Generating Power Law Distribution of Spatial Events with Multi-agents

The automated generation of events that follows a Power Law (PL) distribution has been extensively researched in order to mimic real world phenomena. Typically. the methods pursuing this goal consist of a unique generating function able to reproduce the inner features of PL distributions. On the contrary, most events that follow a PL distribution are produced through the interaction of different and distributed agents, which are often independent, autonomous, and have a partial perception of the world that surrounds them. In this paper, we investigate the circumstances in which multi-agents. in particular their communication mechanisms, produce spatial events that follow a PL. We are going to focus on models in which the agent's behavior is based on the ant colony optimization algorithm. We show that restricted models of agent communication based exclusively on pheromone exchange require an extension to represent direct communication in order to generate PL data distributions.