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National, regional, and corporate planning methodologies—system dynamics versus simultaneous equation using regression
NATIONALS REGIONAL, AND CORPORATE PLANNING ~ET~UD~LUGIES-SYSTEM DYNAMICS VERSUS SIMULTANEOUS EQUATION USING REGRESSION MILTON M. CHEN ~e~a~rnent of Management, San Diego State University, San Diego, CA 92182,U.S.A,
Abstract-This paper compares two distinct large scale modeling approaches-Systems Dynamics and Simultaneous-Equation Regression-in terms of methodological features and user friendliness. The circumstancesunder
which one methodologymay be more appropriatethan the other is explored. Several suggestionsare made regardingthe practicalchoiceof one approachover the other.
Twa distinct and dominant macro-modeling methodologies have emerged in recent years: System Dynamics (SD) and Simultaneous-Equation using Regression (SEX). System Dynamics originates with Forrester’s Industrial Dynamics fl, 8, 9, IO], heavily drawn from the me~odolog~es developed in the Geld of servomechanisms in engineering. The approach, in essence, proceeds in the following manner in the model design: (1) observe the behavior modes of the system, (2) research the feedback structures, (3) identify level and rate variables and describe their relationships in equations, (4) simulate the dynamic behavior of the system in the laboratory using the computer, (5) modify the model structure after the simulation results, and (6) introduce modified policy variables in search of acceptable policies that will yield improved behavior. System Dynamics explicitly deals with dynamic behavior of multiple-feedback nonlinear systems prevalent in corporate and socioeconomic systems. Given the initial values of variables and parameters. the model generates information items useful for policy makers, Since the model explicitly incorporates the policy variables, changes in policy can then be simulated to see their impact on other variables using computer runs. In the SD approach, diagrams are used to depict the multidire~tion~ causal feedback loops. For example, the following causal feedback loop may be used to describe the causal link of a housing sector: New urx)ttr,dered Desired howx~c; L&S in stock
in stock @eve0
The expressions in the SD mod& consists of two main kinds of equations: level and rate. A simple level equation of a level variable Inventory (I) evaluated at time point K may take the form of:
where L indicates a level equation; J is the point in time preceding K; IN and OUT are inflow and outflow rates respectively; and JK is the time interval between .J and K. The rate variable is the activity variable inthrencing the associated level variables. It may take a number of different functional forms, even a multiplicative form to take into account the effect of multiple variables. A simple rate equation for a rate variable, Shipment Received (IN), during the time period KL, may look like; R IN * KL = 1/6(OP ’ K) where OP represents Order in Process (level) at point in time K. With level variables describing the state of the systems and rate variables representing the activity or process, a model of a complex system can be constructed by identifying the causa1 loops using level, rate, and other equations such as auxiliary equation. SD has a relative short history. The S~mu~aneous Equation Regression, on the other hand, has a longer history and is rooted in statistical and discipline-oriented theories [3,20]. Econometrics represents the prototype of such an approach. The widespread use of SER did not begin until the emergence of econometrics, The approach typically goes through the sequence of steps in the modeling effort: (1) construct a s~muItaneous~quation model based on theories developed in the discipline, e.g. economics, sociology, and administrative science, linking a set of variables in equation format. (2) collect.data on variables in the model using time series or cross sectional data or combination of both, (3) estimate the parameters of the system using statistical techniques (e.g. ordinary least-squares or two-stage least-squares), and (4) simnlate the impact of changes in policy variables through the parameterized system. In the SER approach, the set of equations are dictated by theory and the convenience of statistical estimation. The structural form is usuahy expressed in matrix notation as: Y=XBJrU where Y is the vector of the jointly dependent variables; X is the matrix of the predetermined variables, including exogenous variables and predetermined dependent vari-
114
