The impossibility to identify and represent events and emergent characteristics of the system analyzed with computer simulation models aimed at projecting future events has posed serious questions about their validity in the field of social science. While methodological issues, both concerning the foundations of the methodology and the formulation of models, are identified, the System Dynamics methodology seems to allow modelers to gain a deep understanding of the systems studied while answering the four dilemmas identified in this study. These models allow for the structural representation of the system through the identification of causal relations underlying its main functioning mechanisms, represent both dynamic and detailed complexity using wide social, economic and environmental boundaries. Dynamic simulation models are by no means perfect and will never be; nevertheless, modelers have the responsibility to use our best scientific understanding to develop reasonable and sustainable policies. T21 and PCM, integrated national development models, allow us to do so by enhancing the understanding of systems.
The US Government faces some of the biggest strategic challenges with very high stakes. When should we use military power and how do we use it to improve stability rather than worsen it? How should we manage a huge but limited budget to achieve a wide range of big, unstructured, and evolving strategic objectives? Can we help struggling regions develop and if so how? These questions involve highly complex systems with many dynamics over time. They have the further challenges of a lot of uncertainties, limited data, wide boundaries, many stakeholders, and the need for rapid decision-making. In an environment fraught with politics and skeptics, how can we use analysis to improve the rigor of decision-making on such big and important questions? The authors will use examples from the last 8 years of policy and decision analysis work to illustrate lessons both positive and painful for successful use in government and business, speaking from the perspectives of both modeler and decision-maker, inside and outside the government.
Understanding population dynamics is crucial to understanding current and future health care needs and designing systems to meet those needs. In this paper, we provide a methodological approach to answering questions of population dynamics in a system dynamics model configurable to initialise in dynamic equilibrium or disequilibrium. Some questions include: how does current measured population compare to a population of the same size in equilibrium, and how would a change from current observed birth rate to equilibrium birth rate affect population levels and the dependency ratio over time? To illustrate the methodology, we apply our approach to Singapore, which is experiencing an increasing proportion of the elderly population.
The recent attempted or actual overthrow of long incumbent governments in Egypt, Iran, Libya, Algeria, and Tunisia, the continuing engagement of Western powers in a number of counterinsurgencies, and a rise in global religious, nationalist, narco- and cyber-terrorism have highlighted the continuing importance of modeling conflict, defense, and security issues. System Dynamics, because of its ability to integrate political, organizational, and material factors, is ideally suited as a vehicle to investigate these great problems. This potential, however, contrasts greatly with the relative sparseness of academic publications in this area.
The system dynamics contribution to project management research is one of the jewels of the crown with respect to our field. One of its key contributions is to understand how iteration of tasks in the form of project rework influences key outcomes measurable such as cost and timing. We build upon this tradition in a conceptual paper that suggest that other types of iteration, which are mindful and beneficial, need to be considered. When considered, two particular concerns fall out with respect to innovation projects that need further exploration by system dynamicists. One is that some projects, particularly innovation projects, may need to change scope mid-way through the project if they are to be successful, and there is no way to anticipate this ex ante. The other is that innovation projects tend to iterate in scope from project to project and that this iteration is necessary because of the escalation of market expectations. We explore the implications of these ideas and how they should impact the course of future system dynamics research.