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Temptation of complexity

Frequent mistake made by novice systems thinkers - is choosing extremely complex cases for their first attempts at systemic thinking, which will be challenging even for experienced systems thinkers. These situations are primarily related to long chains of information technologies. In programming, the focus is usually not on the physical world, but on manipulating long chains of descriptions stored in databases of various applications, somehow connected to software-implemented algorithms but not clearly linked to the physical world. The course explicitly states that systems are physical. In some schools of thought, non-physical systems are allowed, but not in ours - usually leading not to rigorous reasoning, but rather informal fantasies, detachment from physical reality, lack of grounding. The course will present research on future generations of system thinking, where thinking about parts and wholes for descriptions is allowed. However, the connection to reality is established through the understanding that some physical agent thinks about these parts and wholes. This is indeed a complex case, the edge of systemic thinking, which is not covered in textbooks yet.

Software and databases of this software are descriptions, and these are not always descriptions of the physical world; they are often descriptions of other descriptions. In the case of software, "reaching out to the physical world" often means understanding the engineering of the enterprise that will use the software, implying some knowledge of management. Beginners lack the patience and qualifications to unravel these long chains of descriptions and grasp knowledge of various complex applied engineering systems (including enterprise systems, personal systems). Therefore, warning them to be cautious when working on IT projects does not resonate with them. Consequently, they immediately say, "my target system is a program that implements an algorithm," even though our course discusses numerous mistakes that often occur when making such statements - but these mistakes only become clear a little later and are not initially recognized by the students. The same applies to mastering as a target system because it is also a "program implementing the algorithm of some method," only not for a classical computer but for an agent based on a neural network computer (a human or a robot) and tools. Sometimes the very first target system chosen is a community or even society as a whole: a system consisting of individual agents with strong intelligence, with unclear boundaries. A similarly complex case is the target system "internet" or some other computer network.

This is like a first grader at a music school attempting to play the newest orchestral hit on the piano after hearing it on the radio as their first test - yes, let's try all the instruments in the orchestra, let's try it on my piano, tell me, how should I play it by evening, especially this wonderful drum part! The teacher can only sadly say that it cannot be played now, especially the drum part. Dealing with complex corporate IT projects and human agents is the worst starting point for working with systemic thinking. Unfortunately, this note does not stop many. But it's there, come back and reread it. Surely somewhere in your large work project, there is a small subproject involving individual systems, for which you can easily define their boundaries in space-time (furniture in a room, a physical computer with a power supply, a building), which are straightforward systems for beginners to start with.

By definition, complex organizational projects involve people because people are the most difficult to model, as personalities are much more complex than classical cyber-physical systems. We have a course on "Personality Engineering," where this is discussed in more detail, but it is approached after the course on systemic thinking and methodology, as well as the course on systems engineering. Mastery is a part of the personality, which is in turn a part of the agent. Besides mastery as implemented in the "hardware" of the computer, in the agent, there is also their organism, the actual hardware. Hence, an agent with strong intelligence (e.g., a human) is a software-hardware complex where the personality is the "software," and "mastery" is one of the applications within this software. It is not so difficult, and after a couple of months of studying systemic thinking, this should be clear. However, choosing "mastery" and "personality" as the target system right away leads to many mistakes. It's like getting on a mountain bike and immediately attempting the most difficult trail on the steepest and wildest mountain instead of trying to ride the first hundred meters on a straight road for the first time in your life. It can be done, but be prepared for bruises and fractures.

The organization is the next systemic level after individuals, a more complex level (one must understand the frustrations arising between people and the organization, between the organization and the communities surrounding it, as well as with society as a whole). We have a course on "System Management" that delves deeper into organizational engineering. The main idea there is that functional systems are organizational roles that perform certain methods/practices, and organizational units are departments, committees, project teams. The minimal organizational unit there will be a human or an AI agent. An analog of "mastery" there will be an organization's "capability" to perform work in a role following a certain method. This "capability" will consist of the coordinated and resourced authority of the mastery of the agents forming the organizational unit. So all the complexities of "mastery" remain, but they are multiplied because in the organization, the work of various agents is coordinated when they employ their mastery in work. When they "engage in work," we remember that "method/culture/style/working strategy" differs from the actual "work”? Details on the distinction will be provided mainly in the methodology course that follows the systemic thinking course. Therefore, we do not recommend selecting the organization as the target system. However, we recommend managers not to choose the organization as the target system, as this is a common mistake among them. For a manager who considers the organization their target system, it works fine, but the engineered systems it produces do not. People are taken care of, machines are maintained, computers are running - but there is trouble with the products because they are not the focus of the manager's attention, so the organization is not the target. The target system of the organization is, in fact, the product of the enterprise (including the product that the organization changes with its service) - this should be the target system even for a manager.

To think about all these types of complex systems involving classical computer software and not-so-classical "knowledge software" for agents with strong intelligence (people, organizations, AI agents, collective systems of people, organizations, AI agents), you need certain knowledge and experience. Our courses on systems engineering, personality engineering, and system management (organizational engineering) will help you in this regard. Ideally, further courses on community engineering (including communities such as the enterprise's clients and investors - people who have invested their funds in the enterprise) could help you further, but we currently do not offer such courses; we are still developing them. You will undertake these courses only after completing the "intellectual minimum" along the line of intelligence enhancement (courses on modeling and coherence, systemic thinking, methodology, systems engineering).

