Leveraging distortions: explanation, idealization, and universality in science
"An original argument about how scientific models often times distort reality rather than accurately reflect it. And it's this distortion that often gives scientific models their epistemic power"--
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Massachusetts ; London, England
The MIT Press
[2021]
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | "An original argument about how scientific models often times distort reality rather than accurately reflect it. And it's this distortion that often gives scientific models their epistemic power"-- |
| Beschreibung: | xii, 353 Seiten Illustrationen (schwarz-weiß) |
| ISBN: | 9780262542616 0262542617 |
Internformat
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Datensatz im Suchindex
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Contents Acknowledgments 1 Introduction xi l 1.1 Pervasive Distortion Is Central to Science 1.2 Responding to Pervasive Distortion 12 1.3 Plan of the Book 15 2 8 Causal Explanation, Accurate Representation, and the Decompositional Strategy 21 2.1 The Role of Causation in Explanation 21 2.2 Accurate Representation of Relevant Features 29 2.3 Decomposition of Scientific Representations into Accurate and Inaccurate Parts 34 2.4 Motivation for an Alternative Approach 37 3 Many Explanations Are Noncausal 3.1 Optimality Explanations 41 42 3.1.1 The Core Components of an Optimality Explanation 42 3.1.2 A Frequency-Independent Example: Parker's Dung Flies 45 3.1.3 Explaining General Patterns: Equilibrium Sex Ratios 47 3.1.4 Equilibrium Explanation 48 3.1.5 Essential Idealizations in Optimality Explanations 51 3.1.6 Synchronic Mathematical Representation ofNoncausal Features 3.2 Statistical Explanations 60 3.2.1 Galton's Autonomous Statistical Explanations 60 3.2.2 Natural Selection and Autonomous Statistical Explanations 3.2.3 Distinguishing Two Kinds of Natural Selection Explanations 3.2.4 Essential Idealizations in Statistical Explanations 69 3.2.5 Statistical Dependence and the Role ofIrrelevance 71 63 67 55
viii Contents 3.3 Minimal Model Explanations 73 3.3.1 Essential Idealizations in Minimal Model Explanations 81 3.3.2 The Role oflnelevance Information in Minimal Model Explanations 3.4 A Way Forward 86 4 A Counterfactual Account of Explanation 89 4.1 Three Requirements for an Account of Explanation 90 4.2 A Counterfactual Account of Explanation 93 4.3 Unifying Causal and Noncausal Explanations 111 4.4 Connecting Explanation and Understanding 117 4.5 Accounting for the Positive and Ineliminable Contributions of Idealizations 120 4.6 Comparison with Other Counterfactual Accounts of Explanation 4.6.1 The Relevance ofInelevance 124 4.6.2 Extracting Modal Information from Idealized Models 128 5 82 124 Models Don't Decompose That Way: The Holistic Distortion View of Idealized Models 133 5.1 Against the Decompositional Strategy 135 5.1.1 Many Scientific Models Don't Decompose That Way 135 5.1.2 Many Models Distort Difference-Making Features 140 5.2 An Alternative Approach: The Holistic Distortion View 145 5.3 Conclusion 150 6 Using Universality to Justify the Use of Holistic Distortions to Explain 153 6.1 Appealing to Universality Classes to Justify the Use of Idealized Models to Explain 154 6.2 Parameters of Universality 162 6.3 Explicit Appeals to Universality to Justify the Use of Idealized Models to Explain 164 6.3.1 Modeling Melting Ice Ponds 165 6.3.2 Modeling Bacterial Growth 169 6.3.3 Gaussian Universality 173 6.3.4 The Universality of the Tracy-Widom Distribution 176 6.4 Universality and Other Concepts of Stability 180 6.5 Conclusion 184 7 Multiscale Modeling and
Universality 187 7.1 The Problem of Inconsistent Models 190 7.2 Universality and the Problem of Inconsistent Models 197
