Understanding social behavior evolutions through agent-based modeling

Agent-based social simulation as a computational approach to social simulation has been largely used to explore social phenomena. The purpose of this paper is to describe a theoretical model of transmission and evolution of social behaviors in a network of artificial societies (artificial world) using agent-based modeling technology. In this model, each agent (society) is subdivided into social behaviors where individual and social learning occur. The agent-agent interactions are carried out by their social behaviors; otherwise the agent-environment interactions through consumption of ecological resources by its social behaviors in repression and satisfaction. We distinguish social behaviors by their repressive capacity and their technical satisfaction. Preliminary results of the model generate several evolutions, but we will focus on the two most important types: firstly, evolutions where the system (all living-agents) will end in a state of "globalization"; i.e. where one social behavior predominates the entire system; secondly, evolutions where an Ecological Hecatomb takes place during the globalization with the repressive social behavior. The model is implemented in java language; its simulation can help to understand the implied processes in humanity's evolution and their trajectories.