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:
Contents Preface 1 Introduction 1.1 Two Aquatic Puzzles 1.2 Models of Modeling 2 Three Kinds of Models 2.1 Concrete Model: The San Francisco Bay-Delta Model 2.2 Mathematical Model: Lotka-Volterra Model 2.3 Computational Model: Schelling's Segregation Model 2.4 Common Features of these Models 2.5 Only Three Types of Models? 2.6 Fewer Than Three Types of Model? 3 The Anatomy of Models: Structure & Construal 3.1 Structure 3.1.1 Concrete Structures 3.1.2 Mathematical 3.1.3 Computational 3.2 Model Descriptions 3.3 Construal 3.4 Representational Capacity of Structures 4 Fictions and Folk Ontology 4.1 Against Maths: Individuation, Causes, and Face Value Practice 4.2 A Simple Fictions Account 4.3 Enriching the Simple Account 4.3.1 Waltonian Fictionalism 4.3.2 Fictions without Models 4.4 Why I am not a Fictionalist 4.4.1 Variation 4.4.2 Representational Capacity of Different Models 4.4.3 Making Sense of Modeling 4.4.4 Variation in Practice 4.5 Folk ontology 4.6 Maths, Interpretation, and Folk Ontology 5 Target Directed Modeling 5.1 Model Development 5.2 Analysis of the Model 5.2.1 Complete Analysis 5.2.2 Goal-directed Analysis 5.3 Model/Target Comparison 5.3.1 Phenomena and Target Systems 5.3.2 Establishing the fit between Model and Target 5.3.3 Representations of Targets 6 Idealization 6.1 Three Kinds of Idealization 6.1.1 Galilean idealization 6.1.2 Minimalist idealization 6.1.3 Multiple Models Idealization 6.2 Representational Ideals and Fidelity Criteria 6.2.1 Completeness 6.2.2 Simplicity 6.2.3 1-Causal 6.2.4 Maxout 6.2.5 P-General 6.3 Idealization and Representational Ideals 6.4 Idealization and Target Directed Modeling 7 Modeling Without a Specific Target 7.1 Generalized Modeling 7.1.1 How Possibly Explanations 7.1.2 Minimal Models and First Order Causal Structures 7.2 Hypothetical Modeling 7.2.1 Contingent Non-existence: xDNA 7.2.2 Impossible Targets: Infinite Population Growth and Perpetual Motion 7.3 Targetless Modeling 7.4 A Moving Target: The Case of Three-sex Biology 8 An Account of Similarity 8.1 Desiderata for Model/World Relations 8.2 Model Theoretic Accounts 8.3 Similarity 8.4 Tversky's Contrast Account 8.5 Attributes and Mechanisms 8.6 Feature Sets, Construals, and Target Systems 8.7 Modeling Goals and Weighting Parameters 8.8 Weighting Function and Background Theory 8.9 Satisfying the Desiderata 9 Robustness Analysis and Idealization 9.1 Levins and Wimsatt on Robustness 9.2 Robust Theorems 9.3 Three Kinds of Robustness 9.3.1 Parameter Robustness 9.3.2 Structural Robustness 9.3.3 Representational Robustness 9.4 Robustness and Confirmation 10 Conclusion: The Practice of Modeling References

About the Author :
Michael Weisberg is Department Chair and Professor of Philosophy at the University of Pennsylvania.

Review :
"In my own view, Simulation and Similarity satisfactorily serves such a general purpose, though at a rather formal and abstract level that takes the form of a compilation of advances in meta-theoretical research... For the general reader with a background in the philosophy of science (and I would include here science educators), I see two strong values in the book: (1) it provides a schematic state-of-the-art of semantic stances on what models are and how they can be classified, which is clear and solid, and (2) it contains a careful treatment of (past and present) examples of models and modelling that clarifies theoretical constructs and provides ideas on how to perform good-quality case studies on real instances of scientific modelling, which would be illuminated by those constructs." --Science and Education "[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."--CHOICE