Contents ¡X 7.3 Multiscale Modeling and the Tyranny of Scales 203 7.4 Tackling the Tyranny of Scales 206 7.4.1 Scale Dependence, Scale Separation, and Universality 207 7.4.2 Scale Invariance, Power Laws, and Universality 211 7.4.3 Modeling across Scales: Renormalization, Homogenization, and Universality 217 7.5 Philosophical Lessons: Practical Constraints, Pluralism, and Relative Autonomy 224 8 8.1 Understanding without Explanation 234 8.1.1 Investigating Necessity Claims 235 8.1.2 Modeling Hypothetical Scenarios 237 8.1.3 Exploring Possibility Space 240 8.1.4 Summing Up 243 8.2 An Account of Understanding 247 8.3 Understanding Realism 259 8.4 Understanding and Scientific Progress 264 8.5 The Role of Diversity in Promoting Understanding 8.6 Conclusion 273 9 233 Understanding, Realism, and the Progress of Science 269 Leveraging Holistic Distortions: The Positive Contributions of Idealizations to Expianation and Understanding 275 9.1 Some Other Questions about Explanation 275 9.2 Holistic Distortion Is Necessary for the Modeling Techniques Used to Explain 283 9.3 Holistic Distortions Often Provide Better Explanations 286 9.4 Holistic Distortions Improve Understanding 288 9.5 Intertwined Idealizations and Holistic Distortion 291 9.6 Universal Patterns and the Epistemic Goals of Science 293 9.7 Realism about Epistemic Products 296 9.8 Conclusion 297 Notes 301 References Index 337 315 |
| adam_txt |
Contents Acknowledgments 1 Introduction xi l 1.1 Pervasive Distortion Is Central to Science 1.2 Responding to Pervasive Distortion 12 1.3 Plan of the Book 15 2 8 Causal Explanation, Accurate Representation, and the Decompositional Strategy 21 2.1 The Role of Causation in Explanation 21 2.2 Accurate Representation of Relevant Features 29 2.3 Decomposition of Scientific Representations into Accurate and Inaccurate Parts 34 2.4 Motivation for an Alternative Approach 37 3 Many Explanations Are Noncausal 3.1 Optimality Explanations 41 42 3.1.1 The Core Components of an Optimality Explanation 42 3.1.2 A Frequency-Independent Example: Parker's Dung Flies 45 3.1.3 Explaining General Patterns: Equilibrium Sex Ratios 47 3.1.4 Equilibrium Explanation 48 3.1.5 Essential Idealizations in Optimality Explanations 51 3.1.6 Synchronic Mathematical Representation ofNoncausal Features 3.2 Statistical Explanations 60 3.2.1 Galton's Autonomous Statistical Explanations 60 3.2.2 Natural Selection and Autonomous Statistical Explanations 3.2.3 Distinguishing Two Kinds of Natural Selection Explanations 3.2.4 Essential Idealizations in Statistical Explanations 69 3.2.5 Statistical Dependence and the Role ofIrrelevance 71 63 67 55
viii Contents 3.3 Minimal Model Explanations 73 3.3.1 Essential Idealizations in Minimal Model Explanations 81 3.3.2 The Role oflnelevance Information in Minimal Model Explanations 3.4 A Way Forward 86 4 A Counterfactual Account of Explanation 89 4.1 Three Requirements for an Account of Explanation 90 4.2 A Counterfactual Account of Explanation 93 4.3 Unifying Causal and Noncausal Explanations 111 4.4 Connecting Explanation and Understanding 117 4.5 Accounting for the Positive and Ineliminable Contributions of Idealizations 120 4.6 Comparison with Other Counterfactual Accounts of Explanation 4.6.1 The Relevance ofInelevance 124 4.6.2 Extracting Modal Information from Idealized Models 128 5 82 124 Models Don't Decompose That Way: The Holistic Distortion View of Idealized Models 133 5.1 Against the Decompositional Strategy 135 5.1.1 Many Scientific Models Don't Decompose That Way 135 5.1.2 Many Models Distort Difference-Making Features 140 5.2 An Alternative Approach: The Holistic Distortion View 145 5.3 Conclusion 150 6 Using Universality to Justify the Use of Holistic Distortions to Explain 153 6.1 Appealing to Universality Classes to Justify the Use of Idealized Models to Explain 154 6.2 Parameters of Universality 162 6.3 Explicit Appeals to Universality to Justify the Use of Idealized Models to Explain 164 6.3.1 Modeling Melting Ice Ponds 165 6.3.2 Modeling Bacterial Growth 169 6.3.3 Gaussian Universality 173 6.3.4 The Universality of the Tracy-Widom Distribution 176 6.4 Universality and Other Concepts of Stability 180 6.5 Conclusion 184 7 Multiscale Modeling and
