Nonlinear differential equation systems of the kind used in system dynamics are capable of exhibiting highly irregular, erratic, turbulent or “chaotic” behavior. Conditions for the existence of this phenomenon are discussed and their application in dynamic socio-economic modeling explored. In particular, irregularities in economic cycles might be explained by nonlinear feedback effects in contrast to the usual “random shock” model. Dynamo simulations are used to illustrate the basic concepts involved. The view, often emphasized by Forrester, that policy should not be based on level predictions but should focus on regulating qualitative modes would seem to be strongly supported. The positive economics position associated with Friedman that theories should be judged on their predictive performance would now appear to be too restrictive. Instead, the system dynamics view that the plausibility of model assumptions should be the main basis for establishing theory credibility is strongly reinforced.
At the beginning of the seventies, grave concern existed over low mobility of researchers due to the stagnation in growth in the research capacity available. Through a strong expansion in research capacity prior to this condition of stagnation, the age of researchers was relatively low. It was feared that the small natural turnover (as a result of a low average age) together with the low mobility would lead to a collective aging of the research corps itself. This aging factor was considered to be a threat to research as a whole since it could lead to mental fixation and loss of creativity. In addition to this, the expectations for making a career and the employment possibilities open to the then recently graduated university students had strongly decreased. This situation resulted in a loss of talent for research groups, and it was feared that there would be a decline in the motivation and development possibilities for researchers. The developments briefly described here formed no phenomena exclusive to the Netherlands but also made their appearances in substantially all of the western countries.
Any firm or industry which experiences marked cyclical fluctuations in both output and demand can provide a testimony of the damaging effects such oscillations have on profitability. Perhaps none more so than the British Steel Corporation (B.S.C.) whose record in this respect is rather poor. For such industries, therefore, any methods which management science can provide as a means of analysis of the causes and effects of such cycles are to be welcomed. It is suggested in this paper that system dynamics provides an important framework for analyzing the causes of cyclical behaviour in economic systems and that it is this area of application which possesses the greatest chance for successful demonstrations of its use. It is probably for this reason that work using the methods of system dynamics has concentrated, in Gt Britain, on economic systems.
In large numbers of U.S. urban elementary schools, there is a multiplier effect which operates to reinforce high achievement for initially high-achievers, average achievement for initially average-achievers, and failure for initially low-achievers. This problem is aggravated by the fact that initial levels and perceptions of achievement are systematically related to social class. In part, poor children enter school with less readiness for reading. Evidence also suggests that, actual achievement aside, teachers tend to perceive lower class children as low-achievers simply because they exhibit lower-class family, dress, and behavioral characteristics. In contrast, there are a relatively small number of “lighthouse” schools spread through out the country in which students, often minority and/or poor, achieve far better than home or SES variables would predict.
This paper deals with the structure of the system of arms transfers by the United States to the rest of the world. Arms transfers play a significant role in the political, economic, and military affairs of most countries. Because of the diversity of opinion about the necessity for transfers and their true effect, a single policy concerning arms movement has been impossible to devise. This research is the first step in providing a model that can be used to study the effects of transfers and to evaluate policy concerning arms movement.
The world oil market is undergoing substantial changes, in terms of overall structure, number of key participants, and market adjustment mechanisms. These changes will influence both price determination as well as critical decisions pertaining to the production capacity utilization.
In this paper, a pilot system dynamics simulation model, EDFIN1, is used to forecast the impacts of a cost-of-education index (COEI) on patterns of per pupil expenditures across various types of local school districts. Originally designed to compensate more fully those districts that incurred greater costs in the purchasing of educational inputs (i.e., higher teacher salaries or greater need for transportation), COEI adjustments are seen also to have direct impacts on the relative equity of per pupil expenditures across the states as a whole.
This paper contributes to the discussion of academic training requirements for System Dynamics modelers. In particular, it suggest that training in Strategic Management can provide the System Dynamics modeler with some essential complementary tools and a “top management perspective” (or systems viewpoint), which is needed to define problems of real managerial interest. To illustrate these points, the author describes his experiences in defining a problem for system dynamics modeling. The future prospects for the New Zealand Forestry Sector, and the New Zealand Forest Service, are described and the problem for modeling is presented.
