This research addresses synthetic agents as autonomous software entities, capable of managing social relationships in small scale societies. An individual architecture is structurally designed as enabling primate-like social organization, which is in turn individually modulated by an affective action-selection mechanism. The aim is to improve agent social reactive, and social cognitive capabilities, by implementing plain communication conveying behavioral rewards or sanctions. This artificial society simulation is being developed as an experimental model aimed at exploring the nature of (1) the adaptation of inter-agent social norms, (2) individual behavioral arbitration, and the (3) interplay of reaction and deliberation. This computational outlook on social cognition offers a contrast with traditional socio-unaware action-selection systems, frequently based on function optimization of decision-making processes . To anthropomorphize the model, social networks are analyzed in terms of situated agents and their internal states. Individuals are able to recognize current counterparts and have their community size dependent on accumulated experiences ; thus food and relationship management become crucial individual tasks. However, this work does not seek an ethologically realistic approach like  - nor does it aim at a complete account of animal or human language interaction. It rather argues for a simpler alternative to represent synthetic social intelligence. By interleaving processes of reaction and planning, agents are expected to act following their individual modulation of pre-configured abilities - dealing both with passive objects (resources) and other active characters (agents). Finally, to interpret interactions and the operation of affective feedback, essential observations and analysis are required on the (1) administration of basic social constraints and (2) processes producing social change, relating invidual behavior choices to group dynamics . © Springer-Verlag Berlin Heidelberg 2006.