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System creativity

Slow, "formal", reasoning thinking, with all its merits, can come across substantial problems, even when people are ready to devote enough time to it. A well-formulated problem usually contains an explicit formal contradiction that needs to be "resolved" --- only at that moment does creative thinking kick in, only at that moment is it necessary to "sit down and think" (rather than "remember and apply", routine, automatic thinking). Sometimes it is said that thinking comes about when you need to "turn problems into tasks", i.e. create a list of tasks that are clearly understood how they are to be accomplished, and that together solve the problem, resolve the contradiction, remove the collisions.

Solving problems by formulating and resolving contradictions is inherent in the theory of constraints of Eliyahu Goldratt ("cloud evaporation"[1]), and the TRIZ methodology of Genrikh Altshuller[2], and the system-thoughtful methodology of Georgy Shchedrovitsky's school[3]. All these schools of thought affirm that they are based on a system approach, hence the commonality of thinking techniques.

Where do contradictions come from in general? They can be of two types:

  • Contradictions in terms of logical contradictions, formal incompatibility of statements within the framework of some logic. This can be Boolean logic with two conflicting statements (x=3 and x=5, with both statements being true simultaneously!), but there can be probabilistic Bayesian logic, in which according to the works of E.T.Jaynes, Boolean logic is a special case[4]. Furthermore, unsolvable Bayesian-logical (Bayesian inference) contradictions can also occur in Bayesian logic, then it is necessary to calculate using a quantum-like logic, this is described in the works of Andrei Khrennikov[5].
  • Contradictions/conflicts in interactions between systems of different levels, leading to disorder and as a result the complexity of the system's structure at these levels. A good overview of how it is structured in biology can be found in the work of Wolf, Katznelson, Kunin "Physical foundations of biological complexity"[6], 2018 --- and it would be good to read it, reading literature related to this article, otherwise much remains unclear. In 2022, these authors together with the physicist Vanchurin came up with the idea that these contradictions/conflicts between system levels, leading to disorder at these levels and generating evolution, where the world/universe is a physical neural network and "learns/explores itself". Rather, it should be said that some parts of the world (systems) explore/learn other parts of the world (systems), and thermodynamic balance is not disrupted because knowledge in these well-explored parts of the world can be "unlearned/forgotten", but evolution still prevails and other parts of the world acquire a lot of knowledge[7]. Evolution mainly occurs with non-ergodic systems, i.e. systems with memory. In the evolution's slowest changing levels, memory becomes digital like precise in multiple copying (without errors accumulating) memory, for example, DNA is such a "digital memory". In the case of memetic evolution in humans for memory provision and accurate copying, speech and writing serve as means, allowing to accumulate and endlessly copy knowledge without distortion. The same happens in technological evolution: ideas underlying devices manifest in their design. The central dogma of molecular biology, that information goes from genotype to phenotype, from information models in R&D units to product instances in one direction, and in the other direction totally different modes of transmission (not "biological growth" or "factory manufacturing", but mutations, engineering design and other genotype changes methods --- but definitely not by copying adaptations of the phenotype) was supported by ideas from physics-mathematics in the 2022 work "Toward a theory of evolution as multilevel learning"[8].

And creativity can also be different: if it is about a logical/mathematical contradiction, then you can rewrite the world in terms of some single mathematics/logic, not different ones (by focusing on different objects of attention, different concepts --- switching to another "logic"), and thus resolve the contradiction. For example, if you have concluded that x=3 and x=4, you can consider the physical meaning of the written text and discover that in the first and second cases, different units of measurement were used. This is a simple case: describing the world in the same units of measurement to avoid contradictions! Another simple case: if you find out that the same person is both a physicist and a manager, it does not necessarily mean two different entities, but simply two different descriptions. Describing expertise as professional skills, thus making expertise part of the functional decomposition of a person’s identity, where expertise will be a component of the personality's functional breakdown, and the person’s personality is the functional part of a person, their "software", and the person also has an organism, their "hardware". People are described as agents playing roles --- and the contradiction turns out to be resolved.

If it is not a logical/mathematical contradiction, but a conflict between systems of different levels, then the behavior of these systems will have to be changed. Much depends on how capable these systems are as agents. If it's a mechanical part, its construction will have to be changed mechanically. If it’s a cat --- with higher intelligence, higher capability, it can be trained. If it's a person with strong intelligence, then the capability is even higher, and it can be "convinced", persuaded by rational arguments, change with neurolinguistic programming (text!) the "software" only, not the "hardware". This is described in more detail in the course "Intellect-stack". Changes in the description won't help here. If the conflict lies in the fact that a virus, by its survival-related behavior, conflicts with an organism that also needs to survive somehow, then the solution will be the emergence of an immune system in the organism and the presence of variability in the virus, and a huge variety of implementations of the immune system and methods of implementing variability in the virus to deceive the immune system. This is an evolutionary solution. The immune system acts as a "non-mental brain"[9]. This is all life, Darwinian evolution, where the genome is in every cell.

An alternative solution is engineering, in the order of techno-evolution. If you are an engineer and have found a contradiction in the surrounding world, you can create a new mechanism or modify an existing one, propose a smart mutation to the meme. A meme is like the "genome of a common type", a meme is like a gene, but not necessarily in DNA. Not only do humans think about such solutions, they can also arise spontaneously, evolutionarily in the course of memetic and technological evolution. For example, memeviruses (ideas that people find interesting for dissemination) can be the same as regular viruses: controlling people's behavior (in nature, such viruses that change the behavior of the host are called "zombies", and there is also rabies --- a virus that changes the behavior of the infected to aggressive, increasing the spread of the virus). John Doyle says that humanity needs to think engineering-wise about the immune meme system, rather than waiting for meme viruses to destroy mankind[10].

