A Tale of two Wells: Noise-Induced Adaptiveness in Self-Organized Systems

One of the core aspects that make self-organized systems an interesting engineering paradigm is their potential to behave adaptively. Unravelling the fundamental mechanisms that drive this adaptiveness is of prime importance for understanding and designing such systems. The present paper demonstrates that noise is one of the core ingredients that enables self-organized systems to behave adaptively. This suggests that noise should be taken into account as a constructive component when engineering them.Our study analyses two different but closely related self-organized systems: a man-made system, Ant Colony Optimization algorithms (ACO), and real ant colonies, the natural system that inspired ACO. We demonstrate that the conventionally used mean-field analysis is not a correct description of their behavior in dynamic environments. This can only be achieved by a stochastic analysis that quantitatively takes noise into account. We present such an analysis based on Ito-Diffusions and Fokker-Planck equations and show it to be consistent with experimental data.Real ant colonies and ACO are both controlled by coupled self-limiting feedback loops. Decision making in such systems can be understood as stochastic attractor switching. This is the basis of our analysis. As coupled feedback mechanism are a universal control mechanism found in many types of self-organized systems, we expect our approach to be applicable to a vast array of other natural and man-made self-organized systems.