Putin’s Algorithm

Yuri Barzov
6 min readMar 26, 2022
Picture from The Sun

Putin’s algorithm is damn simple and yet, at times, devilishly effective. However, it can be calculated, and it is very important to calculate Putin today for all of us.

To understand how Putin works, we have to apply the theory of the Universe as a neural network (1,2,3), in which the problems of consciousness, intelligence and machine learning are considered from the standpoint of fundamental physics. In addition, we use behavioral control models in biological neural networks of phase states, also based on theoretical physics (4,5,6).

Let’s start with a few initial statements.

First, Putin is a flat human. He is an absolutely shallow neural network. To see that, it is enough to look at the authoritarian state that he built.

Authoritarianism, and, moreover, totalitarianism is a pyramid of shallow neural networks that tries to copy a deep (multilayered) neural network. However, it lacks the most fundamental feature of a deep neural network — the backpropagation mechanism, enabling a deep network to learn from prediction mistakes.

Secondly, flat creatures with shallow neural networks are ubiquitous in nature. There are a huge number of species that have adapted well to their niches and successfully exist in them for a long time, without learning, developing or evolving in any way. They all use the same survival algorithm. This is Putin’s algorithm.

Thirdly, 3D people who are deep neural networks habitually believe that all humans are 3D. Hence the overestimation of the abilities of flat people (shallow neural networks). Primitive, in fact, actions of flat people are perceived as the result of deliberation, that is, learning, which shallow neural networks are in principle not capable of.

Let’s now look at Putin’s algorithm itself. It only includes two modes: (i) a prey search mode and (ii) an attack/flight mode.

In the first mode, Putin makes a random choice between several predefined states at a level of free energy close to the minimum. Randomness in his moves is constrained by the prohibition on moving straight backwards. Putin cannot reverse his decision, even if he really wanted to. His neural network simply does not have the necessary functionality.

Backward move prohibition turns a random drunken walk into a biased drunken walk (Levy walk). As a result, the route of Putin’s movement across the decision making plane begins to repeat itself. He draws either small pretzels with small moves or large pretzels of the same shape with large moves. It’s called a fractal.

Such a simple prey search strategy allows him to quickly cover the entire search area with minimal energy costs. Moreover, it allows him to adapt to the environment without the need to create a predictive model of it. It is impossible, whoever, to learn new patterns of behavior successfully without a predictive model.

Can you imagine a hedgehog picking mushrooms in the fog and finding them all, even though he can only smell the mushroom from a very short distance away? Putin is such a hedgehog.

When Putin the hedgehog smells prey, his behavior changes. The attack/flight mode gets activated. An instant very strong move follows in comparison with the previous moves.

If the probability of moves in the prey search mode was determined by a dense spot of the most likely moves according to the Gaussian diffusion of probabilities, now the moves become much less predictable. The choice of moves in the attack/flight mode is determined by the power law probability diffusion, the same one from which, according to the idea of ​​Nassim Nicholas Taleb, black swans arise.

If the prey is not caught on the first attempt, a few weaker power law probability moves will follow. With each move, the strength of the move weakens because the free energy accumulated during the prey search mode is spent in power law moves very quickly.

As soon as free energy is exhausted, Putin returns to the search for prey mode. As we remember, the consumption of free energy in this mode is minimal. Putin simply has no choice but to fall into this mode. He begins to draw pretzels with his moves according to the fractal template.

If in the period immediately after the attack/flight mode is expired, when the free energy is exhausted, Putin senses a prey or a threat, he will still draw pretzels with standard easy-to-calculate decisions which he has made many times before in the same sequence.

Does he threaten to use an atomic bomb? Has he made such a decision before? Is this routine for him? If not, then there is nothing to worry about until he has accumulated a level of free energy sufficient for him to turn on the attack/flight mode.

It is understandable why Putin’s henchmen consider him a genius. For all its simplicity, even primitiveness, Putin’s algorithm is extremely effective.

The sea angel snail thus manages not only to routinely feed itself, but also to get stuffed with sea devils (the only food of the angel) for the future in order to create a layer of fat for several months of fasting until the next hunting season.

