Imagine a fleet of many ships, resembling a galaxy — a vast stellar system where every element follows the overall dynamics. From time to time, one ship breaks down, exploding like a supernova — a catastrophic event that can disrupt the integrity of the entire formation. If the damaged unit isn't replaced in time, the fleet loses combat capability. The problem is that predicting the next failure is impossible: failures accumulate invisibly, like dark matter — a hypothetical substance that reveals itself only through gravity but remains inaccessible to direct observation. This hidden growth of faults increases entropy — a measure of disorder in the system — and the challenge is to stay ahead of the onset of chaos.

The traditional approach relies on the Standard Model — here, the familiar scheme of scheduled preventive maintenance on a fixed timetable. However, this method ignores the random nature of failures. In contrast, a decentralized algorithm has been developed: each ship continuously analyzes the actions of its neighbors and, based on that data, autonomously chooses the optimal time for its own repair. The fleet operates without central command — decisions emerge from local interactions, much like a flock of birds coordinating movement without a leader. Experimental testing on a squadron of 70 ships showed that the new approach reduces operating costs by about a quarter compared to classical strategies.