A social metrics based process model on complex social system

In previous work, we investigated the performance of Cultural Algorithms (CA) over the complete range of system complexities in a benchmarked environment. In this paper the goal is to discover whether there is a similar internal process going on in CA problem solving, regardless of the complexity of the problem. We are to monitor the “vital signs” of a cultural system during the problem solving process to determine whether it was on track or not and infer the complexity class of a social system based on its “vital signs”. We first demonstrate how the learning curve for a Cultural System is supported by the interaction of the knowledge sources. Next a circulatory system metaphor is used to describe how the exploratory knowledge sources generate new information that is distributed to the agents via the Social Fabric network. We then conclude that the Social Metrics are able to indicate the progress of the problem solving in terms of its ability to periodically lower the innovation cost for the performance of a knowledge source which allows the influenced population to expand and explore new solution possibilities as seen in the dispersion metric. Hence we present the possibility to assess the complexity of a system's environment by looking at the Social Metrics.