Evolution of cooperation in malicious social networks with differential privacy mechanisms

Cooperation is an essential behavior in multi-agent systems. Existing mechanisms have two common drawbacks. The first drawback is that malicious agents are not taken into account. Due to the diverse roles in the evolution of cooperation, malicious agents can exist in multi-agent systems, and they can easily degrade the level of cooperation by interfering with agent’s actions. The second drawback is that most existing mechanisms have a limited ability to fit in different environments, such as different types of social networks. The performance of existing mechanisms heavily depends on some factors, such as network structures and the initial proportion of cooperators. To solve these two drawbacks, we propose a novel mechanism which adopts differential privacy mechanisms and reinforcement learning. Differential privacy mechanisms can be used to relieve the impact of malicious agents by exploiting the property of randomization. Reinforcement learning enables agents to learn how to make decisions in various social networks. In this way, the proposed mechanism can promote the evolution of cooperation in malicious social networks.