Systems composed of many interdependent active entities working on shared resources can be challenging to regulate and multi-agent simulation is an efécient means for énding 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 difécult to deéne and/or evaluate at each system simulation step. The diféculty 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 efécient to quickly énd dynamic equilibrium solutions where the system continues to function and fulélls its global function. Regulation emerges from simple local interactions.