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E-grāmata: Simulation and Similarity: Using Models to Understand the World [Oxford Scholarship Online E-books]

(Department Chair and Professor, Department of Philosophy, University of Pennsylvania)
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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.
List of Figures
xiii
List of Tables
xv
Preface xvii
1 Introduction
1(6)
1.1 Two Aquatic Puzzles
1(3)
1.2 Models of Modeling
4(3)
2 Three Kinds of Models
7(17)
2.1 Concrete Model: The San Francisco Bay-Delta Model
7(3)
2.2 Mathematical Model: Lotka-Volterra Model
10(3)
2.3 Computational Model: Schelling's Segregation Model
13(1)
2.4 Common Features of these Models
14(1)
2.5 Only Three Types of Models?
15(4)
2.6 Fewer than Three Types of Model?
19(5)
3 The Anatomy of Models
24(22)
3.1 Structure
24(7)
3.1.1 Concrete Structures
24(1)
3.1.2 Mathematical Structures
25(4)
3.1.3 Computational Structures
29(2)
3.2 Model Descriptions
31(8)
3.3 Construal
39(3)
3.4 Representational Capacity of Structures
42(4)
4 Fictions and Folk Ontology
46(28)
4.1 Against Maths: Individuation, Causes, and Face-Value Practice
46(3)
4.2 A Simple Fictions Account
49(2)
4.3 Enriching the Simple Account
51(5)
4.3.1 Waltonian Fictionalism
53(2)
4.3.2 Fictions Without Models
55(1)
4.4 Why I Am Not a Fictionalist
56(11)
4.4.1 Variation
56(5)
4.4.2 Representational Capacity of Different Models
61(2)
4.4.3 Making Sense of Modeling
63(1)
4.4.4 Variation in Practice
64(3)
4.5 Folk Ontology
67(3)
4.6 Maths, Interpretation, and Folk Ontology
70(4)
5 Target-Directed Modeling
74(24)
5.1 Model Development
75(4)
5.2 Analysis of the Model
79(11)
5.2.1 Complete Analysis
79(4)
5.2.2 Goal-Directed Analysis
83(7)
5.3 Model-Target Comparison
90(8)
5.3.1 Phenomena and Target Systems
90(3)
5.3.2 Establishing the Fit between Model and Target
93(2)
5.3.3 Representations of Targets
95(3)
6 Idealization
98(16)
6.1 Three Kinds of Idealization
98(7)
6.1.1 Galilean Idealization
99(1)
6.1.2 Minimalist Idealization
100(3)
6.1.3 Multiple-Models Idealization
103(2)
6.2 Representational Ideals and Fidelity Criteria
105(5)
6.2.1 Completeness
105(2)
6.2.2 Simplicity
107(1)
6.2.3 1-Causal
107(2)
6.2.4 Maxout
109(1)
6.2.5 P-General
109(1)
6.3 Idealization and Representational Ideals
110(2)
6.4 Idealization and Target-Directed Modeling
112(2)
7 Modeling Without a Specific Target
114(21)
7.1 Generalized Modeling
114(7)
7.1.1 How-Possibly Explanations
118(1)
7.1.2 Minimal Models and First-Order Causal Structures
119(2)
7.2 Hypothetical Modeling
121(8)
7.2.1 Contingent Nonexistence: xDNA
122(2)
7.2.2 Impossible Targets: Infinite Population Growth and Perpetual Motion
124(5)
7.3 Targetless Modeling
129(2)
7.4 A Moving Target: The Case of Three-sex Biology
131(4)
8 An Account of Similarity
135(21)
8.1 Desiderata for Model-World Relations
135(2)
8.2 Model-Theoretic Accounts
137(5)
8.3 Similarity
142(1)
8.4 Tversky's Contrast Account
143(2)
8.5 Attributes and Mechanisms
145(3)
8.6 Feature Sets, Construals, and Target Systems
148(2)
8.7 Modeling Goals and Weighting Parameters
150(2)
8.8 Weighting Function and Background Theory
152(2)
8.9 Satisfying the Desiderata
154(2)
9 Robustness Analysis and Idealization
156(15)
9.1 Levins and Wimsatt on Robustness
156(2)
9.2 Finding Robust Theorems
158(1)
9.3 Three Kinds of Robustness
159(8)
9.3.1 Parameter Robustness
160(1)
9.3.2 Structural Robustness
161(1)
9.3.3 Representational Robustness
162(5)
9.4 Robustness and Confirmation
167(4)
10 Conclusion: The Practice of Modeling
171(5)
References 176(10)
Index 186
Michael Weisberg is Associate Professor of Philosophy at the University of Pennsylvania.