National Academies Press: OpenBook
Suggested Citation:"Layered Structure." 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 142
Suggested Citation:"Layered Structure." 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 143

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FUTURE COMPUTING ENVIRONMENTS FOR MICROSIMULATION MODELING 142 Here the term microsimulation model describes some universe of elements solved under a variety of conditions by using computer-based microsimulation techniques. In the context of social and demographic analysis, the microanalytic units of such models are individuals and groupings of individuals, such as families or households. This chapter addresses the impact that computing technology has had and will have, in the medium term, on the conceptualization, design, implementation, and use of microanalytic simulation modeling. Two areas are studied in detail: (1) the current state of computer systems that support static microsimulation models, with specific reference to the TRIM2 (Lewis and Michel, 1990) and SPSD/M (Statistics Canada, 1989a) models and (2) advances in computing technology anticipated in the medium term (1990–1995) and their implications for additional investment in microanalytic simulation models, with special attention given to static models and the computer systems that support them. This chapter also provides an introduction to microanalytic simulation models in terms of their characteristics and the history of their development. The TRIM2 and SPSD/M models, their histories, and model support systems are described and their characteristics are compared. An assessment is made of current computer hardware trends, with emphasis on desktop computing environments that are likely to be available to support such modeling activity in the medium term. Factors affecting the demand for and availability of microanalytic models also are assessed, with special emphasis on shifts in the production function through software advances and the ability to exploit future desktop computing environment characteristics. Finally, an overall assessment is presented of alternatives for investing in the evolution of TRIM2, as well as recommendations for investment planning for future microanalytic simulation model developments in general. Because the focus of this chapter is on examining and comparing a U.S. microsimulation model and a Canadian one, much of the development of microanalytic simulation models outside North America is not covered here. Readers are referred to Orcutt, Merz, and Quinke (1986) for information on the state of activity in Europe. Characteristics of Microanalytic Simulation Models Layered Structure It is useful to think of microanalytic simulation models as being composed of

FUTURE COMPUTING ENVIRONMENTS FOR MICROSIMULATION MODELING 143 a number of related but distinct components that to some extent can be organized into layers. The component approach is useful in separating underlying knowledge, procedural modeling, and computer-based implementation aspects of a model.2 Our suggested components and their structure are as follows: (1) Knowledge regarding the world and how it works, from either a theoretical or an empirical basis, having two distinct parts: (a) the substantive social science and related knowledge in demography, economics, and other disciplines underlying the content of the socioeconomic model, and (b) a representation of the population of microanalytic units that will be used as a basis for simulation exercises. (2) The process of defining, organizing, and shaping this knowledge into a procedural and computer- executable form, including: (a) defining procedures, or operating characteristics,3 for each component of the model, that describe how the knowledge is to be applied to microanalytic simulation units to derive their behavior under alternative assumptions;4 (b) coding each operating characteristic into a computer-executable module that applies that procedure to a specific micropopulation unit used as the basis for the simulation exercise; and (c) preparing the population microdata for simulation, including precise definition through a data dictionary (or equivalent) and possibly including physical medium transformation, record and file reformatting, subset extraction, value remapping, demographic and economic aging of the data, and similar operations. (3) The application computer system that provides a framework for integrating the operating characteristics into a single module that will execute complete simulation exercises, including: (a) the interface(s) seen and used for model construction and execution; (b) the supervisor program that invokes and sequences the collection of operating characteristics; 2The components and their structure presented here are somewhat simplified. See Sadowsky (1977:6–9) for a more detailed decomposition of the structure and activities associated with another model, MASH. 3The term operating characteristics was introduced by Orcutt et al. (1961) and is adopted here. Operating characteristics consist of models of the specific behavioral or structural responses by a microunit to changes in its external environment. They depend on inputs such as the existing state of the microunit as well as the state of the environment that affects the unit; they produce outputs, or changes of state, in the microentity and possibly in other microunits in the population and in the state of the environment. They can be deterministic or stochastic. Their outputs can include state information for present, future, or past time periods. 4 Such a change may represent a simulated behavioral decision by the entity, such as a decision to change portfolio composition because of changes in the market rate of return structure, or it may be deterministic, such as recalculation of a tax unit's liability based on revised tax legislation.

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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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