Meeting 21st centurys challenges of climate change and scarcity of crude oil requires the transition to alternatively powered vehicles, such as electric vehicles. As a consequence, car manufacturers have to integrate these vehicles into their product portfolios. Decisions have to be made about, for instance, the power-train to be offered in specific vehicle models and their times of introduction. This is a complex decision making task, especially due to high uncertainties about the future development of the market demand for alternatively powered vehicles.
Introducing System Dynamics(SD) to solve a complex problem is difficult in two ways: 1) modeling the problem behavior, and 2) selling this approach as a desirable alternative to past troubleshooting methods. Adding the new concept of SD to the problem-solving mix often results in resistance. The perception is that other solutions worked in the past and there isn't time to learn new methods. This classic Limited Growth Archetype is best managed by addressing the balancing loop factors that limit adoption of change. In other words, the change agent needs to identify opponents and change their minds. In this paper I suggest that there is another way: provide the concepts and lessons without recipients being aware.
The financial crisis shifted the focus of monetary policy. Whereas before the crisis the main goal of using monetary policy instruments was to keep the inflation rate low after the crisis policy makers put much emphasis on stabilizing the financial system. The economic literature has started to elaborate on the issue of macroprudential regulation only recently. Financial turbulences, by their very nature, constitute a complex dynamic phenomenon. Hence, an analysis employing tools of system dynamics should help to improve our understanding of the underlying feed-backs. In order to link economic reasoning and the systems approach a model of financial behavior developed by Stein is introduced and used to create building blocks for a basic dynamic simulation model.
Alcohol use is prevalent among college students in the US and is the leading cause of many alcohol-related consequences such as injury, driving under influence, and sexual assault. The problem of college drinking involves complex individual, social, and cultural factors. By viewing college drinking as a complex system problem, this paper describes two components necessary for the full development of a simulation-based dynamic agent model for alcohol use in college. The first component is a basic agent-based model that explores the dynamic of college drinking. The second component discusses the use of system dynamic modeling to explore the causal relationship between various personal/environmental factors and alcohol consumption. The paper also discusses important leverage points for intervention strategies, especially in the context of targeting both high-risk and low- to medium-risk drinkers in college.