System dynamics uses computer simulation modeling to show how stocks and flows cause the behavior of systems. System dynamics is based on an underlying body of theory and principles that permit modeling of physical and social systems. Such models are role-playing or laboratory replicas of actual systems that allow investigation of how organization and policies cause good and bad behavior. From such laboratory experimentation students develop insights that, it has been shown, they carry with them into later careers.
- System dynamics gives students a basic understanding of the underlying causes of change.
- They learn that problems and their solutions arise from the feedback structure of a system.
- They become able to see the big picture, patterns of behavior over time, and similarities across systems.
- They know that policy decisions often entail trade-offs, and unintended consequences.
- They see themselves as an integral part of a larger system with a shared responsibility for the common good. They believe that what they do makes a difference.
- System dynamics was originally developed to understand and to change behavior and the reasons for successes and failures in corporations. Jay Forrester and others expanded its use to address global climate change, health care reform, world sustainability issues, biological systems, physical systems, economic systems and urban systems. All are complex systems lying beyond reach of current thinking.
“So if we want to bring about the thoroughgoing restructuring of systems that is necessary to solve the world’s gravest problems — poverty, pollution, and war — the first step is to think differently. Everybody thinking differently.”
— Donella Meadows
System dynamics provides a framework for interdisciplinary problem-solving. Students are engaged as teams and on their own in experience-based investigation with the teacher as a guide. In pioneering schools we repeatedly observe that all students, independent of prior background and academic record, move readily into this computer-based experimental mode of learning.
The information above is excerpted from the Essex report, “The Future of System Dynamics and Learner-Centered Learning in K-12 Education,” with modifications by Jay Forrester, Lees Stuntz, Tracy Benson, and Diana Fisher. Reprinted here with permission.
Most people understand that the thermostat in their home adjusts the operation of their furnace by determining the difference between the desired temperature set on the thermostat and the current temperature of the room in which the thermostat operates. If the house temperature falls below the desired temperature the furnace is turned on. If the house temperature reaches the desired temperature the furnace is turned off. There is a feedback process that is controlling the thermostat. Many processes in nature, in physical devices, and in social interchange involve feedback interaction. Understanding the power of feedback to control system behavior is THE foundation principal underlying complex systems analysis using the system dynamics modeling method.
There are only two types of feedback, reinforcing (or positive) and balancing (or negative) feedback.
Reinforcing feedback causes the state of a changing (dynamic) system to undergo accelerated change. An example is the compounding process of money in an interest bearing bank account.
The more money in a bank account the more interest that is added each time interest is compounded, increasing the money in the account. Reinforcing feedback is also present in the relationship between births and population. The more people there are the more births there are each year, adding even more people to the population.
Balancing feedback causes the state of a dynamic system to undergo decelerated change. That is, it tries to pull the system toward some steady value.
An example of this is type of feedback can be found in the relationship between population and deaths. The more people in a population the larger the number of deaths there will be per year. But the more deaths there are per year the fewer people there will be in the population. With fewer people there will be fewer death. Eventually, the population will reach a stable value, even if that value turns out to be zero.
Complex systems can contain multiple reinforcing and/or balancing feedback loops. These loops interact with each other. Some loops may dominate at certain times in a simulation and not at other times. Feedback interaction is what makes complex system behavior complex, and interesting. A very simple two-feedback loop example is population, since it contains both births and deaths. There is a sample population diagram on the Reading a Stella Model page.
That simple two-loop diagram can produce three simple behaviors for a population, growth, decay, or equilibrium. With a small modification, it can produce even more interesting behavior, where loop dominance changes.
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