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ables; B is the vector of the coefficients (parameters) of the predetermined variables; and U is the vector of the random disturbances. The estimation of the B vector becomes crucial in completing the model for simulating the impact of policy actions. Although the two approaches are similar in that both use a set of equations to express the causal relationship among variables including policy variables, they differ substantially in these respects: (1) identification of variables. (2) specification of functional relationships, (3) the use of theories developed in social and other sciences, (4) the use of expert’s perception. (5) the estimation of parameters, (6) the use of observed data, and (7) the test of validity. Both methodologies have been extensively applied to the modelling of world, national, regional, and corporate problems[2, 7, 11-13, 15-19, 22-25, 28-30, 32, 331.They constitute competing methodologies for policy simulation in a planning model for all levels of a decision making unit. The two competing methodologies have given rise to much misconception and confusion. Often, system dynamics is referred to as a computer simulation model whereas statistical modelling is not. even though they both involve the use of a computer for computational purposes. Many researchers or students, having been exposed to only one of these methods during their graduate training, naturally do not even know that the other competing method exists. The critiques labeled at the world dynamics (which uses the system dynamics approach) by those who were fostered in the statistical modelling and the counter claims made by the system dynamics group typify the myopic vision of both groups of adherents[4-6, 13, 14, 21, 26, 27, 311. There is a need to objectively and systematically examine the advantages and disadvantages of the two competing methodologies and their application to the modelling of a real world system. Furthermore, we must study the circumstances under which one method may be more appropriate than the other. No direct and comprehensive comparison of the two approaches has been conducted to guide the choice of one approach over the other. Most authors limit their discussion to only one aspect of the contrast or similarity such as the mathematical equivalence between expressions used in both approaches [6]. 2.METHODOLOGICALCOMPtUUSON
The operational features differ substantially between SD and SER approaches because of the discipline orientation and the conceptual framework evolved over the years for each approach. Essential differences discussed below one by one will decipher the advantages and disadvantages of each approach under certain circumstances and the possibility of cross fertilization will be sought. Theoretical underpinnings The discipline-oriented theories (e.g. economics and sociology) guide the construction of SER models. For example, macro-economic theories underly most of the econometric works and key variables. Their links are also derived from theories. In contrast, no disciplineoriented theories directly support the application of SD approach in socio-economic systems. System dynamics does. however, have a unified framework to model any system using the core concepts of feedback system and its associated level and rate variables. The issue has not yet
been settled: whether or not the SD approach based on the servomechanism theory constitutes socio-economic theories. At present, SD is viewed as one of the techniques to be applied just as in the case of statistical method that has been used frequently in socio-economic science. No theories parallel to certain socio-economic theories, e.g. Keynes’ general theory or Friedman’s monetary theory. have emerged from SD studies. Feedback loop and control SD considers the feedback control fundamental to all life and human endeavor. The feedback scheme allows multidirectional causation following a time sequence in a recursive fashion. The lack of explicit feedback concept renders the SER approach less useful in devising a scheme for policy or decision making. Moreover, the feedback systems in SD approach allow a recursive determination of variable values, which is a distinct advantage over the simultaneous solution required of the SER approach. Identification of variables The discipline-oriented theories dominate the SER models and generates the set of variables included. For example, price plays a key role in economic theory. Hence it is an important variable that is almost automatically included in a model where the market mechanism is at work. SD models, on the other hand, lack direct theoretical supports from substantive areas of social economic sciences. Since SD