To introduce system dynamics approach into interprofessional organization to built a model about agricultural market is not so original. That’s more interesting is the use of system dynamic to define what information system must be not only designed but scheduled to regulate the market. Since July 1979, MEDOC gives some useful informations to people who have the difficult challenge to follow the Bordeaux wines market.
In the simulation model the development of the drinking water supply system of South Holland is simulated for the next thirty years given a policy strategy, a certain demand of drinking and subpotable water and some scenario assumptions like discount rate, water quality standards, increase of energy prices. An alternative policy strategy generates an alternative development over time of the supply system.
Input-output analysis for an “open” system relates production rates for various sectors of an economy to stipulated final demands. However, it is well known that the conventional dynamic analysis usually does not yield results which approach smoothly to those of the static analysis. In this work, the dynamic analysis is cast into the form of a system dynamics model. A modification of the rule which governs sector production rates is introduced so that stable results are obtained which do approach those of the usual static input-output analysis. The system equations are further modified to incorporate time-lagged stock indices and damping in the production rate rule. Prices are handled throughout as in conventional input-output analysis.
A study of the multiple-use task produces a method for integrating quantitative and subjective information to enhance decision-making about the multiple use of renewable resources. Methods of resolving conflicts and applying system dynamics methods are given.
The model system is an approach to solve the dynamic multilocation warehouse (or plant) sizing problem by integrating different models and methods: (1) A simulation model of System Dynamics type for analyzing effects of different locations and capacities on demand, cost, and operating results; (2) An Integer Programming model for determining warehouse configurations, i.e. effective locations, and capacities. The model system has been applied to an important German wholesale distributor of pharmaceuticals. It has been used for analyzing the firm’s distribution system, and working out proposals in order to improve it.
This paper describes a System Dynamics approach to the problem of linking national and regional transportation to other components of national development plans. A framework of interactions among social, economic, and transportation variables is constructed based on the proposed approach. Such a framework facilitates the analysis of the reciprocal impacts of transportation infrastructure and the socio-economic environment, thus providing an important input to the process of transportation policy making for development. Specific references are made to Venezuela, where a serious effort is being made to explicitly incorporate a transportation strategy into the national development plan.
About five years ago a semi-governmental firm, the VAM (Vuil Afovoer Maatschappij) formulated plans to extend their efforts to convert domestic waste to compost to a real recycling industry. The idea was to install equipment to extract secondary paper pulp from domestic waste and sell it to paper and board industry. The Union of Old Paper Merchants opposed strongly: abundancy of low grade secondary paper pulp could ruin the old paper market and times were bad just after the oil crisis. A study was started to investigate possible consequences of the VAM plans. In the four following years a System Dynamics model was built to show the most important mechanisms of the problem.
The central premise of this study is that complex models of social processes often fail to provide direct and useful evidence for policy makers because, of necessity, complex models are based upon five distinct classes of assumptions. At least two of these five classes of assumptions are based upon a priori or theoretical arguments rather than strict empirical arguments. Because of their inherent speculative nature (at least in part), complex models produce forecasts that are not admissible as evidence in an essentially political debate.
This paper introduces and discusses the concept of verbally formulated simulation models. Such models can operate with linguistic values as ‘high’, ‘rather high’, ‘low’ and ‘not low’, etc. as inputs. The output will be similarly verbally formulated. The stimulation procedure is based on a fuzzy set-theoretical semantical model of a fragment of English language, which converts verbal expressions into numerical quantities. The paper applies one particular semantical model in a simulation example. Verbal models may be more believable, or significant, than conventional system dynamic models, in that they adequately represent the fuzzy knowledge of the system which is modeled. The cost of this significance is loss of precision in model output. Verbal models are also easier to test for sensitivity to parameter-, state- and input values than traditional models. Therefore, a comprehensive understanding of the model’s behavior patterns is more readily obtained. The realm of successful applications of verbal models seems, however, to be restricted to systems with variables which are not physically measurable, but whose values are only available through human intuition. Finally, verbal models may successfully be incorporated in conventional system dynamic models if technically feasible. Such a prosedure would allow for an adequate handling of non-quantifiable data.
