System thinking as a strong/universal/common
Continue the brief summary of the material without lengthy explanations and examples: we simply provide it as reference material for you to assess the set of concepts that are convenient for discussing the very system thinking among other types of thinking. Of course, later in our course there will be detailed explanations and examples, and subsequent courses on these topics will provide comprehensive information and references to primary sources. Do not get stuck on a detailed understanding of the synopsis in this section, but use it to get a general overview of the topic.
Thinking can be strong/universal/general (intellectual thinking, not limited to a specific subject area) and narrow/non-universal/applied - thinking about objects of a narrow subject area (growing oilseed crops in general and sunflowers in particular, conducting optical calculations, learning horseback riding). These concepts are similar to how intelligence itself is defined: strong (comparable to human, potentially universal in relation to the subject area) and applied - and here are disputes whether narrow intelligence should be considered intelligence or simply referred to as some applied skill. It is not important to us right now, because intelligence is defined as a set of a special kind of skill - skill that conducts thinking based on a set of fundamental/transdisciplinary thinking methods that are responsible for the universality of intelligence. So discussions about applied skill (skill that can conduct thinking based on applied theories/disciplines/knowledge) follow the same line as for fundamental thinking skill, that is intelligence.
The set of thinking methods that a strong/general intelligence should follow forms the intelligence stack. Stack (as in a "stack of paper sheets") means that each thinking method and its knowledge/theory/"scientific/academic discipline" and tools (usually modeling tools - modelers) higher in the stack are easier to explain using thinking methods below it.
Here are the fundamental thinking methods of the intelligence stack from top to bottom, named after "scientific/academic disciplines"/"explanatory theories"/"fields/domains of knowledge": engineering, methodology, rhetoric, ethics, aesthetics, cognition/research, rationality, logic, algorithmics, ontology, theory of concepts, physics, mathematics, semantics, coherence, conceptualization.
The thinking methods (and their knowledge/theories/disciplines) of the intelligence stack are fundamental (meaning they "lay at the base" of applied thinking using concepts from applied methods of thinking). The fundamentality of the thinking methods of the intelligence stack and the theories/disciplines providing concepts for them increases from the bottom of the stack. The lower in the intelligence stack, the more fundamental the thinking methods and their disciplines are/foundation, their concepts are more abstract compared to the concepts of applied disciplines, making it harder to understand their impact on a situation. But the secret here is that these fundamental thinking methods are useful when encountering problems - situations where it's unclear what to do: what and which applied method to use to change something in the physical world for the better, what to think about, about objects of what subject area. It requires the most abstract thinking about objects, about methods of changing objects, about organizing collective thinking and teamwork.
Such situations, where the intellect and therefore methods of thinking from the intelligence stack need to be engaged, are common. Problems occur not only for those who do nothing, but only initially. In the second moment, idlers will receive their unpleasant surprises from the environment, the surrounding world never sleeps.
The theories/disciplines of the intelligence stack that enhance intelligence (making it more universal in thinking subjects and faster through the use of improved algorithms guiding thinking) affect working with applied methods, helping to understand their concepts.
The visible part of a master's thinking as the work of all his skills (including applied, intellectual, self-care) is applied thinking. Someone knows how to build a house, someone knows how to fix a synchrotron. The results of applied thinking are recognizable, clearly visible! But there is an invisible part in thinking: the manifestation of intelligence as the ability to quickly and universally solve problems never encountered before, to "translate problems into tasks."
A task is a unit of working with a known method/way of performing that work and known required resources, which can be entrusted to a single creator, who has access to resources and masters the method. A significant part of strong intelligence here is the mastery of strategizing: the method of finding an approach to overcome a problem's right strategy::" way of work," and after creating the strategy (chosen method to overcome a problem) - the mastering of planning as the composition of a plan to use resource-constructive resources for implementing the strategy in the form of a set of tasks, and working on them implements the strategy, thereby overcoming the original problem.
System thinking relates to the thinking of intelligence, manifested in overcoming problems, not obtaining understandable results. With the adoption of system thinking, you become smarter (your intelligence becomes stronger), but that doesn't mean you learn to do a specific job: be a good manager, singer, or thermal engineer. No, you will simply overcome encountered problems faster (including problems you will encounter in management, singing, thermal engineering), but you will have to separately learn the applied methods of work.
Some general working methods (system engineering, personal engineering as training, organizational engineering as system management) you will learn in our upcoming courses. We will clearly distinguish: if your question is about how to make system descriptions "in general," it is a question for the system thinking course. If the question is about making system descriptions specifically for an organization - you should take the system management course, if you need to systematically describe mastery - you should take the personal engineering course. But if that's the case, why take the system thinking course? It briefly explains the type of "system description" and what should be included there. This is a course on meta-meta-model types, while applied courses (including software engineering, personal engineering, organizational engineering) are courses on meta-model types. In real life, to solve problems and check for major mistakes, you need to type the concepts of the domain model with types from the meta-meta-model, that is, types of fundamental methods in the intelligence stack. If your organization model suddenly stops being systemic, you won't learn this from the system management course, how to create a systemic architecture for a software system - from the software engineering course (although we touch on this issue in the system engineering course, we refer there to literature specifically on software engineering). Do not expect answers to questions about applied work methods from fundamental courses, including the system thinking course.
Fundamental courses serve a different purpose. Thanks to system thinking, completely unknown situations to you will become somewhat familiar. People unfamiliar with system thinking will see a web of closely interconnected unclear and hard-to-distinguish objects from the environment, while as a student of system thinking, you will see a set of less familiar, but systems (this is a type of our course's meta-meta-model) connected not just "somehow," but more or less clearly (with understandable relationships: compositions, creations). And from there, you will understand things faster; it's like an unfamiliar situation will be partially familiar to you: you will know the types of important objects in this situation, and therefore you can reason coherently about them.
