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FUTURE COMPUTING ENVIRONMENTS FOR MICROSIMULATION MODELING 144 (c) the machine-readable versions of the operating characteristics; (d) the input and output programs for manipulating files, including the large population data files; and (e) the data dictionary and related files that define population microdata and, possibly, parameters, aggregate time series, and other related entity types. (4) The computing environment in which the simulation exercises are defined and executed, including: (a) the hardware usedâthe processor(s), primary and secondary memory, and input and output devices; (b) the system software used to support the simulation system; (c) the network configuration used if more than one computer system is used to support the simulation system, or if the application uses linked comodels on different systems; and (d) the program development and support tools that the system software supports to allow program construction, modification, and testing. The separation of microanalytic simulation models into such components is useful in that the intellectual, human, and physical capital associated with a given model is distributed among these various parts, and knowledge of this distribution leads to a better understanding of a given model's flexibility and adaptability to change. In addition, the extent to which existing and previous models have either benefited or been limited by current and past investment policies in each of these areas is likely to lead to a better choice of investment policies in the future. Single Pe riod Versus Future Projections While microanalytic simulation models implicitly have an element of future prediction in them, this aspect is not necessary to the concept of such modeling. Initial simulations of the effects of changes in tax policy computed changes based on the state of the population in the base year only, although later work included projections of income, expenses, and tax unit population sizes. When such models have neither behavioral change nor future projection elements, they are sometimes referred to as static accounting models, since the simulation reduces to a set of accounting rules applied to a static population database. However, while the notion of future projection is not a necessary part of a microanalytic simulation model, the use of such models in a public policy setting is oriented toward providing useful estimates of the future impact of alternative programs and legislation. Such a process is often referred to as aging the population, so that it will be representative of future years and will take into account a predicted pattern of social, demographic, and economic changes.5 5Aging a population can have several dimensions. Demographic aging generally means applying rules for modifying population weights over time. Economic aging involves applying rules for modifying a set of economic variables over time. Both sets of rules apply to individual units in the initial micropopulation. Application of these rules during the progress of the simulation exercise is performed such that key aggregates produced will match control totals that have been defined using methods independent of the simulation. This process may be thought of as a complex normalization process.