Theoretical reflections about System Dynamics (SD) have usually been grounded in the developments of what can be called general philosophy of science. In our paper, a philosophical approach more sensitive to the peculiarities of SD is proposed that is closely linked to the recent constructivist proposal of John Searle and to the expressivist theses of Robert Brandom. We will focus on three very important conceptual problems the ontological problem of realism concerning the structures postulated by SD models, the epistemological problem of the explanatory power of SD models, and the methodological charge of merely producing a kind of patchwork when building of SD models--, arguing that by combining the constructivist and expressivist philosophical perspectives of those authors in a certain way would offer a better understanding of scientific and technical activities such as SD modelling.
After the Islamic Revolution in 1979, Iran had to face another challenge: the war against Iraq. This challenge forced the government to help people by granting subsidy to essential goods such as bread, drugs and different kinds of energy - especially electric power which is one of the major industries in every country. This policy helped people have an easier life during the war, but as the famous law of supply demand tells us, the lower the price of any good, the higher demand for that good is predicted and this low price of energy made Iran one of the most and worst energy consumers in the world. This high rate of consumption will cause lots of problems such as lack of electricity and financial pressure on the government. In this paper, a system dynamics model is developed to simulate the situations of Irans electric power industry since 15 years ago, assuming the effect of peoples pressure on the government and the pressure of the government to decrease subsidy. The main model is built on two positive and negative loops and the results are compared with the real statistics. Then, two policies are applied to the model: education and increasing the price.
Obtaining insight into the effects of policy interventions is often a difficult matter. A new method to obtain a first insight into those effects is presented in this paper. The basis of the method is a Causal Loop Diagram to which information on causal relations and variables is added. Part of the information is expressed in qualitative terms. This Method to Analyse Relations between Variables using Enriched Loops (MARVEL) takes proposed interventions as a starting point. Interventions are interpreted as imposed changes on selected model variables representing intervention points. A new feature is that causal relations are no longer passive but active model elements. They propagate the changes through the model in a time-dispersed way. MARVEL determines how this causes (other) variables representing the models performance to change in the desired direction at selected moments in time. MARVEL can be used for policy development, policy analysis and policy evaluation problems.
Enloe Medical Center is a non-profit community hospital in Chico, California. Among the many services they provide is a Labor and Delivery Department. While mothers are routinely admitted from 1:00pm to 1:00am, they are generally discharged between 10:00am and 5:00pm. This results in a generic bell curve behavior pattern for patient occupancy during the daytime. Hospitals are reimbursed for inpatient services in two major ways: either on a per diem basis, or by diagnosis related groups (DRG). Either way, the revenue to the hospital remains the same, regardless if the patient is discharged at 4:00am or 4:00pm. In California, state mandated nurse to patient ratios require hospitals to maintain a minimum level of nurse staffing for inpatient services. Thus, as the patient census rises during the day, so must the number of nurses on staff. This is the problem studied; costs expended for patient discharge delays.
System Dynamics (SD) would like to increase its influence and promote its professional approach to understanding and solving significant problems. This paper attempts to capture a snapshot of SD research topics, and publications 2000-2005 as a metric of the scope of SD domains and publication venues. The questions addressed by this paper follow: What are the frequently published topics? What may be the emerging topics? Where do SD authors frequently publish? To this end, 35, 920,686 documents were search. Of the total, 935 met the search criteria. After review and analysis of the 935 documents, only 302 qualified as relevant SD material for further analysis.
Nowadays, the performance measurement system has been well developed. And the relations between these performance measures are playing important roles in management science. However, the effective method to analyze these relations is still underdeveloped and attracts more and more concerns. After reviewing relevant research, by adopting and further extending the essential theories of Systems Thinking, we propose a three-dimensional Systems Thinking to achieve better analysis, control and decision-making. In this paper, the rationality of the three-dimensional thought is proved first, and the modelling method is then provided in theory. Finally, a manufacturing enterprise is illustrated as an example for practical implementation.
As part of the first authors PhD project, year 9 and 10 students were given a system dynamics model of the impacts of visitors on a National Park. The students were given a pre- and post-test to determine whether their knowledge of the environment changed. Students were randomly assigned to either the individual learning condition (students interrogated the model as individuals) or the collaborative learning condition (students interrogated the model with one other student). There was a significant increase in the environmental knowledge score for those students in the collaborative learning condition, but not in the individual learning condition. The implications of this finding for the use of system dynamics models in educational settings are discussed.
Goodwins A Growth Cycle [1967] represents a milestone in the non-linear modeling of economic dynamics. In terms of the two variables wage share and employment rate and on the basis of few simple assumptions, the Goodwin Model (GM) is formulated exactly as the well-known Lotka-Volterra system, with all the limits of such system, in particular the lacking of structural stability. A number of extensions have been proposed with the aim to make the model more robust. We propose a new extension that: a) removes the limiting hypothesis of Harrod-neutral technical progress: b) on the line of Lotka-Volterra models with adaptation, introduces the concept of memory, which certainly plays a relevant role in the dynamics of economic systems. As a consequence an additional equation appears, the validity of the model is substantially extended and a rich phenomenology is obtained, in particular transition to chaotic behavior via period-doubling bifurcations.
System dynamics has been successfully applied to the study of projects for many years. While this modeling has clearly defined the structures which create project dynamics, it has been less helpful in providing explicit policy advice to managers. To address this gap, we examine the effectiveness of three common project controls available to project managers to address deviations in project performance; (1) exerting pressure on project staff to work faster, (2) having staff work overtime, and/or (3) hiring additional staff. While the three project controls can have short-term benefits for project performance, their long-term impacts can be detrimental. The current work presents preliminary results of the research, focusing on the impacts of the three project controls on project rework and schedule performance. The work describes the development of project control feedback structures, the initial testing and use of a formal system dynamics model of the system, and preliminary results. The work concludes with a description of future project research efforts.
System dynamics models are often constructed to improve system performance by identifying and modifying feedback mechanisms that drive system behavior. Once identified, these feedback mechanisms can be used to design and test policies for system performance improvement. A preliminary step in developing policies is the identification of high leverage parameters and structures, the influential model sections that drive system behavior. The current work clarifies and extends the use of statistical screening (Ford and Flynn, 2005) as a model analysis tool with a six step process that identifies specific model sections for further analysis and development. The work also presents a method that clarifies the results of model analysis with statistical screening to practicing managers Statistical screening offers system dynamicists a user-friendly tool that can be used to help explain how model structure drives system behavior.