The constrained disorder principle emphasizes the importance of variability boundaries for systems to function...

The Constrained Disorder Principle (CDP) defines systems by their inherent disorder, which is bounded by dynamic borders. This principle determines a system's functionality and efficiency based on its continuously changing boundaries. In this paper, we present the formulation of the principle using the equation B = F, where B represents the dynamic borders, and F denotes the system's function. This equation suggests that the dynamic borders shape a system's existence, functionality, and efficiency. However, these borders impose a limit beyond which the system cannot further enhance its performance. When disorder surpasses established limits, the system's efficiency begins to decline. Conversely, insufficient disorder may also be harmful in certain situations. The paper examines the causal relationship between disorder and function, illustrating how the equation reflects the system's adaptability, efficiency, learning capabilities, memory, energy consumption, aging, and eventual termination. We also discuss how this formula can be applied to correct malfunctions and enhance system functions. Furthermore, we introduce a second-generation artificial intelligence system based on the CDP formula that incorporates noise. In summary, the B = F equation provides a valuable framework for understanding complex systems and lays the groundwork for models designed to enhance system performance.