This paper discusses the reasons why Systems' Dynamics models frequently encounter considerable difficulties in gaining acceptance and suggests several ways for overcoming this obstacle. Resistance to models within organizations is usually generated by one or several of the following causes: insufficient credibility of model's proponents, inability to grasp model's usefulness, cultural background, fear of losing power and negative previous experience with models. In the special case of models addressing issues of wide public interest suggestions are presented on how to plan a communications strategy designed to generate support for the model or for the conclusions derived with its help.
This paper discusses some dynamic effects of robot's introduction on a company in the electric appliances industry. Two key aspects are analysed. The effects on cash flow are explored first, the conclusion is reached that under certain conditions it could represent a controlling element that would slow down the rate of robots' introduction with the respect to the ideal rate suggested on the sole basis of economic convenience. The availability of skilled personnel is considered next. This availability increases through on the job training as more robots are installed. Under most circumstances, however, the availability of skilled technicians represents a controlling element that definitely slows down the introduction of robots. The effectiveness of training technicians therefore represents a variable of strategic importance.
This paper describes the importance of feedback loops included in a policy model constructed for the Office of Conservation of the Bonneville Power Administration (BPA). First there is the description of the region and the responsibilities for conservation planning at the BPA, and then a description of the purpose, structure, and use of the policy model. Several feedback loops involving customer response to higher electric rates are selected for our discussion of feedback. The system dynamics treatment of these feedback loops is contrasted with the treatment found in most electric utility planning models in the USA. The paper concludes with an assessment of whether the inclusion of feedback has been important in BPA's application of the model.
This paper reviews techniques that may assist the system dynamics modeller in defining variables and functional relationships, parameter estimation, validation, sensitivity and policy analysis. The evaluation was made in the context of water resources management modeling effort for the Guadiana basin in Algarve and based on scientific, economic and operational criteria. In general, it was difficult to point out the most appropriate technique but rather recommend combinations of methods for each modeling stage.
This paper introduces a new linguistic dynamic simulation methodology, SLIN which deals with systems defined in either qualitative or quantitative terms. The simulation mechanisms proposed in SLIN include a set of logical rules and fuzzy set theory. An application of SLIN to Sado estuary showed its promise but also some of its present limitations. Future developments including an appropriate diagrammatic representation, a new linguistic simulation computer language, implementation in parallel computers and subsequent real-time multi-expert based simulation are also discussed.
Low Back Pain (LBP) is the most common cause of work loss after the ordinary cold, and it is the single greatest source of compensation payments. In the U.S., it is estimated that one million workers sustain a low back injury every year, and that 217 million work days are lost annually at a cost of 11 billion dollars for males aged 18-55 alone. In an effort to better understand how to control the economic impact of this disorder, a System Dynamics model is being developed. It is hoped that the model, by generating scenarios on the cost effectiveness of different interventions, will provide useful insight into specific policies to fun research addressing the causes of LBP disability.
The techniques currently used for the management of urban road transportation systems are briefly reviewed, and the extent to which they take account of the dynamics of the system examined. Recent work on the development of mathematical models of urban traffic systems is described and the applicability of the model to real-life traffic systems explored. In particular, the ability of these models to reflect temporal as opposed to spacial properties of the system is examined, as well as their ability to assist in the formulation of strategies for system control. The role of system dynamics might play in overcoming some of the problems encountered is then discussed.
Models based on a logic relating military ownership costs to active force assets were developed. Historical budget analyses provided relationships to tailor the models to each military service. The models, validated through projection of the 1980-85 defense growth period, were then used to predict 1986 to 1995 appropriations using top line fiscal levels as inputs. The models can explore policy options such as reduced fiscal growth, altered readiness policy, and changed innovation plans.
Traditional economic theory emphasizes the determination and characterization of static equilibrium. In contrast, understanding of economic behavior can be enhanced through the use of models that explicitly take into consideration the underlying physical and decisionmaking structure of the system and that allow for disequilibrium. This paper presents an example of such a model. A typical static, open input-output model is translated into an equivalent disequilibrium model. It is shown that objective individual decisions can lead to unintended oscillatory modes of behavior of the overall system. An assumption of perfect information can prevent such undesired oscillations. This paper also demonstrates a way of communicating system dynamics thinking to an economics audience. The model is developed in progressive steps, a procedure that is widely found in economic literature. First, stocks are added to a model that originally considers flows alone. Each of three succeeding model changes is then motivated by the results of the previous model and presented as a logical next step towards a more consistent theory. Thus, it is not only the result of the final model as such that is of interest, but also the way the model is developed. Model development is presented as a learning and communication process.
The Le Moigne's theory of General System is presented and applied to the transportation system. A model of this system, using accessibility and generalized cost as the variables to be controlled is also sketched.