Universal active agent: multicomponent vector presentation.

Recently, some agent-based models have been used to describe collective social behavior. Often, the rules chosen to represent agents are somewhat arbitrary. In this work, we attempt to describe evolving agents represented by multicomponent vectors with components defining attitudes to different social norms and behaviors. Each agent can represent an individual or a social group. The system of the vectors is initially established at the very basic state reflecting the averaged or simplified norms of the society. Then the agents are allowed to interact with a reservoir representing much richer types of social norms and behaviors. Thus, the system of agents is allowed to evolve until it reaches a steady state. The Monte-Carlo method is used to represent the interaction and evolution of the agents. Then the system of the agents is disconnected from reservoir and studied by itself. The system of agents prepared in such a way displays rich behavior that explains a number of social phenomena related to the influence of propaganda.