This paper documents a series of lessons that the author and his colleagues have learned about how to achieve implemented results from system dynamics projects. Through a series of three case studies, the paper illustrates the evolution of their approach to implementation over the period of 1966 to 1975. These case studies focus on: client involvement in projects; the process of model development; the nature of the models developed; and the end of the projects. The paper draws upon the case studies and earlier writing on the subject by Roberts to generalize about the factors that are most critical in achieving successful implementation. These factors include: the sharpness of the project’s problem focus; the urgency of the problem addressed; the organizational position of the clients; the degree and nature of client involvement; the size of the model developed; the demonstrable validity of the model and the nature of the project’s end-products.
Starting from the aims and difficulties of social systems modeling this paper argues that a good understanding of dynamic mathematical models is indispensible. The author’s background, and its relation to System Dynamics is elucidated, and a number of definitions are given of concepts and terms that will be employed. A set of general guidelines, and a list of strategies and tools for understanding follow. Most of the methods presented have been applied successfully in an extensive study of the World Models by Forrester and Meadows et al., and are commonly used in systems and control engineering. The main emphasis is on techniques are points of view that are generally unknown to researchers and practicians in the non-technical disciplines.
This paper describes some of the central, non-procedural aspects of sensitivity analysis in system dynamics.First section focuses on the objectives of sensitivity analysis in this particular field of modeling.The second section concentrates on the types of model change involved, with emphasis on changes in model structure and parameters.The third section discusses the interpretation of model response to changes. The central questions are how the sensitivity is judged and by whom.The final section discusses the parts in the modeling process entailing sensitivity testing.Overall the paper asserts a more comprehensive role for sensitivity analysis than seems to be commonly accepted among model builders and model users. The subjectivity and individuality of sensitivity analysis is also emphasized.
The basic assumption of this paper is that system dynamics in its original form was developed to suit policy-making in small organizations and that application of system dynamics in the field of public policy must be accompanied by change in research methodology and organization. To support this view, the paper describes experiences from a study of the Scandinavian forestry and forest industry.The model building process, interaction with decision-makers, and the organization of empirical research are analyzed separately. Based on the analysis a procedure for using system dynamics in public policy analysis is recommended. In the recommended procedure a reference group representing various client groups serves a source of qualitative information and as a channel for implementation. The need to keep model building well focused is stressed. Parallel studies of historical development on the micro- and the macro-level are suggested as a means to speed up modeling. It is finally recommended that the major results from the analysis are presented in a non-technical report.
Model building standards within the field of system dynamics are still evolving. This paper offers some general guidelines for development and presentation of refined models. Model refinement, the core of the modeling process, encompasses incremental structural and/or parametric changes to existing models. Development and presentation of refined models are enhanced through comparison of original and refined model behaviour and through comparison of policy response. Model comparison aids the modeler in identifying misspecification of new structure. In addition, presentation of comparison results assists the reader in evaluating the merits of the refined as compared to the original model, and helps to insure that the builder and user of the refined model is familiar with original model assumptions.
System Dynamics (SD) may be viewed as a process of designing ROBUST systems. The concept of ROBUSTNESS leads to a need for analyzing the effects on SD models of both parameter changes and stochastic inputs. It is demonstrated that the effects of large parameter changes can be measured by the use of hill climbing techniques given efficient computation. The paper describes the traditional ways of assessing sensitivities in SD models, together with methods based on perturbation techniques which unify the parameter and stochastic sensitivity problems. The computational characteristics of the various methods are analysed and the factors that affect their computational efficiency are discussed.The paper discusses the results of experiments to determine the accuracy and speed of the various methods on a 7 state variable, 16 parameter model and on a 70 state variable, 160 parameter model derived from it. The perturbation methods yield acceptable accuracy and for the models described reduce computer time by a factor of between 9 and 25. Compiler changes discussed in the paper would make sensitivity analysis easier and quicker and would improve techniques elsewhere in System Dynamic.
