Composition of thinking methods of the intellect stack ​

Let's give a brief overview of the methods of thinking of the intellect-stack in reverse order of their transdisciplines (the methods are usually named after their disciplines/theories, rarely are methods named after their instruments), from bottom to top, so that it is clearer how the explanations/theories of some disciplines use concepts that have already been introduced by other disciplines:

• Conceptualization teaches to distinguish figures from the background and make them objects of consideration. The role of the agent dealing with conceptualization is a poet, "one who gives names". Some of these objects will later turn out to be systems.
• Composure teaches to keep in focus the "objects" that have already been discussed in conceptualization, giving an idea of consciousness. The role here is "composed", and in the case of a person - a cyborg, because we do not trust the attention of the bare brain, we support it at least with pen and paper, but preferably with a computer. If you don't write it down, you won't think.
• Semantics teaches to differentiate physical objects (dealt with by physics) from mathematical/abstract/mental/ideal objects (dealt with by mathematics), thereby separating objects in the world and objects in their more or less formal descriptions. But these objects can already be kept in focus and for their designation during thinking (composure already showed that everything must be written down) signs/symbols are introduced (dealt with by semiotics, the doctrine of signs). In fact, contemporary semantics is semiotics, the doctrine of the meaning and sense of signs, only supplemented by references to mathematics and physics, rather than simply focusing solely on signs, as is done in semiotics. The role is semantician.
• Mathematics teaches about the different types of abstract objects and their relationships, the behaviors of abstract objects. But semantics has already spoken of their existence. The best system descriptions are of course based on mathematical representations. Mathematics is continuously evolving, today it offers interesting types of mathematical objects that were not used before. The role is mathematician. These newly well-studied mental objects will be found to behave similarly to the actual behavior of physical objects, and mathematical objects will be used by physicists.
• Physics teaches the behavior of physical objects represented by mathematical entities. Semantics have already talked about the fact that physical objects are represented in thought by mental/mathematical objects. But in physics, issues of physical-mathematical information theory are addressed: how exactly mathematical objects are represented in the physical world ("mathematician and astrophysicist - physical objects"). It is in physics where concepts of the system and many other concepts of the systemic approach are first introduced. The role is physicist. Vitaliy Vanchurin further distinguishes between philosophers and physicists: a physicist provides explanations (because he relies on mathematical objects) which can then be measured and predicted, thereby checked. The philosopher is free from this, so the physicist can't really come up with nonsense, whereas philosophers - no problem, their reasoning can be entirely disconnected from reality, fairy tales exist among philosophers but not among physicists. Tying reasoning to reality is key for physicists.
• Theory of Concepts teaches that all objects in some sense resemble each other, and this is described by types/classes or prototypes. And that we can judge objects based on their relationships with each other. Examples of often occurring types of relationships are classification, specialization, composition. Physics (and in it, information theory) has already described how all these descriptions are represented in the physical world on carriers of information. Common human thinking - "imagery" thinking, is based on metaphors. By and large, modeling is also a metaphor, metaphors are needed for building persuasive speech, and for this, one must understand how to work with prototypes for discussed objects. But criticism requires views on objects and relationships to then work with ontology, algorithms, logic. To do this, you need to install a "machine of types" in the brain (or in an AI program) so that it can perform type assignment operations. The role is typologist. Of course, a type is a mathematical object, and keeping attention on the assigned type will require composure, for which you need to write everything down.
• Ontology teaches how we describe/model the world on multiple levels: how we determine what is important and unimportant (modeling as creating "knowledge graphs" corresponding to concepts and their relationships from concept theory), how we use knowledge graphs for reasoning and explanations. We deal with multi-level meta-modeling (abstractions are not arbitrary, but abstraction is controlled by a higher level abstraction). Formal expressions by recognized mathematical objects (e.g., logical predicates), ontologies/knowledge graphs and those expressed in natural language texts, less formal ontics/frameworks, involve concepts (used concept theory) and express properties of the physical world. The role is ontologist.
