As the development of systems of systems becomes more important in global ventures, so does the issues of emergent behavior. The challenge for systems engineers is to predict and analyze emergent behavior, especially undesirable behavior, in systems of systems. In this paper we briefly discuss definitions of emergent behavior, describe a large‐scale system of system that exhibits emergent behavior, review previous approaches to analyzing emergent behavior, and then present a system modeling and simulation approach that has promise of allowing systems engineers to analyze potential emergent behavior in large scale systems of systems.
Complex information systems are often developed without systematic consideration of architectural alternatives partially because systems engineers have lacked a methodology for performing quantitative trade studies of networked systems of sensors, processors, and communications systems. In this paper an approach is discussed for analyzing time-critical information systems and performing systems trades. Information systems are described in terms of design factors with discrete factor levels. Object-oriented models are constructed of the information systems and simulations are run to obtain system measures of performance. Design of experiments is used to drastically reduce the number of models required. The approach is illustrated for an example combat identification information system.
The need to consider the human dimension to systems engineering is no more evident than in the current global economy. The current global financial crisis can be interpreted by applying a system of systems analysis methodology that elucidates the behavior that has emerged from the unregulated US market in collateralized debt obligations (CDOs). The market for US financial obligations of collateralized debt obligation market is modeled dynamically as a system of systems covering the time period from 2000 to 2008. Behavior of home mortgage borrowers, lenders, CDO buyers and sellers, as well as specific market inputs and outputs are included in the model. Results show that an increasing rate of home loan defaults and loss of financial market liquidity is a predictable and natural consequence of the market conditions. Of particular interest are the leading indicators that result in financial instabilities.
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