This paper establishes the importance and usefulness of a well-defined reference mode as a guide to developing transparent causal structures for system dynamics models. The importance of a transparent causal structure is two-fold: it enhances understanding the model dynamics, and it facilitates communicating to others the model and the insights derived from model simulations. The paper offers a fundamental guideline for selecting transparent causal structures the following: strive for as highly-aggregated and as simple a structure that will generate the dynamics of interest. Ability to follow the guideline depends on a well-defined reference mode, which in turn requires a clear model purpose. To illustrate how a well-defined reference mode can guide the selection of a transparent causal structure, the paper traces the development of a model of the labor market. First, the model purpose is described. Next, the evolution of the basic causal structure is discussed, utilizing the reference mode embodied in the model purpose to select a transparent structure. Finally, the causal influences on model rates of flow are highlighted. To establish the suitability of the selected structure, the paper then summarizes the results of model tests. As the paper shows, the relatively transparent causal structure chosen for the model appears capable of providing insight into the real-world labor market, and of enhancing labor-market policy analysis.
This paper presents a system dynamics model of worker mobility and wage determination in a multi-sector economy. The paper reviews the background and structure of the model, illustrates the model validation process, and sheds light on the dynamics of the labor market.
Observations of modeling efforts suggest that many models fail for managerial reasons. This paper is based on the hypothesis that 1) managerial failures occur because various facets of the modeling process are inherently hard to manage, and 2) that deliberate management can reduce or eliminate many common problems. The hypothesis is pursued by breaking the modeling procedure into a series of steps, sketching what typically does but should not happen at each of them, and putting forth some thoughts about what can be done to avoid the normal pitfalls. Particular attention is paid to mundane variables such as time allocations and finances and attitudes and emotional considerations. In general, when modeling study is not deliberately managed, the construction phase preempts the bulk of time and resources to the detriment of planning, conceptualization, testing, documentation, and client-modeler interaction. This phenomenon appears to be caused, in part, by an over-emphasis on the “harder”, more technical work of construction; by difficulty justifying work that produces no direct, tangible product; and by mental resistance to testing.
According to an implicit “start simple” principle widely accepted by system dynamics practioners, model’s complexity must be progressively increased during the modeling process. How this increase in complexity should come about has yet to be explained. In this paper, two strategies are discussed and evaluated. Since a top-down strategy starts with a high level of aggregation but includes in the model all the main variables since the first formulation, it is to be preferred to a bottom-up scheme. Moreover, the top-down strategy ensures the global coherence of the model at any stage of its conception and appears to be much more consistent with the system dynamics philosophy. This paper emphasizes the need for an adequate computer modeling language and briefly describes a first attempt. The main property of such a language is to allow a hierarchical description of models, where any composing unit can be altered without the need for a complete recompiling of the whole.
The process of attaining a useful model embraces the conceptualization, formulation, and testing stages. This paper argues that effective conceptualization can be achieved through a dynamic hypothesis (that is, a chosen time development of interest and hypotheses about the underlying mechanisms). The resulting rough, conceptual model should then be improved gradually through a recursive procedure where the model is tested, redesigned and tested again, in as many ways as possible and as long as is feasible. The paper attempts to structure the hazy topic of model construction by defining a number of terms and presents lists of dysfunctional tendencies in and guidelines for model construction.
The Scandinavian countries are approaching full utilization of the regrowth in domestic forests, and the forest industry is facing a period of much slower expansion in volume than in the past. Slower growth implies problems for the industry, forestry, and society at large. The “transition” from ample to scarce wood resources could take several forms, depending on actions taken both inside and outside the forest sector. A system dynamics simulation model has been constructed to describe different possible transition paths, and to highlight potential problems. The model purpose is not to predict what will actually happen in the future, but to describe possible futures in an internally consistent way. Such insights about the consequences of various management strategies are useful to interest groups as a basis for discussing how to reach their goals. Within the industry, there is a tendency toward temporary overexpansion of capacity. The forest sector's ability to survive under slow growth conditions could be enhanced by technological and organizational remedies. The necessary remedies will be less traumatic the earlier one accepts and acts upon the problems of finite wood supply.
