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System dynamics refers to a technology to model complex systems created by Jay Forrester. It also represents a way to think about complex systems, as argued by Peter Senge. A complex dynamic system is one with many components that interact in ways that change the observed behavior of the system over time. An example is our solar system. One can think of the sun, the planets, and their orbiting satellites as the components. All of these components are moving with their movements determined by the physical laws of nature (e.g., gravity). If one wishes to place an object in orbit around one of these components (a system intervention), one must understand the dynamics of the entire system. Many complex and dynamic systems allow for the direct manipulation of one or more factors or components that influence the behavior of the system. An example is a supply-chain system in which a corporation orders raw materials to produce items (supply) based in part on past, current, and projected orders (demand). The number of orders changes over time, and there can be a delay in getting raw materials and producing the product. The complexity of managing such delays and changes in demand requires understanding internal feedback loops (e.g., receiving more orders creates an increase in demand for raw materials, which eventually leads to more production, which can reduce the number of pending and anticipated orders). There is a cost associated with overproduction (e.g., excess inventory storage, which can also affect price) as well with underproduction (e.g., unsatisfied customers, which can affect demand), so such dynamic and deep understanding is critical for efficiency. Many other examples of complex and dynamic systems that require decision making and policy formulation can be found in nearly every human enterprise. As a consequence, understanding the complexities of a system (delays, nonlinear relationships, internal feedback, etc.) is a highly valued competency in many decision-making and problem-solving task domains. As such, understanding complexity and tools and technologies used by system dynamics professionals is pertinent to many educational technology applications.

System Dynamics Models and Representations

The fundamental model used in system dynamics is called a stock and flow model. However, in order to create such a model, system dynamicists typically engage in a knowledge elicitation process and create an initial causal loop diagram (aka a causal influence diagram). Both can be useful in supporting learning and instruction, as argued by Marcelo Milrad and colleagues, and the stock and flow model can serve as the basis of an interactive simulation when the components are mathematically specified, according to John Sterman. A causal loop diagram is intended to represent all of the major factors and their relationships that are involved in a complex and dynamic system. While its primary use is in eliciting information from experts about the system, it is also used to convey information to others about how a complex system operates. In addition, the process of developing a causal loop diagram has been shown to be an effective indicator of how well someone understands the complexities of a problematic situation and, as a consequence, can be used for assessment and feedback to support the learning process. Figure 1 is a simplified causal loop diagram representing a population model. Note that two death rates are included—one for adults and one for children. The person developing the diagram could have included additional death rates (e.g., to explicitly represent infant mortality rates or death rates for adolescents). The point here is twofold: (1) first, there is typically not a single correct representation for a complex and dynamic situation, and (2) the representation should include components that might be targets for human intervention. With regard to the latter, it could easily be imagined that proper prenatal care might impact infant mortality rates; therefore, it could be argued that this relationship should be made explicit in the figure. The purpose and projected use of the representation is an important consideration. A general advantage of a causal loop diagram is that it supports a holistic view of a complex and dynamic system in a single figure represented on a single page or screen. Such representations address a common deficiency in human reasoning—namely, the tendency to ignore significant portions of a complex system, as shown by Dietrich Dörner as well as by Peter Senge. As Figure 1 indicates, delayed effects can also be represented, which is an additional challenge; all too often, humans expect to see nearly instantaneous effects of a decision or action, but real systems often involve significant delays. Positive and negative feedback loops are depicted through the use of influence indicators. In Figure 1, a plus sign indicates a change in the same direction between the connected nodes, while a minus sign indicates a change in the opposite direction between the connected nodes. In general, a loop with an odd number of minus signs is a negative feedback loop (aka a balancing loop), while a loop with an even number of minus signs is a positive feedback loop (aka an escalating loop). The loop connecting children and maturing adolescents is a balancing loop as there is one minus sign in that loop.

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