Disorderliness and meta-system transition ​

Evolution: the process of changes—"physical evolution of the universe" is a universal phenomenon, but we will still divide it into separate variants:

• Biological Evolution. Complexification progresses from molecules to cells, from cells to organisms, from organisms to populations, and so on.
• Memetic Evolution. This is the evolution of concepts-memes (meme), which exist in symbolic local representations (one place—one symbol) as well as in distributed representations (connectionism) such as neural networks.
• Technological Evolution. Mutations of technical systems are primarily produced by people through inventions. We have seen developments in electric transport, robotics, plumbing, skyscrapers, and smartphones. However, older objects like forks, spoons, and hammers have remained relatively unchanged.

In our course, we will not delve into the mechanisms of evolution, although we have already mentioned in the section "2. Our variant of systems thinking: the third generation" enough materials for curious students to understand the physical nature of the processes happening. The main idea there is that the concept of frustration leads to a huge variety of quasi-stable configurations of evolving systems, where each configuration is quasi-optimal. The transitions between quasi-optima are extremely easy, making the entire set of configurations unstable and non-equilibrium.

The concept of frustration is required to discuss the various forms of evolution (not to be confused with psychological "frustrations," as the term has many meanings in English, and the original term was "geometrical frustration"). The article "Physical foundations of biological complexity" explains the concept of frustration with illustrations.

The evolution of systems across different system levels leads to the emergence of new system properties at each level due to emergence. Therefore, methods of working with these systems, the language for discussion, and the explanations of the functions and workings of these systems change with each level. It is essential to use an adequate language for discussion and change explanatory theories when transitioning from one level to another. Metasystem transitions along the line of increasing observable system levels arise during evolution. The growth in the number of system levels occurs due to inevitable frustrations between the system levels, where only certain system configurations, most accurately solving the multi-level optimization task, survive.

The interaction of physics, learning, and evolution shows that thermodynamics, learning (such as deep learning in AI), and evolution describe laws governing the same processes present in nature. This indicates that thermodynamics, learning, and evolution are different descriptions in different terminologies of approximately the same set of regularities, the same process occurring in nature. Thus, systems thinking, which discusses the emerging multi-level complexity and diversity in somewhat stable systems, is suitable for thinking about a wide range of life phenomena.