Industrialized societies are presently characterized by rapid change, strong interactions and an abundance of new phenomena. To increase the likelihood of policies having the intended effects, there is a need for policy analysis with a broader perspective and longer time horizon. The main task in such broad policy analysis should be to integrate the vast amount of available information into a useful conceptual structure of the problem area. System Dynamics (SD) –relying heavily on descriptive information for a data base, on a theory of the structure of social systems for theory formation, and on computer simulation for relating structure to behaviour—offers one method of attaining such broad policy analysis. This paper reviews the historical development of the field and examines the major system dynamics literature. The impatient questions of “what is?”, “why does one do?”, “when should one do?”, and “how does one do SD?” are all answered in summary fashion. Within the system dynamics profession, intense conflicts abound as to what constitutes “proper procedure” for the policy analysis process, particularly concerning model conceptualization and testing. Much disagreement arises from implicit differences in modeling objectives. Explicit recognition of objectives and procedures could reduce much of the conflict.
This paper focuses on the aggregation that is implicit in the use of distributed delays in dynamic models. The aggregation process relates the continuous time-dependent response of a delay structure to the underlying distribution of delay times of the disaggregated events which constitute the delay. The discussion covers in particular the special case of exponential delays used in system dynamics models.
Conclusions derived from world models have little value if they do no include an estimate of the uncertainty associated with the outputs. This paper describes the System Analysis Research Unit World Model and gives an account of the application of Monte Carlo techniques to testing the model. Samples of uncertain data encoded in probability densities are used as input for model runs. The model output is analysed statistically and the contribution to total uncertainty by the variance of the inputs is determined. The output is also to be additive over a limited range. Due to the strong negative feedback loops in the model, the model usually attenuates any variation in inputs. The cost of Monte Carlo methods is justified by the quality of the results obtained.
In this report we discuss our possibilities to attain insight about social phenomena. In the first part of the report we argue the nature of social phenomena is different from natural phenomena. Therefore there is a danger connected with the fact that social science for so long time has been dominated by techniques and goals which were successfully developed for the purpose of natural science. In the second part of the report we identify and discuss four essential problems in the study of social phenomena. The problems are: (a) the definition problem, (b) the issue of limitation, (c) the problem of causality and (d) the problem of stability. In the last part of the report we discuss in what way social phenomena can be understood. Six conditions for a successful paradigm in social science are presented and we can conclude that used in a proper way System Dynamics can be one paradigm that fulfill these conditions.
This paper is a summary of the major assumptions underlying the field of computer modeling and the specific assumptions that differentiate four modeling methods used to represent social systems: system dynamics, econometrics, input-output analysis, and optimization. The primary conclusions are: 1. Each modeling method is based on a set of techniques and priors that suit it well to some sorts of policy problems and poorly to others. 2. Misunderstandings between different kinds of modelers and between modelers and clients often arise from failures to recognize these implicit priors and the various strengths and weaknesses of the various modeling schools. 3. Some modeling schools, especially system dynamics and econometrics, are based on such different basic world views and assumptions about the nature of human knowledge that communication from one school to another is almost impossible.
This paper contrasts two approaches to testing the importance of model variables: single-equation statistical tests and model-behavior tests. The paper demonstrates that, both theoretically and operationally, tests which analyze the impact of individual variables on model behavior are better suited to the task of selecting model variables. Conversely, the statistical tests should not be viewed as tests of model specification per se, but as tests of a particular type of data usefulness. When viewed as tests of data usefulness, the statistical tests have a clear, albeit quite narrow, role in model validation: they warn the modeler when available data do not permit accurate estimation of model parameter. However, as a detailed example illustrates, a model relationship may be difficult to estimate yet extremely important for overall model behavior.