• Algorismics - This natural/experimental science discusses the ways of reasoning with information models (i.e., calculation methods) that are already known from ontology. These discussions/calculations involve objects in different physical nature universal calculators (all of them correspond to the Turing machine, the most important theoretical result of algorismics - brain, electronic computer, quantum computer, optical computer). The role is algorithmist. There are universal algorithms that can be taught to infinitely closely approximate some applied algorithms. These are the algorithms of artificial intelligence, algorithms dealing with thinking/cognition/learning!
• Logic tells what ways of reasoning over models exist so that the results of reasoning (also models) under correct premises and correct rules of reasoning somehow correspond to the real world. Ontology has already talked about how we have sliced the world into objects, describing this slicing with certain models, so the reasoning works with models, and this reasoning involves calculations, we know this from algorismics. The role is logician. In principle, modern "mathematical logic" seems to be part of mathematics, but we use a more traditional understanding of logic, largely overlapping with embedding semantic issues into logic and emerging into pragmatism, "taking seriously" - it is important to us that non-contradictory reasoning must be the basis for action, and contradictory ones must be the reason to engage intellect and think further, not to act based on logically fallacious reasoning. Hence, the main question we are discussing here - "Why so?" - ignoring errors like "2 * 2 = 5". If you know that there is a mistake in the reasoning, such reasoning cannot be the basis for action! You cannot be illogical!
• Rationality as a fundamental method tells us that logical reasoning on models/theories/explanations/knowledge is necessary for actions that improve the world. So, reasoning on the connection of causes and effects in a specific situation is necessary, for which on one hand you must obtain information about the world, figuring what to look for, then looking and making a decision about action, including whether to look at something else, or if the actor can already decide to act on changing the world under conditions of uncertainty ("a tiger is attacking you: gather additional information and observe, run or attack, or there are some other options - come up with and implement them?! You have about three seconds for all reflections"). Decision theory will be the core of rationality. The role is reason.
• Research/Cognition as a separate method. In fact, this method is part of methods based on "theory of knowledge"/epistemology, rationality as "scientific reasoning" is the second part of epistemology. Epistemology is the theory not of any knowledge method but of one that results are alienated (written culture of science! Expressing in words and formulas in formal languages!) and collectively checked. It differs from the "theory of knowledge"/gnoseology that includes artistic and religious irrational "cognition" of the world in a form of non-alienated own feelings. "Rational research"/"scientific knowledge" talks about how rational agents get useful theories/disciplines/explanations that they then take seriously, start acting based on them in hopes to "save" from unpleasant surprises that the Universe is full of. We are making guesses about a good explanatory (causal) predictive/generative model/theory and then criticize this guess for non-contradictory reasoning results from this model and for better model predictions matching the experiment results. All necessary concepts for describing research are already known from conceptualization, composure, mathematics, physics, semantics, ontology, algorismics, logic, rationality. Research as a discipline/knowledge/theory/model explains how they are involved in the course of infinite knowledge. The role is researcher/scientist.
• Aesthetics gives criteria of beauty (in research, we talk about elegance) in the results of thinking and applied work. Aesthetics explains the influence of a created object by engineers/actors (including artists, performers, and other "people of creative professions", and now AI) not so much on its physical environment as is usually the case with systems, but on other agents triggering changes in self-models, then changes in physical "selves", changes in world models, and changes in confidence/beliefs in the reliability of these models. It is not guaranteed that contemporary aesthetics discusses, for example, the emotional impact of some work products and descriptions only on human agents. No, modern aesthetics examines humans, agents with artificial intelligence, and artificial life. The role is aesthetician.
• Ethics tells us what needs to be achieved in life: what goals are acceptable to set for the agent and how to achieve the realization of these goals. Should people die, or is it better to make them immortal? It is normal if people change their worldview and emigrate into other societies, their originating society thus dying off? What is preferable: to kill and cremate a group of people infected with a fatal virus and thereby save humanity, or not to kill because "not to kill" is a commandment - and to heck with humanity, it will somehow survive on its own? For ethical reasoning on this subject, we already know what rationality is and how research works. Contemporary ethics is multi-level, and for this, we can already fully engage the concepts of the systemic approach from various methods of thinking intellect-stack, to reason about the agents of different system levels, conflicting in aims of "rescuing" objects of these different system levels, possibly to the detriment of "saving" objects from other levels. For some, heck with a country with millions of people, but all people are alive, and countries might disappear, but for others, countries must survive "at any cost", even if after a total genocide a couple of people remain in each of the surviving from the many millions. The role of seeking multi-level optimization of the survival of objects from different system levels during an inevitable conflict - conscience. And then, when an agent is "haunted by conscience" - either it is "ethical kulybism", when the agent knows nothing about contemporary ethics as a fundamental method of thinking (with its transdiscipline), or it is the thinking of a civilized and educated agent (be it a human or a computer with AI) who knows something about contemporary multi-level ethics.
