- Pasta blanda: 482 páginas
- Editor: Mit Pr (3 de abril de 2015)
- Idioma: Inglés
- ISBN-10: 0262731894
- ISBN-13: 978-0262731898
- Dimensiones del producto: 20.3 x 2.5 x 22.9 cm
- Peso del envío: 1.3 Kg
- Opinión media de los clientes sobre el producto: Sé el primero en calificar este artículo
- Clasificación en los más vendidos de Amazon: nº48,285 en Libros (Ver el Top 100 en Libros)
An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems With Netlogo (Inglés) Pasta blanda – 3 abr 2015
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The advent of widespread fast computing has enabled us to work on more complex problems and to build and analyze more complex models. This book provides an introduction to one of the primary methodologies for research in this new field of knowledge. Agent-based modeling (ABM) offers a new way of doing science: by conducting computer-based experiments. ABM is applicable to complex systems embedded in natural, social, and engineered contexts, across domains that range from engineering to ecology. An Introduction to Agent-Based Modeling offers a comprehensive description of the core concepts, methods, and applications of ABM. Its hands-on approach -- with hundreds of examples and exercises using NetLogo -- enables readers to begin constructing models immediately, regardless of experience or discipline.
The book first describes the nature and rationale of agent-based modeling, then presents the methodology for designing and building ABMs, and finally discusses how to utilize ABMs to answer complex questions. Features in each chapter include step-by-step guides to developing models in the main text; text boxes with additional information and concepts; end-of-chapter explorations; and references and lists of relevant reading. There is also an accompanying website with all the models and code.
Biografía del autor
Uri Wilensky is Professor of Learning Sciences, Computer Science, and Complex Systems at Northwestern University and Director of the Center for Connected Learning and Computer-Based Modeling there. He is the author of the NetLogo language. William Rand is Assistant Professor of Marketing and Computer Science and Director of the Center for Complexity in Business at the Robert H. Smith School of Business at the University of Maryland.
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At nearly 500 full-color pages, Wilensky and Rand’s book does an excellent job progressively walking through the decision to use agent-based modeling, creating simple ABMs, extending preexisting ABMs, creating more complicated ABMs, analyzing ABMs, and conducting verification, validation, and replication.
One of the greatest strengths of Wilensky and Rand’s approach is that IABM (Introduction to Agent-Based Modeling) is that it is a hands-on, exploratory book intended for use with the NetLogo multi-agent modeling environment, which is freely available for download. Each chapter of IABM includes many illustrative examples, all implemented and executed in NetLogo. Moreover, the example models and code are not just available to readers (again, free of charge), but are conveniently bundled in the current release of NetLogo. In other words, rather than just read about the models, the reader is encouraged to run the models his or herself. The Chinese proverb says it best:
Tell me and I’ll forget;
show me and I may remember;
involve me and I’ll understand.
Beyond just running the models described in the book, each chapter concludes with a substantial number of exercises or “Explorations,” usually numbering 20 to 30. Each Exploration is a potentially deep opportunity to learn more about ABM by getting involved rather than just reading, as the Chinese proverb suggests.
Wilensky and Rand do a very nice job of using illustrative models from a variety of disciplines; one example might come from the social sciences and the next example from ecology. This is helpful since each reader may come from a different background or have different experience or interests.
The book requires no special background in mathematics or computer science, which is a huge plus in terms of accessibility to a broader audience.
The authors suggest that it could be used as a textbook for an undergraduate course on complex systems or a computer science course on ABM, or even as a supplement to science, social science, or engineering classes. Graduate students who wish to use ABM in their research – regardless of discipline – would likely find IABM one of the best possible places to start. Even experienced researchers with no agent-based modeling experience would benefit from IABM as an introduction to the method.
While the book is aimed at high-level undergraduates and graduate students, it is sufficient to successfully create very detailed and scientifically valuable agent-based models in NetLogo. The authors reserve a final chapter for “advanced” applications potentially of greater interest to individuals interested in specific sorts of ABM: computationally intensive models, participatory or stakeholder-driven modeling, robotics, spatial and geographic information systems (GIS), and network science / social network analysis. They select just a handful of NetLogo’s more advanced capabilities to describe in this chapter, but include helpful references enabling interested readers to learn more.
I can’t find anything about IABM to criticize, though reading it cover to cover (as I did) is certainly an investment of time, albeit a worthwhile one for the reader wishing to learn and use agent-based modeling.
One of the best ways to explore the science of complexity and how complexity theory can be applied to the numerous real-world phenomena we experience and study is through agent-based modeling. Uri Wilensky and Bill Rand have written an excellent book to help anyone do just that, and I recommend An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo to anyone wishing to get started with agent-based modeling.
Wilensky and Rand make ABM accessible and, importantly, thoroughly enjoyable to learn.