approach emphasizes the feedback control structure and certain functional forms that are not part of the theoretical tools in some areas of socio-economic sciences, the application of SD approach often results in models that appear to be disjointed and which deviate from generally accepted theories. Level and rate The SD approach insists on the explicit distinction between level and rate. It places constraints on the use of these two different kinds of variables. Level variables and their equation define the state of the system at a given point in time and the change in their values are caused by the rate of inflow and outflow. Only levels create rates and the group of rate variables are evaluated for their value after the level variables. Moreover, levels may or may not influence the rates. Even though the concepts of stock and flow as used in economics and finance are identical to the levels and rates and have been implicitly or explicitly recognized, no strict unified rules apply to their uses. The clear distinction of level and rate variables and their equations in the SD approach represents a more consistent manner of describing the system’s behavior than the SER approach does. Functional forms The SD approach exemplifies the nonlinear feedback system: the simulation methods enable the approach to deal with the nonlinearity more effectively. The reliance on linear statistical method renders the SER approach ineffective in handling the nonlinear system even though certain linear transformations and linear approximation methods are available. Measurement of variables The SD approach emphasizes the direct use of important variables rather than using proxies even though no acceptable measuring units or data exists. These
National.regional,and corporateplanningmethodologies variables may include quality of life, crowding, and management effectiveness. SER approach, on the other hand, stresses measurement and data, and consequently, proxy variables are often used for variables lacking measurement and data. Parameter determination and data requirement In the SD approach, level equations are pure identities and no determination of parameters values is needed. The rate equation and auxiliary equation require the determination of parameter values. Heavy reliance on the judgment of analysts, policy makers, and experts characterizes the actual practice. In its extreme, the SD approach represents the explicit quantification of the mental model of the policy makers and puts subjective beliefs into numerical forms. Extensive sensitivity analysis is performed to determine the need for seeking more precise parameter values. Statistical analysis of past data is minimal or nonexistent. The SER approach, in contrast, relies almost exclusively on statistical estimation techniques based on data. This practice has drawbacks. In particular, the biasness and multicollinerity often result in parameter values that are contrary to theoretical expectation, for example, a coefficient for an important variable having a wrong sign. Sensitiuity analysis The SD Approach performs sensitivity analyses routinely. The DANAMO language used in the SD approach facilitates the task. As often happens, too many combinations of assumptions and postulates may entail large scale sensitivity tests which give inconclusive results. Moreover, the sensitivity of parameters still must be subjectively evaluated even after the test. In the SER approach, sensitivity analysis is performed on an ad hoc basis. Data requirement SD approach requires no particular set of data whereas SER needs data with sufficient degree of freedom for statistical analyses. This requirement places a heavy burden on the SER approach. The SD approach is typified by a lack of detailed, objective, and empirical search for data. This deficiency should be rectified. The SD approach treats the computer simulation as if it were equivalent to laboratory experiment whereas the SER approach treats the larger world as the laboratory. verification and validity The SER approach accentuates the empirical verification of models whereas SD approach deemphasizes it. The positive test of validity in socioeconomic sciences relies heavily on criterion validity, convergent validity, and content validity. Predictive validity, a type of criterion validity represented by the old adage “the proof is in the pudding,” has been the keystone for testing validity. Such method of positive proof is shunned in the SD approach. Management acceptance is often cited as the criterion to judge the usefulness of the modeling effort without showing the actual implementation or cost saved, or the profit generated. Empirical
