Group abstraction for large-scale agent-based social diffusion models with unaffected agents

Abstract

In this paper an approach is proposed to handle complex dynamics of large-scale multi-agents systems modelling social diffusion processes. A particular type of systems is considered, in which some agents (e.g., leaders) are not open to influence by the other agents. Based on local properties characterising the dynamics of individual agents and their interactions, groups and properties of the dynamics of these groups are identified. To determine such dynamic group properties two abstraction methods are proposed: determining group equilibrium states and approximation of group processes by weighted averaging of the interactions within the group. This enables simulation of the group dynamics at a more abstract level by considering groups as single entities substituting a large number of interacting agents. In this way the scalability of large-scale simulation can be improved significantly. Computational properties of the developed approach are addressed in the paper. The approach is illustrated for a collective decision making model with different types of topology, which may occur in social systems. © 2011 Springer-Verlag.