Journal of Experimental & Theoretical Artificial Intelligence

This paper investigates excitation information propagation in artificial societies. We use a cellular automaton approach, in which it is assumed that social media is composed of tens of thousands of community agents, where useful (innovative) information can be transmitted to the closest neighbouring agents. The model's originality consists of the exploitation of artificial neuron-based agent schema with a nonlinear activation function to determine the reaction delay, the refractory (agent recovery) period and algorithms that define mutual cooperation among several excitable groups that comprise the agent population. In the grouped model, each agent group can send its excitation signal to the leaders of the groups. The novel media model allows a methodical analysis of the propagation of several competing innovation signals. The simulations are very fast and can be useful for understanding and controlling excitation propagation in social media, planning, and social and economic research.