Optimization techniques used The SD approach avoids the optimization techniques, e.g. mathematical programming prominent in operations
175
research/management science and economics. Several SER models, however, utilize such optimization techniques. Long-range us short-range forecast Due to emphasis on empirical testing, most SER models are used for short-range forecasting or projection 5 yr or less. SD practitioners, on the other hand, have been bold enough to perform long range forecasts or projections, some with a time span of over 50 yr. A stream of numbers can be generated to produce forecases over the long had since the SD approach, as it is practiced, often generates all values for variables included in the system. This is done with certain mathematical function, e.g. a sine function, using time as the augment. The SER approach requires the determination of a large number of exogenous variables, which is by itself a huge undertaking and the long-range forecast or projection is rarely attempted due to their unreliability. 3.PRACTICALCHOICEOFAPPROACH
The foregoing discussion points out advantages and disadvantages of the two approaches on pure methodological ground. No clear-cut choice exists for favoring one over the other. However, some fairly useful guidelines can be drawn. SD approach is favored in the following cases: (1) Substantive theories in the areas are weak and don’t provide specific quantitative framework for constructing the model. This criterion would probably rule out most economic modelling since economics has developed various theories that guide the model building in a specific manner. On the other hand, in regional planning and modeling of world systems where diverse variables interact and no theoretical framework has been provided, the SD approach is appropriate. (2) Feedback control is important. (3) Nonlinear forms predominate and the usual linear transformation or approximation is misleading. (4) Measurement of key variables or data are missing. (5) Accurate or robust parameter values can be determined without resorting to statistical estimation. (6) Empirical verification is not an important issue. (7) Long range projection or forecast is required. The reverse of these criteria would favor the SER approach. The practical selection of an approach should consider other criteria independent of the methodological consideration. One such criterion involves the degree to which the approach can be comprehended by the modelers and potential users. The friendliness of the approach may determine which method should be employed. Since most researchers and users of socioeconomic models have been exposed to statistics and social science theories, the SER approach has the advantage. In the corporate planning area, managers and researchers with engineering background may find the SD approach easily comprehensible. However, the overriding majority of top management personnel comes from business, economics, and law, thus the SER approach would probably dominate. The difficulty in understanding the SD approach also stems from the unwieldly manner in which expressions are written. DYNAMO, the language used for SD simulation, is more complex than general languages such as BASIC or FORTRAN. Moreover, as Day [6] points out, the use of functions such as TABLE in SD approach usually results in a large number of expressions that are
176
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A Dialogue American Elsevier, Amsterdam, North Holland (1976). 5. H. S. D. Cole and R. C. Curnow, Models of Doom. Universe is illustrated by a simple example. In conBooks, New York (1973). housing sector model for a region, an 6. R. H. Day, On system dynamics Behavioral Sci. 19 260-271 study may produce the following model: (1974). 7. R. Dusansky, M. Ingler and J. Waloh. Estimating the H = 218722- 2.437PR- 14863MOR+47INC - OS23EMP economic impacts of public spending in a sub-region: An (demand equation) econometric approach, Socio-Econ. Plan. Sci. 15, 255-262 (1981). H = 15190+ 0.314PR - 6859MORt 0.759LAB 8. J. W. Forrester, Industrial Dynamics. The MIT Press, Cam(supply equation) bridge, Mass. (1961). 9. 1. W. Forrester, A response to Ansoff and Slevin, Managewhere: H = housing stock (units); PR = unit price of a ment Sci. 14, 601-618 (1968). housing unit ($); MOR = mortgage rate (Percentage 10. .I. W. Forrester. Industrial dynamics after the first decade. Management Sci. 14, 398-415 (1968) point); INC = per capita personal income ($); and LAB = 11. 