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FUTURE COMPUTING ENVIRONMENTS FOR MICROSIMULATION MODELING 225 Recommendation 1. TRIM2 should not be moved to a microcomputer environment. The investment would be sizable without corresponding short-term benefits. Medium-term benefits might be negative in terms of preempting more efficient longer-run strategies. Investment in TRIM2 should remain incremental and should track current areas of policy evaluation interest. Me dium-Te rm Issues Turning to medium-term issues, our first question is: What are the characteristics of environments in which next-generation microanalytic simulation systems should be implemented for maximum utility? In the medium run, we believe that the arguments for implementing new microanalytic simulation system software on desktop systems are compelling and overwhelming, as discussed earlier. Major reasons are as follows: â¢ Both hardware and software capabilities on desktop systems now equal if not surpass those offered by mainframe environments in most important respects. â¢ The rate of technical progress and innovation in desktop environments is both substantial and sustainable. In the medium term (1990â1995), desktop systems will be appropriate platforms for efficiently implementing microsimulation models that are regarded as large scale today. â¢ The synergy of desktop environments allows specialization of function without negatively affecting the user's ability to exploit an integrated environment. â¢ Interactive user interfaces, dedicated hardware, and tightly integrated bit mapped graphics are an integral part of quality user environments and are increasingly better and more readily available in desktop systems than in mainframe environments. Recommendation 2. Medium- and long-term investment in computing environments for microanalytic simulation activities should focus on systems implemented on desktop hardware platforms. Another question is: What should the next-generation system for implementing microanalytic simulation models look like, and how should its design be approached? We believe that current developments in graphical user interfaces, CASE tools, object-oriented systems, and desktop development environments will provide a radically more powerful and effective environment. Our reasons are briefly discussed below. Graphical user interfaces have been commonly available on desktop systems only for the past 5 years, starting with Apple's Macintosh Operating System. During this time a variety of such interfaces have proliferated in the
FUTURE COMPUTING ENVIRONMENTS FOR MICROSIMULATION MODELING 226 MS-DOS and UNIX workstation world. The computing industry is now gaining substantial experience regarding how to implement and use such interfaces effectively at both the systems and applications software levels. Existing interfaces can now be exploited for the next generation of microanalytic simulation model systems. CASE tools, object-oriented systems, and desktop development environments lag behind the development of graphical user interfaces but not by much. The recent explosion in CASE tools indicates that these markets are becoming established and available for exploitation. It is now time to seriously consider using these emerging tools to change in a fundamental manner the way in which microanalytic models are constructed and simulated. The availability of such a rich set of techniques means that a user-oriented approach can be taken in designing the specifications for the next generation of microanalytic simulation modeling tools. Rather than letting the specification be driven by established computing patterns, it should be possible to design specifications for a user interface, including model construction and simulation activities, that have an object and action orientation and contain the objects and actions that are the basic elements of microanalytic simulation. While it may not be possible to meet such specifications exactly, within the next few years it should be possible to approach them closely enough for practical purposes. Such specifications are, of necessity, an interdisciplinary effort. Formulating them must be the joint responsibility of microanalytic modeling specialists and computer industry specialists. The computing specialists can provide paradigms of what is possible with the new evolving software tools, and the modeling specialists can use the set of possibilities to construct a computer-based, object-oriented environment that best represents their activities in constructing, refining, and using microanalytic simulation models. For reasons given above, neither the current TRIM2 nor SPSD/M environments are appropriate bases on which to build a next-generation model. Neither simulation system was built from a user-oriented perspective (i.e., starting with a user interface and working from it to the underlying system structure). Such an interface could be retrofitted to SPSD/M, but the existing rigidities and limitations of the system's structure would limit the functionality of the retrofitted interface. And while such an interface could in theory be retrofitted to TRIM2, the functionality of the fit would be limited and would serve as neither a good basis nor a good example for new investment in a system for microanalytic simulation. While any new specification is influenced by what users can currently do with existing systems, any new specification should be free of implementation histories that old systems necessarily carry with them. Recommendation 3. An in-depth, medium-term study should be initiated to define over the next 1â2 years a next-generation computing environment for supporting microanalytic simulation modeling activities. Specification of such
FUTURE COMPUTING ENVIRONMENTS FOR MICROSIMULATION MODELING 227 a system should be oriented to the objects used and the actions taken by system users. Still another question is: How can the capital embedded in current microanalytic simulation models be retained as we move to new computing environments? A substantial part of the capital embedded in TRIM2 is contained in the substantial amount of complex code that describes in detail the ma ny tax and transfer programs and rules that have formed the basis for past evaluations. If this capital could not eventually be moved to a new system that supports microsimulation modeling activities, it would have to be recreated. The magnitude of the reinvestment required is very large and should be avoided at all costs. Both existing and any new microanalytic simulation systems ultimately depend on executing a bottom layer of assembled computer program modules, no matter how they are specified, bound together, and invoked at higher levels. It therefore may be possible to convert TRIM2 code into modules that can be used as the fundamental low-level building blocks of models implemented in the next-generation system. If it is possible, such a conversion will not fall out easily from the system specification process. Nevertheless the amount of capital involved in the TRIM2 code (and maybe other simulation model code) is such that conversion of the code for use in the new system is highly desirable. Work to explore the feasibility and cost of this course of action must proceed in parallel with the system specification process. Recommendation 4. The medium-term next-generation system specification study should assess