Speeding up the Search of a Global Dynamic Equilibrium from a Local Cooperative Decision.

Systems composed of many interdependent active entities working on shared resources can be challenging to regulate and multi-agent simulation is an efecient means for ending the suitable entities' behaviors. On the other hand, the search space for a stable solution of the system is usually forbidding and hinders any effort to solve the problem using a top-down approach. Furthermore, the complexity of possible global functions to optimize increases rapidly with the size of the system and can prove difecult to deene and/or evaluate at each system simulation step. The difeculty when designing bottom-up systems is to be able to identify all their emergent properties and the parameters to modulate them. Here we propose a local cooperative decision making process that helps to stabilize such systems. These local processes prove to be very efecient to quickly end dynamic equilibrium solutions where the system continues to function and fulells its global function. Regulation emerges from simple local interactions.