Universality 187 7.1 The Problem of Inconsistent Models 190 7.2 Universality and the Problem of Inconsistent Models 197
Contents ¡X 7.3 Multiscale Modeling and the Tyranny of Scales 203 7.4 Tackling the Tyranny of Scales 206 7.4.1 Scale Dependence, Scale Separation, and Universality 207 7.4.2 Scale Invariance, Power Laws, and Universality 211 7.4.3 Modeling across Scales: Renormalization, Homogenization, and Universality 217 7.5 Philosophical Lessons: Practical Constraints, Pluralism, and Relative Autonomy 224 8 8.1 Understanding without Explanation 234 8.1.1 Investigating Necessity Claims 235 8.1.2 Modeling Hypothetical Scenarios 237 8.1.3 Exploring Possibility Space 240 8.1.4 Summing Up 243 8.2 An Account of Understanding 247 8.3 Understanding Realism 259 8.4 Understanding and Scientific Progress 264 8.5 The Role of Diversity in Promoting Understanding 8.6 Conclusion 273 9 233 Understanding, Realism, and the Progress of Science 269 Leveraging Holistic Distortions: The Positive Contributions of Idealizations to Expianation and Understanding 275 9.1 Some Other Questions about Explanation 275 9.2 Holistic Distortion Is Necessary for the Modeling Techniques Used to Explain 283 9.3 Holistic Distortions Often Provide Better Explanations 286 9.4 Holistic Distortions Improve Understanding 288 9.5 Intertwined Idealizations and Holistic Distortion 291 9.6 Universal Patterns and the Epistemic Goals of Science 293 9.7 Realism about Epistemic Products 296 9.8 Conclusion 297 Notes 301 References Index 337 315 |
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| spelling | Rice, Collin ca. 20. Jh. Verfasser (DE-588)1245003852 aut Leveraging distortions explanation, idealization, and universality in science Collin Rice Cambridge, Massachusetts ; London, England The MIT Press [2021] xii, 353 Seiten Illustrationen (schwarz-weiß) txt rdacontent n rdamedia nc rdacarrier "An original argument about how scientific models often times distort reality rather than accurately reflect it. And it's this distortion that often gives scientific models their epistemic power"-- Kausalität (DE-588)4030102-3 gnd rswk-swf Grund (DE-588)4022317-6 gnd rswk-swf Wissenschaftsphilosophie (DE-588)4202787-1 gnd rswk-swf Causation Science / Philosophy Grund (DE-588)4022317-6 s Kausalität (DE-588)4030102-3 s Wissenschaftsphilosophie (DE-588)4202787-1 s DE-604 Erscheint auch als Online-Ausgabe 978-0-262-36582-6 Digitalisierung BSB München - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032886149&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Rice, Collin ca. 20. Jh Leveraging distortions explanation, idealization, and universality in science Kausalität (DE-588)4030102-3 gnd Grund (DE-588)4022317-6 gnd Wissenschaftsphilosophie (DE-588)4202787-1 gnd |
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| title | Leveraging distortions explanation, idealization, and universality in science |
| title_auth | Leveraging distortions explanation, idealization, and universality in science |
| title_exact_search | Leveraging distortions explanation, idealization, and universality in science |
| title_exact_search_txtP | Leveraging distortions explanation, idealization, and universality in science |
| title_full | Leveraging distortions explanation, idealization, and universality in science Collin Rice |
| title_fullStr | Leveraging distortions explanation, idealization, and universality in science Collin Rice |
| title_full_unstemmed | Leveraging distortions explanation, idealization, and universality in science Collin Rice |
| title_short | Leveraging distortions |
| title_sort | leveraging distortions explanation idealization and universality in science |
| title_sub | explanation, idealization, and universality in science |
| topic | Kausalität (DE-588)4030102-3 gnd Grund (DE-588)4022317-6 gnd Wissenschaftsphilosophie (DE-588)4202787-1 gnd |
| topic_facet | Kausalität Grund Wissenschaftsphilosophie |
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