Dynamic sanctioning mechanism for cooperative multi-agent systems

Coordinating multi-agent systems to accomplish complex tasks presents a profound and unprecedented challenge, introducing significant uncertainty into the operational framework of collective artificial intelligence. Addressing this formidable challenge requires collective actions of cooperation and concerted efforts on a global scale. However, progress in addressing this challenge through voluntary contributions has been regrettably slow, highlighting the need for the participation of robust sanctioning mechanisms to drive meaningful change. Here, we propose a dynamic sanctioning framework that relies on adjusting between positive and negative incentives based on the collective status of the population. We show that the transition of sanctioning institutions from punitive measures to rewarding mechanisms can effectively sustain a high level of cooperation, even when the risk of collective action failure is low. The threshold at which sanctioning institutions choose to switch incentives plays a crucial role in shaping evolutionary outcomes. Moreover, we provide further evidence that the success of the reward mechanism is based on the presence of self-interested altruism, in which the implementing authority also benefits from the incentives.