1. W. Forrester, Urban Dynamics. The MIT Press. Camnumber employed (persons). Endogenous variables conbridge, Mass. (1969). sist of H and PR whose values are simultaneously 12. J. W. Forrester. World Dynamics, 4th Edn, The MIT Press, determined within the system. If MOR is the decision Cambridge, Mass. (1973). variable, then H and PR values can be solved for given 13. J. W. Forrester, G. W. Low and N. .I. Moss, The debate on values of INC, EMP and LAB. world dynamics: A response to Nordhaus. Policy Sci. (1974). A corresponding SD model will most likely consist of 14. J. W. Forrester, The validity of system dynamics: An interover 30 equations with numerous pages of output that change. Technology Rev. pp. 2, 3, 72 (July/Aug. 1976). tax the patience of most researchers and policy makers. 15. J. W. Forrester, The system dynamics national model: Understanding socio-economic behavior and policy alterSD approach often adapts the point of view of the natives. Technological Forecasting and Social Change 9, strategic decision-maker. The resulting model may be 51-68 (1976). less useful for others, e.g. operations managers at the 16. J. W. Forrester, Changing economic patterns Technological middle level. This is in sharp contrast with the goal of Rev. (Aug./Sept. 1978). SER approach where the increase in knowledge for 17. N B. Forrester. The Life Cvcle of Economic Deueloornent, everyone is the real concern. 2nd Edn. The MIT Press, Cambridge, Mass. (1973). 18. N. J. Glickman, Econometric Analysis of Regional Systems. Academic Press, New York (1977). 4.CONCLUSION 19. H. R. Hamilton et al., Systems Simulation for Regional Analysis. The MIT Press, dambridge, Mass. (1968). Can both approaches be synthesized? Presently, the 20. J. Johnston, Economettic Methods. McGraw-Hill. New York possibility seems rather remote due to all the differences (1972). previously enumerated. As with any endeavor, a cross 21. Carl Kaysen. The computer that printed out WOLF. A fertilization to modify some parts of the approaches critiaue on limits to growth. For&n Affairs (Julv 1972). would probably emerge. The most likely outcome would 22. N. J.‘Mass, Monetari and real causes-of investment dooms be for SD approach to: (1) adapt more data analysis in and declines. Socio-Econ. Plan. Sci. 14, 281-290 (1980). estimating parameters, (2) use empirical verification, and 23. N. J. Mass and G. W. Low, Transfer payments and the Phillips curve, Socio-Econ. Plan. Sci. 14, 291-301 (1980). (3) integrate existing socio-economic theories and cumu24. D. H. Meadows et al., The Limits to Growth. Universe lated knowledge. Books, New York (1972). At the same time, the SER approach should: (1) con2s T. H Naylor, Simulation Models in Corporate Planning. sider the feedback and dynamic structure of the actual Praeger, New York (1979). system, (2) deal with nonlinearity, (3) distinguish be26. W. D. Nordaus, World dynamics: Measurement without data. tween stocks and flows and devise rules for applying Econ. J. 83, 1156-1183 (1972). their causal link, and (4) expand the narrow discipline 27. W. L. Oltmans, On Growth. Putnam, New York (1974). focus (economics, sociology, management science, etc.). 28. R. Pindyck and D. L. Rubinfeld, Econometric Models and Both approaches would be enriched by utilizing these Economic Forecasts. McGraw-Hill, New York (1976). 29. R. F. C. Shen, R. G. Paulsen and H. Sternbach. Toward suggestions. regional economic planning: The New England case, SocioEcon. Plan. Sci. 10, 181-192 (1976). REFERENCES 30. E. B. Roberts (Eds.), Managerial Applications of System Dynamics. The MIT Press, Cambridge, Mass. (1978). 1.I. H. Ansoff and D. P. Slevin, An appreciation of industrial 31. R. M. Solow, Notes on ‘doomsday models.’ Proc. Nat. Acad. dynamics. Management Sci. 14, 383-397 (1968). Sci. 69, 3832-3833 (1972). 2. K. R. Baker and W. W. Damon. A simultaneous planning 32. M. M. Stein et al., A multipurpose multilevel socio economic model for production and working capital. Decision sci. 8, 95 planning model: Theoretical framework and preliminary (1977). 3. H. M. Blalock, Jr.. Causal Models in the Social Sciences ;esults.Socio. Econ Plan. Sci. 13, 7-12 (1979). 33. J. L. Waelbroeck (Ed.), The Models of Project LINK. NorthAldine, Chicago (1971). Holland, Amsterdam (1976). 4. C. W. Churchman and R. 0. Mason (Eds.), World Modeling:
difficult for plies. The point structing a econometric
novices to grasp. Here Occum’s razor ap-
Abstract-This paper compares two distinct large scale modeling approaches-Systems Dynamics and Simultaneous-Equation Regression-in terms of methodological features and user friendliness. The circumstancesunder
which one methodologymay be more appropriatethan the other is explored. Several suggestionsare made regardingthe practicalchoiceof one approachover the other.
