Dynamics in the normative group recognition process

This paper examines the decentralized recognition of groups within a multiagent normative society in dynamic environments. In our case, a social group is defined based on the set of social norms used by its members. These social norms regulate interactions under certain situations, and situations are determined by the environmental conditions. Environmental conditions might change unexpectedly, and so should the notion of social group for each agent. Consequently, agents need mechanisms to adjust their notion of group dynamically and accordingly the agents with whom it is socially related. In this work we analyze how different algorithms (whitelisting, blacklisting, labelling), that allow agents to recognize the others as members of a certain social group, behave in these dynamic environments. Simulation results are shown, confirming that the limited memory approach reacts better against environmental changes. Moreover we compare two approaches that regulate the adaptation of the relevance of norms and the notion of group: the unlimited normative memory and the limited memory.