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.
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.
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.
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.
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.
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.
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.