Twa distinct and dominant macro-modeling methodologies have emerged in recent years: System Dynamics (SD) and Simultaneous-Equation using Regression (SEX). System Dynamics originates with Forrester’s Industrial Dynamics fl, 8, 9, IO], heavily drawn from the me~odolog~es developed in the Geld of servomechanisms in engineering. The approach, in essence, proceeds in the following manner in the model design: (1) observe the behavior modes of the system, (2) research the feedback structures, (3) identify level and rate variables and describe their relationships in equations, (4) simulate the dynamic behavior of the system in the laboratory using the computer, (5) modify the model structure after the simulation results, and (6) introduce modified policy variables in search of acceptable policies that will yield improved behavior. System Dynamics explicitly deals with dynamic behavior of multiple-feedback nonlinear systems prevalent in corporate and socioeconomic systems. Given the initial values of variables and parameters. the model generates information items useful for policy makers, Since the model explicitly incorporates the policy variables, changes in policy can then be simulated to see their impact on other variables using computer runs. In the SD approach, diagrams are used to depict the multidire~tion~ causal feedback loops. For example, the following causal feedback loop may be used to describe the causal link of a housing sector: New urx)ttr,dered Desired howx~c; L&S in stock
in stock @eve0
The expressions in the SD mod& consists of two main kinds of equations: level and rate. A simple level equation of a level variable Inventory (I) evaluated at time point K may take the form of:
where L indicates a level equation; J is the point in time preceding K; IN and OUT are inflow and outflow rates respectively; and JK is the time interval between .J and K. The rate variable is the activity variable inthrencing the associated level variables. It may take a number of different functional forms, even a multiplicative form to take into account the effect of multiple variables. A simple rate equation for a rate variable, Shipment Received (IN), during the time period KL, may look like; R IN * KL = 1/6(OP ’ K) where OP represents Order in Process (level) at point in time K. With level variables describing the state of the systems and rate variables representing the activity or process, a model of a complex system can be constructed by identifying the causa1 loops using level, rate, and other equations such as auxiliary equation. SD has a relative short history. The S~mu~aneous Equation Regression, on the other hand, has a longer history and is rooted in statistical and discipline-oriented theories [3,20]. Econometrics represents the prototype of such an approach. The widespread use of SER did not begin until the emergence of econometrics, The approach typically goes through the sequence of steps in the modeling effort: (1) construct a s~muItaneous~quation model based on theories developed in the discipline, e.g. economics, sociology, and administrative science, linking a set of variables in equation format. (2) collect.data on variables in the model using time series or cross sectional data or combination of both, (3) estimate the parameters of the system using statistical techniques (e.g. ordinary least-squares or two-stage least-squares), and (4) simnlate the impact of changes in policy variables through the parameterized system. In the SER approach, the set of equations are dictated by theory and the convenience of statistical estimation. The structural form is usuahy expressed in matrix notation as: Y=XBJrU where Y is the vector of the jointly dependent variables; X is the matrix of the predetermined variables, including exogenous variables and predetermined dependent vari-
114
M. M. CHEN
ables; B is the vector of the coefficients (parameters) of the predetermined variables; and U is the vector of the random disturbances. The estimation of the B vector becomes crucial in completing the model for simulating the impact of policy actions. Although the two approaches are similar in that both use a set of equations to express the causal relationship among variables including policy variables, they differ substantially in these respects: (1) identification of variables. (2) specification of functional relationships, (3) the use of theories developed in social and other sciences, (4) the use of expert’s perception. (5) the estimation of parameters, (6) the use of observed data, and (7) the test of validity. Both methodologies have been extensively applied to the modelling of world, national, regional, and corporate problems[2, 7, 11-13, 15-19, 22-25, 28-30, 32, 331.They constitute competing methodologies for policy simulation in a planning model for all levels of a decision making unit. The two competing methodologies have given rise to much misconception and confusion. Often, system dynamics is referred to as a computer simulation model whereas statistical modelling is not. even though they both involve the use of a computer for computational purposes. Many researchers or students, having been exposed to only one of these methods during their graduate training, naturally do not even know that the other competing method exists. The critiques labeled at the world dynamics (which uses the system dynamics approach) by those who were fostered in the statistical modelling and the counter claims made by the system dynamics group typify the myopic vision of both groups of adherents[4-6, 13, 14, 21, 26, 27, 311. There is a need to objectively and systematically examine the advantages and disadvantages of the two competing methodologies and their application to the modelling of a real world system. Furthermore, we must study the circumstances under which one method may be more appropriate than the other. No direct and comprehensive comparison of the two approaches has been conducted to guide the choice of one approach over the other. Most authors limit their discussion to only one aspect of the contrast or similarity such as the mathematical equivalence between expressions used in both approaches [6]. 2.METHODOLOGICALCOMPtUUSON
