Artificial evolution for the detection of group identities in complex artificial societies

This paper aims at detecting the presence of group structures in complex artificial societies by solely observing and analysing the interactions occurring among the artificial agents. Our approach combines: (1) an unsupervised method for clustering interactions into two possible classes, namely in-group and out-group, (2) reinforcement learning for deriving the existing levels of collaboration within the society, and (3) an evolutionary algorithm for the detection of group structures and the assignment of group identities to the agents. Under a case study of static societies - i.e. the agents do not evolve their social preferences - where agents interact with each other by means of the Ultimatum Game, our approach proves to be successful for small-sized social networks independently on the underlying social structure of the society; promising results are also registered for mid-size societies.