• Rhetoric tells us how to persuade someone to perform certain actions, or conversely - persuade them not to act. You start by having some rational model of the situation (obtained through research). Next, you explain your model of the situation to another agent, trying to convince them to use this model to achieve some of your goals, delaying the pursuit of their own goals. Rhetoric considers ethics, so as not to encourage agents (animals, humans, robots) to do something bad with sweet words. The role is rhetorician. But you also use the theory of concepts: sweet speeches involve metaphors and images to work with prototypes. Sweet persuasive speeches appeal to fast thinking for intuitive decision-making and attention in communication. The transfer of rational models/knowledge, on the other hand, requires a rejection of fast thinking in favor of slow thinking with strict types of objects and relationships, and then decisions will be made rationally, based on logical reasoning and decision theory, rather than based on metaphors and what's beyond metaphors intuition. Rhetor deals with just that: translating content between different ways of expressing knowledge, and also inciting action based on conveyed knowledge.
• Methodology tells about the methods/ways/practices of work (labor/activities/culture/engineering), in which agents (people, AI agents, enterprises) organize into a team (in the case of enterprises - this is an "extended enterprise"), take roles in it based on division of labor according to "work methods"/"types of work"/practices/cultures, and then perform collective work on changing states of certain objects, maintaining their roles, in the sense that each agent specializes in some method of work. The most important thing is that the change of states of objects occurs not just during work but during work according to methods/ways of work. Methods are constantly evolving, the best ones are chosen for the present day. Rhetoric allows to understand how agents come to agreements. The role of someone dealing with methodology::method is methodologist. How to describe an applied method? Methodology precisely provides an answer to this question. You describe methods for creating a rocket or growing oats - and then this will be the basis for making organizational decisions on how to organize such an engineering project, change, and replace the methods used in the organization. Methodology deals with work methods called by the many different terms from a long synonymous row: "type of work"/"way of work"/"method of working"/practice/activity/culture/style/"type of engineering" and even "strategy" turns out to be a method of work that some agent will adhere to in achieving their goals.
• Engineering (universal/transdisciplinary/fundamental, often called systems engineering) - this is the most generic way/method of creating new and changing old systems in such a way that the world changes for the better. Only the most general engineering as a normative work method used at all levels of system organization - from inanimate molecules and nanoparticles to humanity as a whole along with all its material culture, millions of tons of substances - is included in the fundamental methods of thinking. And then in applied methods, engineering will be specified for systems of different scales and different types at each scale. All previous levels of intellect-stack methods are involved in reasoning about engineering, especially considering that people, people and computers, sometimes people with other living beings (for example, blinded with guide dogs), and even computers themselves without people are acting as agents (refer to materials on contemporary robotics). The role of someone performing engineering methods is engineer.

Each method of thinking in the intellect-stack, based on a fundamental/scalar discipline/transdiscipline/theory/knowledge, helps to understand the next method/culture/way/practice of thinking in the stack. Although this statement about the stack of thinking methods is quite conditional: all these fundamental methods of thinking are closely intertwined in their explanations/theories/knowledge with each other and the tools of these methods are used by the methods. How and what specifically supports isn't very clear.

We have chosen this composition of the intellect-stack methods and the order of methods therein mainly for methodological purposes: to facilitate understanding while teaching strong thinking. Of course, during the evolution of knowledge, this set of thinking methods will be significantly changed, and a variety of alternative intellect-stacks will be proposed, which will compete with each other. Nevertheless, our courses will be based on such a compilation of fundamental thinking methods and such an order of these methods in the intellect-stack.