The scientific technique known as the method of multiple hypotheses can be adapted to suit the purposes of system dynamics policy modeling. This method would allow determination of a model’s value through comparison with other competing models. It would also diminish modelers’ emotional attachment to any single theory. But in adopting this method, system dynamicists would need to develop a new philosophy of model evaluation, emphasizing disproof over verification and comparison among theories over improvement or elaboration on a single model.
The SD approach is based on control theory. As with general system theories, it postulates that system structure causes system behavior. Computer simulation used to be the only means of solving complicated models at the time SD was invented. Therefore: (1) only system structure and system behavior could be used as yardsticks in model validation (2) without an intuitive or intelligent guess, that related structural explanation to model behavior, all modelling work would have been fruitless or at least extremely laborious. In computer simulation, no automatic feedback from model behavior to structural changes is feasible. A human link is needed and, therefore, system dynamists have rightly argued about the significance of insights gained from the very modelling process. Without insights, no feedback mechanism would work properly in model construction. The authoritarian relationship between man and machine, prevailing in SD, is an outgrowth of this situation.
Our work during the past several years leads us to believe that there now exists a small but significant number of American corporations engaged in daring experiments in organizational transformation. These companies fundamentally alter our understanding of how a group of people can work together. They are committed to the absolute highest in organizational performance and human satisfaction. They view themselves as microcosmic demonstrations of how society could work towards everyone’s fulfillment.
This paper is destined not so much for those who are present at this conference than for the members of the business community whose absence constitutes one of the main problem facing System Dynamics. Indeed, since its inception more than 20 years ago, quite a few Industrial or System Dynamics publications have dealt with industry or government related applications. However, very few of those have been effectively developed within and present by business or government representatives.
In order to investigate the regulation of breathing under various conditions, we have developed a dynamic model of the human respiratory and cardio-vascular systems. The model describes the flows of oxygen and carbon dioxide between the atmosphere and the tissues as well as the chemical regulation of breathing in a rather detailed manner. When testing the model with a step increase in muscular metabolism (simulating a transition from rest to physical activity), it reproduces clinical observations for the variation in ventilation and in arterial oxygen and carbon dioxide pressures. The model also reproduces the respiratory response to changes in the composition of the inspired air. Combined with a model of the Hafnia A anaesthetic system, the respiratory system model has finally been used to examine the life-threatening dynamical run-away effects which may occur, if the fresh gas flow is reduced too much, and the patient starts to rebreathe his own expiration.
This paper explores the possible paths of emergence of a new medical technology and how those paths might be altered by government regulations of the sort now promulgated by the Food and Drug Administration (FDA). The purpose of the paper is to help clarify the role of FDA regulation in a dynamic context. The analysis focuses on the idea that an emerging technology’s effectiveness may change over time and that the benefits and losses due to regulation may themselves have a dynamic character. An increasingly complex story of the emergence (or dissemination and development) process is told with the help of causal-loop diagrams. Results from a preliminary system dynamics model based on this story are illustrated and discussed. They suggest that the FDA’s actions may have unintended effects, such as slower development of a technique, which may or may not be harmful. They also suggest that, in certain cases, post-marketing surveillance and communication of results may be at least as important an activity for the FDA as pre-marketing evaluation.
Planning and Control are essential for the success of any human endeavour and are now widely established concepts in most organizations, usually enshrined in formal corporate/planning systems. The process of planning may be analysed in a number of different ways, but generally there is a consensus on the need to split the process up into strategic planning, which directs the organization and tactical or operational planning which deals with the resource allocation for specific operational units and integrates them into the whole.
The model in this paper is, therefore, directed towards an understanding of the mechanisms at work during the UK business cycle. Its time horizon is no greater than ten years, with the main emphasis on the next five. It is frequently argued that cycles of longer period than the business cycle exist, e.g. the 50 year long wave. It is not intended that this model should try to capture in detail the mechanisms believed to produce them. However, their role in determining the underlying trend must be recognized, and their effects incorporated exogenously, perhaps by reference “off-line” to other models designed to look at these more distant horizons.
Since 1972, Jay Forrester and colleagues at MIT have been evolving the System Dynamics National Model (SDNM). The purpose of this model is to guide policy makers in dealing with today’s major problems. The ambitious scope of the project motivates careful examination of modeling practices and how they contribute to the success of the project. The above paper recounts incidents in the development of the SDNM and discusses the related modeling issues.
The purpose of this paper is to introduce an integrated framework for long-range strategic planning to a railroad. The framework is a computer simulation model designed to be useful to most freight –hauling railroads. The model can help to increase the understanding of problems facing the railroad and to aid in developing strategies for addressing these problems. It is designed to forecast railroad performance and to aid in developing more effective policies for railroad management. It can also be used by Federal agencies to evaluate impacts policy on railroad performance.