If you have a managerial problem, you will use organizational engineering, and organizational engineering will involve system engineering, which involves methodology. And as a manager, you will use methodology and solve ethical problems, think rationally and logically. Rarely does one notice that the activity of an agent in the role of a manager involves organizational engineering. They do not emphasize that the personality of this agent thinks logically and is guided in their decisions by the most recent ethical achievements. But that doesn't mean it actually happens in reality. And the agent-manager with the stronger intelligence will be the better manager: they will simply learn the necessary applied methods as they encounter problems. System thinking will help them do that. An agent is a rational person (not a three-year-old child), or today such an agent can be AI, or even a whole organization of humans and artificial intelligences.
Thus, the thinking methods of the intelligence stack (physics, mathematics, logic, methodology etc.) are used in very different applied fields, from musical composition to robotics. We are less concerned with full thinking methods from the intelligence stack (including not only theories/knowledge/disciplines but also thinking support tools) - so often people talk not about the thinking methods but only about the disciplines/theories/knowledge of the intelligence stack. Moreover, these methods are usually named after their theories/knowledge/"educational/scientific disciplines" rather than tools/instruments (although there are exceptions, like "microscopy"), and it's not by chance. The tools to support some method can be easily replaced (for example, replacing one modeler with a completely different one), while the supported theory/discipline will remain.
Systematic thinking - universal, fundamental, scale-less thinking: is used in thinking about very different applied subject areas.
Proficiency in thinking according to the methods of the intelligence stack is necessary for solving problems that have not been encountered by the possessor of intelligence yet. Applied work methods help solve tasks that the possessor of intelligence has already encountered. But as soon as a situation in life differs from a situation described in the applied method or its explanations/discipline (be it plumbing, instrumentation, or genetically modified fish breeding), intellect will be needed - to understand the differences and how to solve the emerging problem.
The intellect, strengthened by fundamental knowledge/theories/disciplines, is universal because it helps deal with problems in very different applied subject areas, from animal husbandry to rocketry. Therefore, the explanations/practices of intelligence stack disciplines are often called transdisciplines, as they are "disciplines for reasoning when applied methods are activated" (trans) - they are on the side "beyond" the applied disciplines, or rather, the applied methods. An overview of contemporary content of transdisciplines will be covered in the course "Intelligence Stack."
These fundamental knowledge/disciplines are scale-less - unlike knowledge tied to specific scales in space and time in applied disciplines. The term "scale-less" came from physics and is used in relation to disciplines/knowledge/explanatory theories where their applicability does not depend on the size of the discussed objects. Fundamental disciplines allow reasoning about elementary particles, people, airplanes, mountains, quasars, and galaxies. System thinking within these fundamental/transdisciplines is not only universal regarding subject areas dealing with different properties of physical world objects, but also scale-less in terms of reasoning about various situations in the physical world, viewed at different size scales - from the microcosm of subatomic particles to the cosmic world of galactic clusters, as well as different time scales - from femtosecond light pulses in lasers to billions of years spent in biological evolution.
Applied disciplines/knowledge/explanatory theories use concepts (like the meta-meta-model type) from intelligence stack transdisciplines to discuss methods of working with systems as physical objects divided into levels of some specific scale/size. These levels "by scale/size" are called system levels. Small molecules form bigger cells, small cells form larger organisms, a multitude of different organisms form huge populations - this is the division of living nature systems into system levels. From screws and metal parts, aircraft engines are assembled, from aircraft engines, fuselages, and wings - airplanes, from airplanes and airports - aviation transport systems. These are system levels in engineering.
Various applied work methods deal with the behavior regularities of systems from different system levels. Molecular biology focuses on proteins, engine building focuses on engines, aviation engineering focuses on airplanes. These system levels are addressed by applied methods/practices/styles/cultures of engineering. Engineering changes the physical world for the better, utilizes thinking about certain types of systems to alter the physical world with the aim of creating and developing targeted systems - engines, airplanes, cows, enterprises, and even societies. For instance, classical system engineering is the engineering of cyber-physical systems (robots, airliners), medicine is the engineering (currently mainly repair) of human beings, education is the engineering (mostly development/modernization) of personalities, management is the engineering of organizations.
Strong intelligence implements the "rescue" ideas of agents who change the world with their engineering to eliminate problems now and prevent them in the future. There is intelligence in anything (agents in the broadest sense), just of varying strength. But intellectual agents are currently only educated people, and modern artificial intelligence systems are getting closer to them. People also join organizations along with AI agents. Humankind has processed 50 kg per square meter of Earth's surface to avoid death from hunger, cold, diseases, to avoid being eaten by wild animals, and even to avoid being destroyed by other humans. Avoiding death from hunger and cold is more than a task, it's not a problem. Regarding diseases - depending on the disease, treating some diseases is still a problem, and some are already tasks; however, biological immortality is still a problem. Destroying fellow beings is currently also a problem, although the situation is much better than it was a couple of thousand years ago.
The meaning of life is "to survive," in the evolutionary sense of the word. Living now and in the future; to do this, stock up on knowledge of all imaginable working methods and then carry out work using the knowledge and tools of work methods in engineering projects to create various systems.
Systemic thinking - this is a subset of thinking, dealing with the emergence from the environment's perspective of new properties of a whole object-system (emergence) resulting from the interactions of these objects' parts. Systemic thinking is based on the concepts of the systemic approach.