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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.