inclusion in the main genus (type, class) can manifest themselves. more and more new, but completely specific objects. The limitless "COMPLEXES" generated by creative people often amaze with the universality of the connections they unite.

      4.2.3.11.3.5. “PSEUDO CONCEPT COMPLEX” – is formed by the researcher whenever he selects a number of objects to the given sample that could would be selected and combined with each other on the basis of some abstract concept.
      For example, a researcher to a given sample - an object with a feature (property, parameter) "A" - selects all the objects in the experimental material with a feature (property, parameter) "A". Such a group could also arise on the basis of abstract thinking (the concept or idea of an object with a sign (property, parameter) "A"). But in fact, as the study shows, the researcher combined objects on the basis of their specific, factual, visual connections, on the basis of simple association. He built only a limited "ASSOCIATION COMPLEX"; he came to the same point, but went in a completely different way.

      4.2.3.11.4. Any theoretical thinking, as scientific studies have shown already in the 20th century, is based on a certain "IMAGERING LINING" - the totality of what could be called "INTELLECTUAL IMAGERY". Any genuine understanding does not begin at all at the “CONCEPTUAL” level, but at the level of intuitive grasp of the “IMAGE OF UNDERSTANDING”.
      and only through “personal figurative structures” ascends to the “essence of the actual concept”. Moreover, although the "IMAGE" does not have the accuracy and clarity of "CONCEPTUAL STRUCTURES", it has a "HUGE POTENTIAL OF HEURISTICITY".
      There is no universal universality of the "CONCEPT" in this "FIGURE LINING", but there is a coiled spring of huge cognitive interest, functioning according to the laws of inaccurate, approximate, vague, incorrect thinking - thinking in "COMPLEXES" .
      At first, thinking is characterized by "COMPLEX" thinking with a predominance of "PSEUDO CONCEPTS" (which corresponds to the boundaries of concrete - operational thinking), and only then do "CONCEPTUAL" structures develop (a stage of formal operations arises) , "thinking in CONCEPTS", while the formation of a new system is completed.

      4.2.3.11.5. Based on the concepts of operational thinking by L. Vygotsky and J. Piaget, it is possible to create a software package that will help the researcher and the neural network to create new “CONCEPTS” in various areas of human activity. (See Figure 24).

      5. "INSIGHT" — sudden conscious finding of a solution to some non-standard problem, which became the result of prolonged unconscious mental activity.

In psychotherapy, insight refers to a person's awareness of the causes of his condition or problem accompanied by insight and catharsis.

The appearance of insight is facilitated by a change in human activity. There are also special technologies, such as business games and the brainstorming method.

Insight is actively used in psychodrama. After solving the problem by the "illumination" method, its further optimization is most often performed by algorithmic methods.

Intuition (late Latin intuitio "contemplation" from the verb intueor "gaze") is the ability, the property of a person to understand, form and penetrate into the meaning of events, situations, objects through insight, insight, simultaneous unconscious conclusion (based on imagination, empathy and previous experience), "flair", insight.

      METHODS

      6. METHODS FOR SOLVING THE PROBLEM.

   6.1. 'The Triangle of the Conflict Process' in the TECHNICAL sphere.

   6.4. 'The Karpman Triangle' ('The Triangle of Fate', 'The Triangle of Toxic Relationships') in a NON-TECHNICAL sphere.