  Agent based simulation of social organizations, via the investigation of
agents' training and learning tactics and strategies, has been inspired by the
ability of humans to learn from social environments which are rich in agents,
interactions and partial or hidden information. Such richness is a source of
complexity that an effective learner has to be able to navigate. This paper
focuses on the investigation of the impact of the environmental complexity on
the game playing-and-learning behavior of synthetic agents. We demonstrate our
approach using two independent turn-based zero-sum games as the basis of
forming social events which are characterized both by competition and
cooperation. The paper's key highlight is that as the complexity of a social
environment changes, an effective player has to adapt its learning and playing
profile to maintain a given performance profile
