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Titlebook: Dynamic Modeling; Bruce Hannon,Matthias Ruth Book 19941st edition Springer-Verlag New York, Inc. 1994 Dynamics.computer simulation.model.m

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ted with each simulation run. Real systems are not necessarily that predictable. Rather, some random element may determine the direction of a system’s behavior. This case is illustrated in the model developed in this chapter. Other sources of unpredictability of system behavior are discussed later in the book.
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apter we turn our attention to positive feedback processes. Positive feedback reinforces a given tendency of a system and can lead a system away from equilibrium states, possibly causing quite unexpected results.
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d condition has been found. For example, we may be modeling a firm that attempts to maximize its profits. We will start the firm off with a low output per time period and calculate the corresponding profit. Then we incrementally increase output over time, thereby increasing revenues from sale of tha
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Book 19941st editionmputers, as well as computer files of sample models used in the book. Dynamic Modeling offers a clear, approachable introduction to the modeling process, and will be of interest in any field where real problems can be illuminated by computer simulation.
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s-based computers, as well as computer files of sample models used in the book. Dynamic Modeling offers a clear, approachable introduction to the modeling process, and will be of interest in any field where real problems can be illuminated by computer simulation.978-1-4684-0224-7
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Derivatives and Lagsd condition has been found. For example, we may be modeling a firm that attempts to maximize its profits. We will start the firm off with a low output per time period and calculate the corresponding profit. Then we incrementally increase output over time, thereby increasing revenues from sale of tha
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Gradual Development of a Dynamic Modela simple system under alternative assumptions, this chapter shows how to expand and refine a model. Additionally, we want to stress the need for treating consistently the units in which parameters and variables are measured.
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Two Independent Variables change in our models. First we introduce several stock variables to indicate various temporal aspects of the state variable of the system. Such a disaggregation can be done, for example, for a population into different age classes. Here we deal only with a more sophisticated representation of essen
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Randomnessfined and initial conditions were specified within certain bounds, the dynamics of the system as a whole were predetermined and could be exactly repeated with each simulation run. Real systems are not necessarily that predictable. Rather, some random element may determine the direction of a system’s
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