An engineered solution that reduces the number of deaths is an example of democracy. When there were no democracies (elections for a non-violent change of dictator), power either didn’t change at all (some kind of autocracy), or the question of power was resolved by a civil war --- because "God is always on the side of the big battalions" when the armament and level of knowledge are equal. The change of power happens, but quite a lot of people die in doing so. Thus, an engineering solution emerged: instead of combat, a vote was conducted to determine whose forces are stronger[11]. This can be considered as “evolution”, but the solution itself got invented and approved by people, so it can confidently be considered one of the solutions of social engineering. But there can be many different variations for problem-solving, and the complexity of society has increased and problems have not disappeared, with democracy simply changing the problems! But with the change of power, the people no longer die simply for the power to be changed, there are not very many monarchies left on Earth, and in many cases, monarchs have ceased to have real power and power turns out to be elective. Of course, this "virus of bloodless power change" did not reach all brains on the planet, but progress is evident. And for what purpose to change power? Karl Popper provides an answer to this question: if everyone is satisfied with the power, then it's OK. If it is no longer satisfactory, there was an error in the last choice, then democracy provides a bloodless way to correct the mistake. The public choice error correction mechanism is what's essential in democracy!

Engineered solutions as "smart mutations for the techno-organism” are based on the knowledge that contradictions/conflicts in system levels are inevitable, but optimizing these contradictions/conflicts for all system levels leading to overall reduction of disorder/frustrations through the growth of system complexity can be achieved through engineering design, not through trial and error of evolution.

System thinking does not specify how to resolve contradictions. In our course, there are no methods for creative thinking, decision tables, methods for conducting brainstorms or ways of developing imagination (but they might be in other courses. For example, methods for developing creative imagination are used within applied systems engineering methods under the general name of TRIZ).

Miracles do not happen, thinking during problem solving is required not less and not more than in any other schools of thought. System thinking allows understanding and accepting the inevitability of problems through disorders/frustrations from conflicting interactions of different system levels, and also, during problem solving, it preserves the vision of the entire system both on a few levels above it and on a few levels within it. System thinking enables solving problems without losing sight of the forest for the trees, not losing sight of the leaves for the tree, not losing the tree leaf for plant cells.

System thinking allows targeting finding contradictions, demanding their solutions, and documenting these solutions. Spotting an important contradiction, not letting it slip by, not allowing it to be ignored --- that is the task of system thinking. And then, it's necessary to take have strategical thinking (and often also a modeler in the computer) in hand and think, using different other methods.

How does it work? In the real world, agents don’t quite understand how to segment it into objects. Here's a metaphorical example --- try to name the objects you see and pinpoint the important objects:

Next comes the cycle of quick generation of guesses and the laborious critique of them. During that, guessing/explanations/knowledge/theories are generated in the form of generative models --- future world states can be predicted based on these models, and criticism follows using discriminative models, on which class membership can be determined (thus finding the "logical contradiction"). In the picture is the cycle of cognition/creativity, which is explained in more detail in the course "Intellect-stack", although it's also explained without this picture. However, its various elements are discussed in various courses of the Systems Thinking School, here we'd only note that focusing on system objects/relations comes at the formalization step in the cycle:

This all means that system thinking implies not only informal reasoning within connectivist/neural network/S1 representations and their expression through the theory of prototypes (metaphors), but also quite formal reasoning using sign-based representations of theoretical concept theory, where we talk about objects and relations, and in which all objects and relations have types. Don't get carried away by the fact that our course text is in natural language: the course's text is quite type-driven. So, we don't confuse "work method" and "work", "system" from the system approach and "system" in the Stanislavsky system. In system thinking, the primacy of working with objects and relations using logic plays a key role, rather than using metaphors and analogies with artistic elements. Keep track of the types of objects and relations, they are important!

In engineering, they sometimes discuss "foolproofing": the impossibility of incorrect activation of certain items. System creativity is then about making sure the "affordance" not only fits for the required function but also doesn't fit for the unwanted[12]:

In any case, system thinking is primarily creative thinking. Of course, it is not careless creativity, with random ideas. No, randomness (after all, evolution is based on mutations) still has a place in system thinking, but we strive to make them smart mutations/considered mutations, with the highest probability leading to the success of the system[13].


  1. https://en.wikipedia.org/wiki/Evaporating_Cloud ↩︎

  2. https://ru.wikipedia.org/wiki/Теория_решения_изобретательских_задач ↩︎

  3. http://www.fondgp.ru/ ↩︎

  4. http://www.med.mcgill.ca/epidemiology/hanley/bios601/GaussianModel/JaynesProbabilityTheory.pdf ↩︎

  5. https://scholar.google.com/citations?hl=en&user=wdhkzPMAAAAJ&view_op=list_works&sortby=pubdate, see a brief story about quantum-like rationality in https://www.preprints.org/manuscript/202103.0395/v1 ↩︎

  6. https://www.pnas.org/doi/10.1073/pnas.1807890115 ↩︎

  7. https://artificialneuralcomputing.com/wann ↩︎

  8. https://www.pnas.org/doi/10.1073/pnas.2120037119 ↩︎

  9. https://www.frontiersin.org/articles/10.3389/fnint.2023.1057622/full ↩︎

  10. https://www.youtube.com/watch?v=Bf4hPlwU4ys, and more references to his ideas at https://ailev.livejournal.com/1622346.html ↩︎

  11. https://ailev.livejournal.com/1616855.html ↩︎

  12. https://sketchplanations.com/affordance ↩︎

  13. https://arxiv.org/abs/2206.08896 ↩︎