This paper contrast two viewpoints for analyzing the concepts of supply and demand. The first viewpoint, which dominates most economic thinking, treats supply and demand as rates of flow. For example, supply in economic models tends to be measured by a rate of production, while demand is measured by a flow of consumption or purchases. The second viewpoint sees supply and demand primarily as stock variables or integrations. According to this viewpoint, for example, supply would be measure by the available inventory of a commodity while demand would be measured by a backlog of unfilled orders. The central point of the paper is that stock-variable concepts of supply and demand must be incorporated explicitly in economic models in order to capture the full range of disequilibrium behaviour characteristics of real socio-economic systems. More specifically, the paper shows that consideration of stock-variable measures of supply and demand is necessary to describe the price- and quantity-adjustment mechanisms linking supply and demand; to analyze properly the stability characteristics of an economic systems; to analyze short-run and long-run disequilibrium behaviour; and to assess the desirability of economic policies intended to influence such disequilibrium modes behaviour as economic growth and fluctuation.
The principle of conservation states that physical quantities are confined to their own identifiable channels and can enter, circulate within, or depart from a system only by explicit processes. This paper applies the conservation principle to an analysis of the multiplier-accelerator theory of business cycles. Section I describes and critiques a well-known model of the multiplier-accelerator interaction. By ignoring accumulations of inventory and fixed capital investment, the model fails to observe the conservation of important physical flows. Section II proposes a system dynamics model that incorporates the multiplier and accelerator processes within a closed, conserved-flow framework. Section III uses computer simulation to portray the impact of conservation on the multiplier-accelerator interaction. Simulations of the system dynamics model reveal plausible long-term cycles, rather than the short-term fluctuation associated with traditional multiplier-accelerator models. At the end of Section III, the model is modified to account explicitly for labor, as well as capital, in the production process. This revised model produces both short-term and long-term oscillation when submitted to a noise input. The short-term oscillations, averaging about 5 years, reflect the attempt to adjust inventories by varying the labor input to production. The longer fluctuations in capital stock, averaging 19 years, reflect the management of investment in fixed capital. In all of the tests, the incorporation of conserved flows considerably reduces the sensitivity of system behavior to changes in parameter values. The simulations provide theoretical evidence for divorcing short-term business cycles from the interaction of the multiplier and accelerator.
The opening address at the 1976 International Conference on System Dynamics points out that today's social ills are diffuse difficulties rather than clear-cut problems. Remedial action must start with attempts to clarify the problem, and develop alternative comprehensive strategies that consider a wide segment of society and also the long-term future in an open minded fashion. System Dynamics may serve as a tool for broad policy analysis of this kind.
Often system dynamics, and particularly the DYNAMO- language, is attacked for not integrating other modelling approaches into the field. This investigation offers alternatives that will hopefully stand up against the critics. The first part of this paper concerns the integration of external functions into system dynamics models. Modifications of the DYNAMO simulation language and of the DYNAMO compiler are explained, and conceptional questions about the integration are discussed. By means of examples of LP programs and statistical methods, the paper shows the philosophical improvements entailed by the system dynamics method.The same criteria are applied in the second part of this paper to the model-method integration of a system dynamics model with an input-output method, considered to be representative of a complete economic structure.The last part of the paper explains the integration of system dynamics model into the higher program structure of an optimizing feedback loop. The best combination of input vector parameters is calculated in the feedback loop at any time so that the output vector follows a predetermined objective function. The overall paper contents demonstrate the flexibility of the system dynamics method.
