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Suggested Citation:"Static Versus Dynamic Simulation." National Research Council. 1991. Improving Information for Social Policy Decisions -- The Uses of Microsimulation Modeling: Volume II, Technical Papers. Washington, DC: The National Academies Press. doi: 10.17226/1853.
Page 145

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FUTURE COMPUTING ENVIRONMENTS FOR MICROSIMULATION MODELING 145 The effects of time passing can be incorporated entirely outside the model by scaling the results of simulations to match independent estimates of present or future aggregate demographic composition and economic activity, or it can be incorporated in a variety of ways within the simulation model using either data at the micro or at the macro level. Since an accurate representation of all of the factors involved in providing accurate estimates from a microsimulation is probably beyond the scope of what is possible today with microanalytic models, such models generally use a mix of techniques.6 Static Versus Dynamic Simulation Microsimulation models are often categorized as static or dynamic with respect to the method they use to predict future outcomes. Static models are characterized by lack of direct interaction of microanalytic units within the context of the model during the time period simulated. Static models rely on a combination of time- dependent weighting of the micropopulation units and application of normalization factors from external sources to attributes of each micropopulation unit. Dynamic models are characterized by varying degrees of direct interaction between micropopulation units within the simulation process. Such interaction includes the birth, death, and recombination of micropopulation units in a manner intended to simulate accurately those processes in the entire population. Dynamic microanalytic models rely on an accurate knowledge of the dynamics of such interactions. Models that support such general interaction patterns between microunits must carefully sequence the application of operating characteristics to individual micropopulation units so that such interactions are consistent in simulated time. The choice of model used is determined to a large extent by the external requirement for its creation or use. Broad social science research objectives often dictate very general model structures that can evolve in a very flexible manner, tracking intermediate results and decisions in the research process. In contrast, many uses of socioeconomic microsimulation models arise from specific policy initiatives and executive or legislative processes that are more limited in scope and more detailed in focus. In general, dynamic models have arisen from the former area, whereas static models have found greater acceptance in addressing specific legislative issues. 6A good discussion of the mix of techniques used by various models is contained in Devine and Wertheimer (1981).

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Improving Information for Social Policy Decisions -- The Uses of Microsimulation Modeling: Volume II, Technical Papers Get This Book
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This volume, second in the series, provides essential background material for policy analysts, researchers, statisticians, and others interested in the application of microsimulation techniques to develop estimates of the costs and population impacts of proposed changes in government policies ranging from welfare to retirement income to health care to taxes.

The material spans data inputs to models, design and computer implementation of models, validation of model outputs, and model documentation.

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