Selfformations

Selfformations, a term originating from discussions in philosophy and artificial intelligence, refers to the emergent self-organization of systems. It describes a process where a system develops complex structures and behaviors without external direction or pre-programmed instructions. This often occurs through local interactions between the system's components, leading to global patterns.

Examples of selfformations can be found in nature, such as the flocking behavior of birds, the schooling of fish, or the formation of ant colonies. In these cases, individual agents follow simple rules, and their collective actions result in sophisticated emergent properties. In artificial systems, selfformations are observed in cellular automata, genetic algorithms, and certain types of artificial neural networks. The concept is closely related to self-organization, emergence, and complexity theory. Understanding selfformations is crucial for designing adaptive and robust systems, both natural and artificial, and for understanding the fundamental principles of how order can arise from disorder. The focus is on the system's inherent capacity to configure itself into more complex and functional states.