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The U.S. Department of Defense builds many weapons systems as Systems of Systems (SoSs). Operational testing is used to test capabilities in these SoSs, but testing all configurations of the SoSs may not be possible due to cost and the environment. Modeling and simulation (M&S) is an alternative to operationally testing all functionality and interfaces in these SoSs. An inherent deficiency of existing M&S approaches, however, lies in the emergent behavior that occurs as a result of interactions among the component systems. Applying M&S to individual components cannot detect this emergent behavior, but not detecting emergent behavior of an SoS as a whole diminishes its testing process. This dissertation develops a software architecture for an M&S framework, based on Ziegler’s theory, that makes the identification and quantification of emergent behavior possible in an SoS M&S by providing a collector of SoS metrics. This software architecture has a great advantage over the current approaches because it supports swappable and configurable components to make simulation possible over different models and parameters without costly reprogramming. This dissertation also develops an improved process for the analyst to identify and quantify emergent behavior in an SoS M&S.

The U.S. Department of Defense builds many weapons systems as Systems of Systems (SoSs). Operational testing is used to test capabilities in these SoSs, but testing all configurations of the SoSs may not be possible due to cost and the environment. Modeling and simulation (M and S) is an alternative to operationally testing all functionality and interfaces in these SoSs. An inherent deficiency of existing M and S approaches, however, lies in the emergent behavior that occurs as a result of interactions among the component systems. Applying M and S to individual components cannot detect this emergent behavior, but not detecting emergent behavior of an SoS as a whole diminishes its testing process. This dissertation develops a software architecture for an M and S framework, based on Zieglers theory, that makes the identification and quantification of emergent behavior possible in an SoS M and S by providing a collector of SoS metrics. This software architecture has a great advantage over the current approaches because it supports swappable and configurable components to make simulation possible over different models and parameters without costly reprogramming. This dissertation also develops an improved process for the analyst to identify and quantify emergent behavior in an SoS M and S.