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2016 | Buch

An Introduction to System Dynamics Kapitel lesen Erstes Kapitel lesen

System Dynamics Modeling with R

verfasst von: Jim Duggan

Verlag: Springer International Publishing

Buchreihe : Lecture Notes in Social Networks

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This new interdisciplinary work presents system dynamics as a powerful approach to enable analysts build simulation models of social systems, with a view toward enhancing decision making. Grounded in the feedback perspective of complex systems, the book provides a practical introduction to system dynamics, and covers key concepts such as stocks, flows, and feedback. Societal challenges such as predicting the impact of an emerging infectious disease, estimating population growth, and assessing the capacity of health services to cope with demographic change can all benefit from the application of computer simulation. This text explains important building blocks of the system dynamics approach, including material delays, stock management heuristics, and how to model effects between different systemic elements. Models from epidemiology, health systems, and economics are presented to illuminate important ideas, and the R programming language is used to provide an open-source and interoperable way to build system dynamics models. System Dynamics Modeling with R also describes hands-on techniques that can enhance client confidence in system dynamic models, including model testing, model analysis, and calibration. Developed from the author’s course in system dynamics, this book is written for undergraduate and postgraduate students of management, operations research, computer science, and applied mathematics. Its focus is on the fundamental building blocks of system dynamics models, and its choice of R as a modeling language make it an ideal reference text for those wishing to integrate system dynamics modeling with related data analytic methods and techniques.

Inhaltsverzeichnis

Frontmatter
Chapter 1. An Introduction to System Dynamics
Abstract
This chapter presents important concepts underlying the system dynamics modeling method. Following an initial definition of the term model, a summary of a successful system dynamics intervention is described. The key elements of system dynamics—stocks and flows—are explained. The process for simulating stock and flow models—integral calculus—is described, with an example of a company’s customer base used to illustrate how stocks change, through their flows, over time. A summary of dimensional analysis for stock and flow equations is provided before the second feature of system dynamics modeling—feedback—is presented. The chapter concludes by summarizing the system dynamics methodology, which is a five-stage iterative process that guides model design, development, test and policy design.
Jim Duggan
Chapter 2. An Introduction to R
Abstract
This chapter introduces R, a dialect of the S language, which was developed at Bell Laboratories. R’s inventor Dr. John Chambers was awarded the 1998 Association of Computing Machinery Software award. In its citation, the ACM noted that S will forever alter the way people analyze, visualize, and manipulate data. R’s mission is to enable the best and most thorough exploration of data possible. R is open-source software (GNU General Public License), and has statistical, data manipulation, and visualization libraries. R is a functional programming language, where software programs are organized into functions that can be invoked to transform data. This chapter describes key R elements, including vectors, lists, matrices, data frames and functions. It concludes by presenting a system dynamics model of customer growth, which is implemented using the deSolve open source package. Appendix A summarizes the installation process for R, and the reader is recommended to work through this chapter using the R Studio console, so that the short examples can be executed.
Jim Duggan
Chapter 3. Modeling Limits to Growth
Abstract
This chapter introduces system dynamics models of limits to growth. First, a one-stock model is presented, where the growth rate varies, and is influenced by the system’s carrying capacity. Second, a model of economic growth is described, which captures the law of diminishing returns, a feature of many economic systems. Third, a two-stock model of limits to growth is specified, where a growing stock consumes its carrying capacity, and this dynamic leads to growth followed by rapid decline. Before introducing the limits to growth models, an explanation of an important formulation method in system dynamics is presented. This allows modelers to construct robust equations to model the effect of one variable on another. This is useful for many system dynamics models, particularly where one system stock influences another system stock.
Jim Duggan
Chapter 4. Higher Order Models
Abstract
This chapter presents a higher order model, which has a greater number of stocks and feedbacks than those presented in earlier chapters. This is an important perspective, as real-world system dynamics models tend to have a significant number of stocks. To aid understanding, higher order models are often sub-divided into distinct sectors, where each sector contains a recognizable sub-system. This higher order model represents a primary health care system that models an aging demographic, the supply of general practitioners, and the annual demand the population places onto the primary care system. Before presenting this model two important modeling constructs are described. These are delays, which allow modelers to simulate time lags, and the stock management structure, which provides a structure to simulate how decision makers regulate the stock levels.
Jim Duggan
Chapter 5. Diffusion Models
Abstract
This chapter focuses on diffusion, which is a common feature of many social and biological systems. Innovative consumer products frequently “take off” and “go viral”, with sales driven by the word of mouth effect, as their adoption spreads through a population. Infectious diseases can transmit rapidly through a population, accelerating from seemingly low incidence levels, to sizable numbers in a short space of time. Here, the focus is on models of infectious diseases. These have an important decision support function for public health professionals faced with challenge of responding to an infectious disease outbreak. The first model is the classic SIR structure, which divides the population into those who are susceptible, infected and recovered. This model is then extended to cater for multiple age cohorts, so that diverse mixing patterns can be simulated. Finally, a scalable R model of infectious diseases is introduced, combining matrix operations with vectorized differential equations.
Jim Duggan
Chapter 6. Model Testing
Abstract
This chapter provides an overview of model testing in system dynamics, and presents practical methods—using the R framework—that can be used to develop automated model tests. An important challenge in system dynamics is to build client confidence in models. While there is no single test that serves to validate a system dynamics model, confidence in a model gradually accumulates as the model passes more tests. Testing should not be designed to prove that a model is right, as all models are simplified representations of the world. However, models can be useful, and performing a wide range of tests on models can uncover errors. The chapter shows how R can be used to support automated testing of system dynamics models, and also how the concept of the atomic behavior pattern can support behavior tests.
Jim Duggan
Chapter 7. Model Analysis and Calibration
Abstract
This chapter introduces methods that support policy analysis for system dynamics models. First, a mathematical method for calculating loop polarity is presented, and this formal approach can be used to detect shifts in loop dominance, for example, when two feedback loops compete to influence a stock’s value. Second, statistical screening is summarized, and this allows for an exploratory analysis of a system dynamics model in terms of analyzing which of the many uncertain parameters stand out as most influential. Third, model calibration is explored, which is a valuable technique based on optimization methods. This approach can be used to fit model parameters to historical data. In turn, this can improve client confidence, and also provide good parameter estimates that can form the basis of policy design and analysis.
Jim Duggan
Backmatter
Metadaten
Titel
System Dynamics Modeling with R
verfasst von
Jim Duggan
Copyright-Jahr
2016
Electronic ISBN
978-3-319-34043-2
Print ISBN
978-3-319-34041-8
DOI
https://doi.org/10.1007/978-3-319-34043-2

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