An Infection-Based Mechanism for Self-Adaptation in Multi-agent Complex Networks

Distributed mechanisms that regulate the behavior of autonomous agents in open multi-agent systems (MAS) are of high interest since we cannot employ centralized approaches relying on global knowledge. In actual-world societies,the balance between personal and social interests is self-regulated through social conventions that emerge in a decentralized manner. As such, a computational mechanism that allows to engineer the emergence of social conventions in MAS can become a highly promising tool to endow open MAS with self-regulating capabilities. To this end we propose a computational self-adapting mechanism that facilitates agents to distributively evolve their social behavior to reach the best social conventions. Our approach borrows from the social contagion phenomenon: social conventions are akin to infectious diseases that spread themselves through members of the society. Furthermore, we experimentally show that our mechanism helps a MAS to regulate itself by searching and establishing (better) social conventions on a wide range of interaction topologies and dynamic environments.