Today's systems typically do not stand alone in isolation. Often a system fits within a System of Systems, a network of interconnected systems that often exhibits unpredictable behavior. This study is motivated by the challenges of understanding the emergent system level behavior of System of Systems given the opaque characteristics of social processes and continuously changing operating and environmental conditions. An artificial life based framework for modeling System of Systems is presented as an analysis technique. The framework comprises cognitive architectures embedded in multi-agent models. Financial markets are selected as an analysis domain to demonstrate the framework since they are a good example of self-organizing systems that exhibit System of Systems characteristics, specifically emergence on a grand scale. The effects of different mechanisms on system level market dynamics are analyzed. In particular, the effects of the covering mechanism, learning mechanism and bias mechanism are analyzed. A trader-based architecture is proposed to formulate a trader decision model that combines bias mechanisms with learning mechanisms. A prediction accuracy based Learning Classifier System is used to model the trader learning mechanism. Markov processes are utilized to model the bias mechanism of traders. Simulation experiments are generated using the Anylogic5.1^™ software. Homogenous rational expectations equilibrium is utilized as the benchmark for comparison of results from the hybrid proposed model. The model derived from the framework contributes to understanding the market behavior and potential sources of deviation from efficient market equilibrium. The artificial life based framework provides a flexible way of modeling sub-systems of System of Systems and captures the adaptive and emergent behavior of the system.