The multiplexing process ensures scalability by being scale-independent—it applies uniformly regardless of the state or context of an instance
Key Points on Scale-Independence in Multiplexing
- Time-Division Multiplexing: The holonic structure uses a time-division multiplexing (TDM) approach where executional focus is quantised into arbitrary units, allowing a hierarchical structure to be iterated cyclically.
- Instance-Tree Structure: The instance-tree is structured such that attention/execution flows downward to child holons and upward for aggregation and feedback
- Independence from Depth or Width: The process itself does not depend on the scale of the structure, meaning it operates the same whether there are two or two million holons.
- Self-Similarity: Since each holon contains the same fundamental organizing principles, the system recursively applies the same logic at all levels, making it intrinsically scalable.
Thus, the key insight is that scalability emerges as a direct consequence of scale-independence. This is a crucial property that allows holonic AI and self-organizing systems to function coherently across varying levels of complexity without requiring centralized oversight.