The operational features differ substantially between SD and SER approaches because of the discipline orientation and the conceptual framework evolved over the years for each approach. Essential differences discussed below one by one will decipher the advantages and disadvantages of each approach under certain circumstances and the possibility of cross fertilization will be sought. Theoretical underpinnings The discipline-oriented theories (e.g. economics and sociology) guide the construction of SER models. For example, macro-economic theories underly most of the econometric works and key variables. Their links are also derived from theories. In contrast, no disciplineoriented theories directly support the application of SD approach in socio-economic systems. System dynamics does. however, have a unified framework to model any system using the core concepts of feedback system and its associated level and rate variables. The issue has not yet
been settled: whether or not the SD approach based on the servomechanism theory constitutes socio-economic theories. At present, SD is viewed as one of the techniques to be applied just as in the case of statistical method that has been used frequently in socio-economic science. No theories parallel to certain socio-economic theories, e.g. Keynes’ general theory or Friedman’s monetary theory. have emerged from SD studies. Feedback loop and control SD considers the feedback control fundamental to all life and human endeavor. The feedback scheme allows multidirectional causation following a time sequence in a recursive fashion. The lack of explicit feedback concept renders the SER approach less useful in devising a scheme for policy or decision making. Moreover, the feedback systems in SD approach allow a recursive determination of variable values, which is a distinct advantage over the simultaneous solution required of the SER approach. Identification of variables The discipline-oriented theories dominate the SER models and generates the set of variables included. For example, price plays a key role in economic theory. Hence it is an important variable that is almost automatically included in a model where the market mechanism is at work. SD models, on the other hand, lack direct theoretical supports from substantive areas of social economic sciences. Since SD approach emphasizes the feedback control structure and certain functional forms that are not part of the theoretical tools in some areas of socio-economic sciences, the application of SD approach often results in models that appear to be disjointed and which deviate from generally accepted theories. Level and rate The SD approach insists on the explicit distinction between level and rate. It places constraints on the use of these two different kinds of variables. Level variables and their equation define the state of the system at a given point in time and the change in their values are caused by the rate of inflow and outflow. Only levels create rates and the group of rate variables are evaluated for their value after the level variables. Moreover, levels may or may not influence the rates. Even though the concepts of stock and flow as used in economics and finance are identical to the levels and rates and have been implicitly or explicitly recognized, no strict unified rules apply to their uses. The clear distinction of level and rate variables and their equations in the SD approach represents a more consistent manner of describing the system’s behavior than the SER approach does. Functional forms The SD approach exemplifies the nonlinear feedback system: the simulation methods enable the approach to deal with the nonlinearity more effectively. The reliance on linear statistical method renders the SER approach ineffective in handling the nonlinear system even though certain linear transformations and linear approximation methods are available. Measurement of variables The SD approach emphasizes the direct use of important variables rather than using proxies even though no acceptable measuring units or data exists. These
National.regional,and corporateplanningmethodologies variables may include quality of life, crowding, and management effectiveness. SER approach, on the other hand, stresses measurement and data, and consequently, proxy variables are often used for variables lacking measurement and data. Parameter determination and data requirement In the SD approach, level equations are pure identities and no determination of parameters values is needed. The rate equation and auxiliary equation require the determination of parameter values. Heavy reliance on the judgment of analysts, policy makers, and experts characterizes the actual practice. In its extreme, the SD approach represents the explicit quantification of the mental model of the policy makers and puts subjective beliefs into numerical forms. Extensive sensitivity analysis is performed to determine the need for seeking more precise