3D people are also surprised at Putin’s luck because they endow him with the ability to think and learn. They begin to think about what they could do in Putin’s place and begin to fear Putin’s phantom abilities, which in principle real Putin cannot have.

A lot of very knowledgeable scientists, for instance, still believe that bacteria move along a gradient towards food and away from poison, but in reality, bacteria simply switch between two modes like Putin (7).

One important point. Putin cannot lose his mind because for this he will have to reset his knowledge (memorized patterns of behavior) to zero, but he knows that he cannot acquire new knowledge. Therefore, he will hold on to what he has to the last of his strength. Shallow neural networks don’t lose their mind.


  1. Flat people and 3D people as name tags for shallow and deep neural networks are borrowed from the science fiction story Without Sky written by Vladislav Surkov, a Putin’s henchman personally responsible for the Russian aggression against Ukraine. The story under the pen name Natan Dubovitsky was published in 2014 when the Russian invasion into Ukraine began. Flat (2D) people in the story launch an attack at 3D people: “We will come tomorrow. We will conquer or perish. There is no third way.” (8)
  2. According to the theory of the Universe as a neural network, the more multi-layered (deep) a neural network cluster is, the faster and better it is able to learn. Flat (shallow) neural networks are practically devoid of learning ability. Since the neural network of the universe has a fractal (self-similarity) structure, its individual clusters are also self-similar within the cluster. The Russia cluster is similar to the Putin cluster and vice versa.
  3. It is very important to see the difference between what Putin thinks and says and what he can do. The decision-making algorithm in a shallow neural network is set rather rigidly, although it is based on a random choice. Randomness in this mechanism has clear and understandable constraints. Therefore, Putin can want anything, but he can only do what has an execution machinery under it. It is this mechanism that we have seen now. And we now know its limitations.
  4. A mathematical formalism underlying Putin’s algorithm was developed on the basis of animal models (4).Therefore, we understand rather well how Putin works.
  5. We cannot predict what Putin will do, but we can predict with a high degree of certainty what he will not be able to do. Of course, no one can give a 100% guarantee. But this is by far the best scientific explanation we have.


  1. Vitaly Vanchurin, “Towards a theory of machine learning”, 2021 Mach. Learn.: Sci. Technol. 2 035012, https://iopscience.iop.org/article/10.1088/2632-2153/abe6d7
  2. Vitaly Vanchurin, Yuri I. Wolf, Mikhail I. Katsnelson, Eugene V. Koonin, “Towards a Theory of Evolution as Multilevel Learning”, PNAS February 8, 2022 119 (6) e2120037119 https://www.pnas.org/content/119/6/e2120037119
  3. Vitaly Vanchurin, “The World as a Neural Network”, Entropy 2020, 22(11), 1210; https://doi.org/10.3390/e22111210
  4. Latorre R, Levi R, Varona P (2013) Transformation of Context-dependent Sensory Dynamics into Motor Behavior. PLoS Comput Biol 9(2): e1002908. https://doi.org/10.1371/journal.pcbi.1002908
  5. Brad K. Hulse, Hannah Haberkern, Romain Franconville et al. A connectome of the Drosophila central complex reveals network motifs suitable for flexible navigation and context-dependent action selection. bioRxiv preprint doi: https://doi.org/10.1101/2020.12.08.413955; December 22, 2020
  6. Buhl, E., Kottler, B., Hodge, J.J.L. et al. Thermoresponsive motor behavior is mediated by ring neuron circuits in the central complex of Drosophila. Sci Rep 11, 155 (2021). https://doi.org/10.1038/s41598-020-80103-9
  7. Daniel J. Webre, Peter M. Wolanin, Jeffry B. Stock (2003) Bacterial chemotaxis. DOI: https://doi.org/10.1016/S0960-9822(02)01424-0
  8. Without Sky by Natan Dubovitsky, Russian Pioneer, No 46 (May 2014) http://www.bewilderingstories.com/issue582/without_sky.html