- Tapa dura: 224 páginas
- Editor: OUP USA (14 de febrero de 2013)
- Colección: Oxford Studies in Philosophy of Science
- Idioma: Inglés
- ISBN-10: 0199933669
- ISBN-13: 978-0199933662
- Valoración media de los clientes: Sé el primero en opinar sobre este producto
- Clasificación en los más vendidos de Amazon: nº972.795 en Libros en idiomas extranjeros (Ver el Top 100 en Libros en idiomas extranjeros)
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Simulation and Similarity: Using Models to Understand the World (Oxford Studies in Philosophy of Science) (Inglés) Tapa dura – 14 feb 2013
Descripción del producto
Michael Weisberg has given us a lovely book on models. It has very broad coverage of issues intersecting the nature of models and their use, an extensive consideration of long ignored concrete models with a rich case study, a discussion and classification of the many diverse kinds of models, and a particularly groundbreaking and innovative discussion of similarity concerning how models relate to the world ... his analysis is both clear and rich. (William C. Wimsatt, Biology and Philosophy)
[This book] is lively, well-written, and should be accessible to novice audiences as well as informative and provocative to disciplinary insiders. It skillfully makes use of a relatively small set of carefully explained and not-overly-complicated examples to give an account that succeeds in being sophisticated and attentive to the details of scientific practice without getting overly mired in the details of 'case studies' that sometimes plague the literature on scientific modeling. (Eric Winsberg, Notre Dame Philosophical Reviews)
[Simulation and Similarity] is well written and detailed in its exposition, providing concrete examples to ground the discussion. It is a very interesting complement to standard mathematical modeling treatments for scientists, engineers, and mathematicians. (R. A. Kolvoord, CHOICE)
The book provides a useful and intuitive classification of kinds of models, kinds of modelling methodologies, and model components that is sensitive to the diversity of scientific practice. (Michael Weisberg, The British Journal for the Philosophy of Science)
...a compelling account of models and can be highly recommended to philosophers of science as well as to scientists of any particular discipline, especially those practicing modeling and simulation in their everydays work. (V. S. Pronskikh, Metascience)
Reseña del editor
In the 1950s, John Reber convinced many Californians that the best way to solve the state's water shortage problem was to dam up the San Francisco Bay. Against massive political pressure, Reber's opponents persuaded lawmakers that doing so would lead to disaster. They did this not by empirical measurement alone, but also through the construction of a model. Simulation and Similarity explains why this was a good strategy while simultaneously providing an account of modeling and idealization in modern scientific practice. Michael Weisberg focuses on concrete, mathematical, and computational models in his consideration of the nature of models, the practice of modeling, and nature of the relationship between models and real-world phenomena.
In addition to a careful analysis of physical, computational, and mathematical models, Simulation and Similarity offers a novel account of the model/world relationship. Breaking with the dominant tradition, which favors the analysis of this relation through logical notions such as isomorphism, Weisberg instead presents a similarity-based account called weighted feature matching. This account is developed with an eye to understanding how modeling is actually practiced. Consequently, it takes into account the ways in which scientists' theoretical goals shape both the applications and the analyses of their models.
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Weisberg first defends his categorization of three kinds of models (concrete, mathematical, and computational models). He then makes clear that a model is composed of two things (its structure - the thing we normally equate with the model - and its construal). The construal defines 1) the relation between the model and the target system, 2) the intended scope of the model, and 3) its fidelity criteria, i.e., ways to quantify the question of whether the model is good enough to be applied. He then discusses a specific view of mathematical models, called fictions, and dismisses it. He quickly sketches the process of modelling against a given target system, and the three kinds of idealizations we often use in this process (Galilean idealization, minimalist idealization, and multiple models idealization). He also discusses the case, where a model is targetless and shows that it might still be beneficial even if it does not explain any phenomenon of a real-world system. Finally, he introduces a way to measure the goodness of fit of a model, and a way to analyze a model's robustness before he concludes the book with a 3 page summary.
Reading the book was very relieving, as my intuition that we are sometimes too fast to apply models to situations where they are not appropriate, can be finally put in scientific terms, namely: in too many cases, the construal is not made explicit and then it can happen that a model is applied outside of its intended scope.
I am currently putting bits and pieces of this book in all of my lectures, from "graph theory" to the "fundamentals of programming" to "Complex network analysis", "Formal models of complex systems", and others, because I think that we are sometimes speaking to much about "computers" and too little about "science".
I recommend to start the book with the three page summary in the "Conclusion". Since Weisberg avoids using more specific jargon than absolutely necessary, it is well understandable and gives a first conclusive picture of the book. In his introductory pages, he starts with three examples of the three different model types and then developes the story based on these examples bit by bit.