how to move the capital embodied in the TRIM2 model program modules to the new environment in a verifiably functionally equivalent form. Our last question is: Can the occasion of the recommended move be used to fashion more general systems that would span more types of models and have longer effective lifetimes? Another cycle of significant investment in application systems code to support microanalytic simulation seems clearly in order, to be made after a thorough study and specification of such a system from a user perspective, as discussed above. Given the importance of the methodology and the limited opportunities to obtain funding for an effort de novo, if an investment is made, it is likely to be the one major investment of the decade. Thus, it is important to ensure that the system created be as general as possible and span implementation of as broad a class of microanalytic simulation models as possible. Future obsolescence is a threat to every system, and, for a given level of investment, the lifetime of the resulting system should be maximized. In this regard some conceptual and definitional progress would be very helpful with respect to the concept of what is referred to as aging of the
FUTURE COMPUTING ENVIRONMENTS FOR MICROSIMULATION MODELING 228 micropopulation. The term aging refers to actions taken, generally at the microunit level, to project the unit forward in time by a specific time period, usually 1 year at a time. The actions taken depend on the nature of the model and the attributes being projected forward. The two main classes of attributes requiring projection are demographic attributes and economic attributes. Core demographic attributes are birth, death, age, and marital status. Static aging techniques such as those used in TRIM2 implement aging by changing the population weight associated with the micropopulation unit in a manner such that the distribution of core attributes for the population as a whole corresponds to a set of control distributions derived from sources external to the microsimulation model. Such static aging techniques do not change the values of any attributes in the record describing the micropopulation unit. Dynamic aging techniques such as those used in DYNASIM (Orcutt et al., 1976) change the actual record structure and content of the micropopulation file in a manner believed to be consistent with reality based on the demographic operating characteristics used; units are created and destroyed to simulate birth and death, relinked to simulate family formation and dissolution, and aged by incrementing individual age variables. A variety of economic attributes are often aged, or projected forward in time, by applying growth assumptions for the years over which the projection is made. Such projections are applied to account for changes in economic status between 2 years. For static simulation, projection of economic variables generally relies on assumptions regarding the level of economic activity exogenous to the microsimulation model; dynamic simulation operating characteristics would be likely to regard the level of economic variables for individuals as at least partially endogenous to the model, affected by such variables as age, education level, occupation, and industry. Aging techniques can be applied at either of two stages in microanalytic model simulation activity. They often are applied at initial population creation time, during which the initial population for simulation is projected forward from the date at which the data were collected to the date at which the simulation experiment is to begin. The natural lag between data collection and availability for simulation exercises makes aging at this stage of the activity necessary unless simulation exercises are structured to begin simulation at the point in time when the data were collected. Often such aging steps at this time are combined with initial adjustments to the microdata file to compensate for deficiencies in the data collection process, or with file augmentation through imputation or exact or stochastic record linkages. Aging techniques can also be applied at simulation time (i.e., during the course of the simulation exercise), and generally are applied in most such exercises. Assuming that the exercise concerns itself with future projections (which most do), application of these techniques provides the raison d'Ãªtre for the simulation exercise.
FUTURE COMPUTING ENVIRONMENTS FOR MICROSIMULATION MODELING 229 Casual discussions of the role of aging in microanalytic simulation often do not differentiate between the type of projections used and when they are used. It is our opinion that lack of precision in the treatment of aging has created an artificial barrier to considering the similarities of static and dynamic aging techniques as opposed to their differences from the viewpoint of system implementation. By reconsidering the structural implementation of various aspects of the aging or projection activities within microsimulation models, it may be possible to create an overall system structure for microsimulation that can incorporate both static and dynamic models and therefore increase the return on system investment. There are two reasons for adopting this approach. First, most microanalytic simulation models do not use purely static or dynamic techniques. As reported by Devine and Wertheimer (1981:6): Virtually all of the major microsimulation models summarized earlier [TRIM, MATH, KGB, STATS, DYNASIM/ MASH, DYNASIM/MASS, OTA's personal income tax model, and HHS's health care financing model] contain elements of both the static and the dynamic approach. For example, although TRIM relies primarily on static aging techniques, unemployment and labor force participation are both adjusted using a dynamic technique. Conversely, although DYNASIM relies primarily on dynamic aging techniques, immigration is handled entirely through static aging, and other static techniques can be applied if desired by the user. Thus, the capability to implement a heterogeneous aging strategy would be exploited by most models, including TRIM2. Second, investment in a microsimulation software system that is capable of supporting both static and dynamic models is an efficient investment assuming that the overhead involved in satisfying both needs is low. From a consideration of the concepts of a simulation agenda contained in TRIM2 and MASH, we believe that if the notion of aging at simulation time within TRIM2 were made an explicit entry in its run sequence, the execution pattern of TRIM2's run sequence would parallel that of the simulation agenda in MASH, and both could be mapped into a common software framework. In this case all of the investment in graphical user interfaces, linkages with complementary programs on the same hardware platform, and other benefits of the new system to be constructed would automatically benefit static, dynamic, and mixed models implemented within the context of the software system. Further implementation within the same system could lead to the ability to compare the outputs of models of different types and orientations, running within a common system structure and using common micropopulations, aggregate time series data, and common parameter libraries. Recommendation 5. The imprecise treatment of the notion of aging needs conceptual and definitional attention. A better conceptual framework would