Despite that, the majority of the first systems that newcomers to systemic thinking want to think about, turn out to be systems involving software and people because we work in a world surrounded by software and people, and recently even "non-living" beings (AI software, which behaviorally resembles people more than classical computer software). We strive to teach as quickly as possible in our courses how to think about software, people, AI agents, and the organizations created from them, but still, try not to take such systems as your primary targets. It is too complex! You will get confused! A first-grader does not need to solve differential equations; they first need to practice arithmetic!

When it comes to a city (community) or a country (society), the situation becomes even more complex in systemic modeling than dealing with a business. In a business, you can still influence something because there are ways of giving instructions - people are organized. If people are not organized (it is unclear who is the boss and who is subordinate in certain matters, work cannot be delegated), the possibilities for purposeful action of an individual or even a team of people are limited (unless you are the commander of a small armed squad financed by taxpayer funds). Your thinking about communities and societies will be unproductive - the thinking of novice systems thinkers will abound in errors due to the extreme complexity of such situations.

Different agents you want to engineer into your project to create a community or society will have different preferences, and they all already try to do something to realize their desires of varying levels of thoughtfulness; they do not wait for your actions and rightly consider them thoughtless and not up to State-of-The-Art (best known today) working methods if you attempt to control them based on knowledge from textbooks. Just like you, why would not they do the same? If your organization consists of a couple of thousand individuals like Vasya and Dasha, it may not behave as expected - and strong intelligence (including mastering systemic thinking) will help you understand these unexpected behaviors and solve numerous problems.

Besides intelligence, you need some knowledge-based support in thinking about these complex objects. Our courses on system engineering, personality engineering, and system management (organizational engineering) will assist you in this. Ideally, further courses on community engineering (including communities like the enterprise's customers and investors - people who invested their funds in the enterprise) could be beneficial, but we currently do not offer such courses; we are still developing them. You will undertake these courses only after completing the "intellectual minimum" along the line of intelligence enhancement (courses on modeling and coherence, systemic thinking, methodology, systems engineering).

And yet, the majority of the first systems that newcomers to systemic thinking want to think about will turn out to be systems involving software and people because we work in a world surrounded by software and people, and recently, even "non-living beings" (AI software that more closely resembles people in behavior compared to classical computer software). We, of course, strive to teach you as quickly as possible in our courses how to think about software, people, AI agents, and the organizations created from them, but still, try not to take such systems as your primary targets. It is too complex! You will get confused! Come back and reread these warnings: in cases involving a lot of information technologies (including AI agents) and people (often projects include both software and people and AI - with some not even noticing it, "a fish does not notice water," even you yourselves - these are people, and your work is supported by software), be cautious with your first examples of systemic thinking. Do not select such examples. Do not start with your most complicated work project; you will only be disappointed. If you started learning Swahili yesterday, do not engage in a synchronous translation situation at a business meeting of military officials and businessmen; nothing good will come out of such a situation except trouble. Wait until you achieve a certain level of fluency. Practice on simpler projects where there are fewer information technologies and fewer people.

This creates significant challenges with organizing education: a person faces the maximum complexities in their current projects, but they are learning a tool to reduce this complexity, and they should not find themselves thrust into the center of this complexity with their current skills; they must gain a little practice on simpler examples!

Evaluations to achieve an initial fluency in systemic thinking (completion of our course) - require approximately 20-30 active days with two to three hours each day plus about three one-day six-hour sessions with the instructor.

Systemic thinking is not the only course in the intelligence stack that a modern adult needs to complete. We aim for about a year-long effort spanned over two semesters of study (our course is in the second semester of this work[1]).

For such education, a lifestyle change is necessary because these extra hours need to be found somewhere, and your typical daily schedule needs to be rearranged in some way. This is similar to enrolling in a full-time or part-time physical and mathematical school or evening graduate study: it is difficult to work for several years to gain other life opportunities. In essence, learning is an ungrateful pursuit: if you spent the whole weekend from morning to evening studying, there will be no one to praise you - it is not like a job where significant progress can be visibly accomplished in a couple of days. No, you will have to spend many days without immediate rewards. However, this will enable you to aim for other jobs and another level of rewards later. For the systemic thinking course, there is a mandatory prerequisite course on "Modeling and Coherence," which teaches you how to gather yourself and reorganize your life to make time for education (learning the methods of intelligence stack thinking, enhancing intelligence) and practical training in various methods/cultures of working with specific types of systems.

"Expert" is someone who understands everything across the board (primarily based on textbooks, not real-life) and can discuss problems, quoting a textbook to describe a certain method appropriately. An "expert" will not actually do anything but talk.

"Practitioner" is someone who effectively integrates into a project and brings utility to the project, finds agreements with everyone (an indicator of success: managed to coordinate their work with the work of other agents). A "practitioner" must already think about how a project is structured to reach agreements with everyone, so must know how to think about organizations of various types of agents (people, AI, and even other organizations).

"Master" (here we use a different meaning of the term "master" as a qualification level in expertise. Several different meanings of the term "master" are discussed in the course "Personality Engineering") already understands that reaching agreements with everyone is pointless - if the agreements of other agents (people and their organizations) regarding the release of the target system change, their agreements with everyone will change as well. Therefore, a "master" deals with organizational development: manages agreements, changes organizations, conducts enterprise reorganizations, teaches people to do something new. Naturally, this requires the ability to think about people as systems, about systems like AI agents, about systems like organizations of people and AI agents. This is where the qualification of a "reformer" comes in, where it turns out that "you cannot make a clean factory in a dirty puddle," and you will have to get involved in organizing clubs, business ecosystems, communities (clientele) - which will require developed thinking about community systems. A "revolutionary" will think about societies. But for now, let's start with an "expert" and learn to think about some simple systems that are clearly identifiable by their physical objects.

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