Delays are a ubiquitous feature of dynamic systems; they are present at every stage of an action. An understanding of delays is necessary if policy makers are to foresee the consequences of their actions. It is often not sufficient to rely on âexpertâ opinion to tell how long it will take for the repercussions of an action to be complete, because even the âexpertsâ can seriously underestimated delay times. It is, therefore, important to have systematic methods of estimating the length of delays in system dynamics models. The time structure of delays is also important.Whether a delay is destabilizing or stabilizing will depend on whether the repercussions are concentrated or dispersed, as well as whether the time lag is long or short. Systematic methods of estimating the orders of delays are, therefore, also useful. This paper presents five statistical methods that can be used to estimate lengths and orders of delays in system dynamics models. The presentation contains a discussion of when each method is applicable and what problems may be encountered in using it. Empirical results from applying two of the methods are discussed. The empirical studies respectively involve the problem of estimating the delay between changes in export prices and changes in export market shares and the problem of estimating the delay between capital appropriations and capital expenditures.The paper also offers guidelines for choosing an estimation technique and discusses validation of the estimates obtained.
The purpose of this paper is to convey the techniques and considerations normally involved in formulating and estimating parameters in system dynamics models. Ideally, model equations should be formulated so that the associated parameters each describe some unique observable characteristic of the real system. Thereby, translating observations and measurements below the level of aggregation of model structure (estimation from disaggregate data) into specific parameter values becomes very straightforward. Fewer assumptions about the structure of the system are needed than if the parameters were set by equation estimation or model estimation from data at the level of aggregation of model structure. Making additional assumptions provides more opportunities for systematic errors to creep into the parameter-setting process. Rather than using data at or above the level of aggregation of model structure to set parameters, such information might better be reserved for validity testing. When such data are not already used to set parameter values, the validity tests become simpler and depend upon fewer assumptions.Parameters need only be set accurately enough to allow the model to fulfill its purpose. One time-saving research strategy is to determine, by using only roughly-set parameters at first, how accurately the parameters must be set before investing time and effort in setting them accurately. Then, sensitivity testing can identify the relatively small number of parameters whose values significantly alter the model behavior or response to policy changes. The model can then be reformulated, the policies redesigned, or the sensitive parameters reset by more elaborate and hopefully more accurate techniques.
The paper describes a system dynamics model of the consumer durables manufacturing industry in the United Kingdom. The model purpose is to analyse the causes and effects of cyclical fluctuations in the industry with a view to encouraging government or operational policies that might improve industry stability. The paper extensively examines the consumer durables industry and explains the model in detail, each equation being accompanied by an account of its construction. The results of the simulation experiments conducted on the model using various test inputs are described. The paper appraises the technique of spectral analysis, which has served as one means of assessing model validity. The model, once validated, should form part of a larger model which will also represent the steel stockholding and steel manufacturing industries. Work on the larger model is in progress.
Even the experienced practitioner of system dynamics can encounter serious conceptual problems in getting started on a model, and is tempted to add more and more to his model. A technique â âlist extensionâ â is described which, from the purpose of the project and the importance of feedback loops, guides the evolution of the simplest adequate model. This model is expressed as an influence, or causal loop, diagram. The influence diagram should be tested to ensure that its structure contains the necessary elements of a dynamic model. If it fails the test attention is directed to the area of the system where further elucidation is needed. The techniques have been applied in many practical cases and have been found to give useful results and to increase the efficiency of the modelling process.
Views of knowledge contain methodological theories--theories of how knowledge progresses-- and epistemological theories-- theories about the nature of knowledge. The former serve four particularly important functions: providing formulas for the generation of knowledge, criteria for the legitimation of knowledge, reasons to suspect other ideas, and rules for the propogation of ideas.
This paper presents a conceptual framework for understanding the influence of alternative paradigms on policy conclusions. Two types of assumptions are associated with mathematical models--meta-assumptions or methodological priors and specification assumptions. Because two different paradigms must assume two different sets of methodological priors, the possibility exists that different problem definitions and hence policy conclusions may emerge from two parallel studies of the same area. In each of two cases presented here, a single problem area has been analyzed with two different methodologies. In each case, different policy conclusions have been reached as a result of the different methodological priors of the two paradigms. The first case involves two models used to analyze changes in retirement policies within the military enlisted system of the United States Armed Services. The second case involves two models used to analyze the determinants of equal educational opportunity in the United States. The dependence of the policy conclusions upon the analytic paradigm employed in a given study has important practical implications for the use of quantitative models in the analysis of social policy situations.
