Simulation and Similarity: Using Models to Understand the World(Oxford Studies in Philosophy of Science)

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.

Table of Contents:
1 Introduction1.1 Two Aquatic Puzzles1.2 Models of Modeling2 Three Kinds of Models2.1 Concrete Model: The San Francisco Bay-Delta Model2.2 Mathematical Model: Lotka-Volterra Model2.3 Computational Model: Schelling's Segregation Model2.4 Common Features of these Models2.5 Only Three Types of Models?2.6 Fewer Than Three Types of Model?3 The Anatomy of Models: Structure & Construal3.1 Structure3.1.1 Concrete Structures3.1.2 Mathematical3.1.3 Computational3.2 Model Descriptions3.3 Construal3.4 Representational Capacity of Structures4 Fictions and Folk Ontology4.1 Against Maths: Individuation, Causes, and Face Value Practice4.2 A Simple Fictions Account4.3 Enriching the Simple Account4.3.1 Waltonian Fictionalism4.3.2 Fictions without Models4.4 Why I am not a Fictionalist4.4.1 Variation4.4.2 Representational Capacity of Different Models4.4.3 Making Sense of Modeling4.4.4 Variation in Practice4.5 Folk ontology4.6 Maths, Interpretation, and Folk Ontology5 Target Directed Modeling5.1 Model Development5.2 Analysis of the Model5.2.1 Complete Analysis5.2.2 Goal-directed Analysis5.3 Model/Target Comparison5.3.1 Phenomena and Target Systems5.3.2 Establishing the fit between Model and Target5.3.3 Representations of Targets6 Idealization6.1 Three Kinds of Idealization6.1.1 Galilean idealization6.1.2 Minimalist idealization6.1.3 Multiple Models Idealization6.2 Representational Ideals and Fidelity Criteria6.2.1 Completeness6.2.2 Simplicity6.2.3 1-Causal6.2.4 Maxout6.2.5 P-General6.3 Idealization and Representational Ideals6.4 Idealization and Target Directed Modeling7 Modeling Without a Specific Target7.1 Generalized Modeling7.1.1 How Possibly Explanations7.1.2 Minimal Models and First Order Causal Structures7.2 Hypothetical Modeling7.2.1 Contingent Non-existence: xDNA7.2.2 Impossible Targets: Infinite Population Growth and Perpetual Motion7.3 Targetless Modeling7.4 A Moving Target: The Case of Three-sex Biology8 An Account of Similarity8.1 Desiderata for Model/World Relations8.2 Model Theoretic Accounts8.3 Similarity8.4 Tversky's Contrast Account8.5 Attributes and Mechanisms8.6 Feature Sets, Construals, and Target Systems8.7 Modeling Goals and Weighting Parameters8.8 Weighting Function and Background Theory8.9 Satisfying the Desiderata9 Robustness Analysis and Idealization9.1 Levins and Wimsatt on Robustness9.2 Finding Robust Theorems9.3 Three Kinds of Robustness9.3.1 Parameter Robustness9.3.2 Structural Robustness9.3.3 Representational Robustness9.4 Robustness and Confirmation10 Conclusion: The Practice of ModelingReferences

About the Author :
Michael Weisberg is Associate Professor of Philosophy at the University of Pennsylvania.

Review :
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. [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. [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. 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. ...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.