parameter values. Statistical analysis of past data is minimal or nonexistent. The SER approach, in contrast, relies almost exclusively on statistical estimation techniques based on data. This practice has drawbacks. In particular, the biasness and multicollinerity often result in parameter values that are contrary to theoretical expectation, for example, a coefficient for an important variable having a wrong sign. Sensitiuity analysis The SD Approach performs sensitivity analyses routinely. The DANAMO language used in the SD approach facilitates the task. As often happens, too many combinations of assumptions and postulates may entail large scale sensitivity tests which give inconclusive results. Moreover, the sensitivity of parameters still must be subjectively evaluated even after the test. In the SER approach, sensitivity analysis is performed on an ad hoc basis. Data requirement SD approach requires no particular set of data whereas SER needs data with sufficient degree of freedom for statistical analyses. This requirement places a heavy burden on the SER approach. The SD approach is typified by a lack of detailed, objective, and empirical search for data. This deficiency should be rectified. The SD approach treats the computer simulation as if it were equivalent to laboratory experiment whereas the SER approach treats the larger world as the laboratory. verification and validity The SER approach accentuates the empirical verification of models whereas SD approach deemphasizes it. The positive test of validity in socioeconomic sciences relies heavily on criterion validity, convergent validity, and content validity. Predictive validity, a type of criterion validity represented by the old adage “the proof is in the pudding,” has been the keystone for testing validity. Such method of positive proof is shunned in the SD approach. Management acceptance is often cited as the criterion to judge the usefulness of the modeling effort without showing the actual implementation or cost saved, or the profit generated. Empirical
Optimization techniques used The SD approach avoids the optimization techniques, e.g. mathematical programming prominent in operations
175
research/management science and economics. Several SER models, however, utilize such optimization techniques. Long-range us short-range forecast Due to emphasis on empirical testing, most SER models are used for short-range forecasting or projection 5 yr or less. SD practitioners, on the other hand, have been bold enough to perform long range forecasts or projections, some with a time span of over 50 yr. A stream of numbers can be generated to produce forecases over the long had since the SD approach, as it is practiced, often generates all values for variables included in the system. This is done with certain mathematical function, e.g. a sine function, using time as the augment. The SER approach requires the determination of a large number of exogenous variables, which is by itself a huge undertaking and the long-range forecast or projection is rarely attempted due to their unreliability. 3.PRACTICALCHOICEOFAPPROACH
The foregoing discussion points out advantages and disadvantages of the two approaches on pure methodological ground. No clear-cut choice exists for favoring one over the other. However, some fairly useful guidelines can be drawn. SD approach is favored in the following cases: (1) Substantive theories in the areas are weak and don’t provide specific quantitative framework for constructing the model. This criterion would probably rule out most economic modelling since economics has developed various theories that guide the model building in a specific manner. On the other hand, in regional planning and modeling of world systems where diverse variables interact and no theoretical framework has been provided, the SD approach is appropriate. (2) Feedback control is important. (3) Nonlinear forms predominate and the usual linear transformation or approximation is misleading. (4) Measurement of key variables or data are missing. (5) Accurate or robust parameter values can be determined without resorting to statistical estimation. (6) Empirical verification is not an important issue. (7) Long range projection or forecast is required. The reverse of these criteria would favor the SER approach. The practical selection of an approach should consider other criteria independent of the methodological consideration. One such criterion involves the degree to which the approach can be comprehended by the modelers and potential users. The friendliness of the approach may determine which method should be employed. Since most researchers and users of socioeconomic models have been exposed to statistics and social science theories, the SER approach has the advantage. In the corporate planning area, managers and researchers with engineering background may find the SD approach easily comprehensible. However, the overriding majority of top management personnel comes from business, economics, and law, thus the SER approach would probably dominate. The difficulty in understanding the SD approach also stems from the unwieldly manner in which expressions are written. DYNAMO, the language used for SD simulation, is more complex than general languages such as BASIC or FORTRAN. Moreover, as Day [6] points out, the use of functions such as TABLE in SD approach usually results in a large number of expressions that are
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difficult for plies. The point structing a econometric
novices to grasp. Here Occum’s razor ap-