Оглавление
Franz-Peter Griesmaier. This is Philosophy of Science
THIS IS PHILOSOPHY
THIS IS PHILOSOPHY OF SCIENCE. AN INTRODUCTION
Contents
List of Figures
Guide
Pages
PREFACE
ACKNOWLEDGMENTS
ABOUT THE COMPANION WEBSITE
1 PILLARS OF SCIENCE: REASONS, KNOWLEDGE, AND TRUTH
1.1 Epistemic Reasons
1.1.1 Conclusive Reasons
1.1.2 Defeasible Reasons
1.2 Reasoning from Evidence
1.2.1 Statistical Inference (SI)
1.2.2 Inductive Generalization (IG)
1.2.3 Inference to the Best Explanation (IBE)
1.3 Knowledge and Truth
1.4 Facts, Hypotheses, and Theories
1.4.1 “It’s True for You but Not for Me”
1.4.2 Perspectivism
1.5 Conclusion
Notes
Annotated Bibliography
2 EVIDENCE, OBSERVATION, AND MEASUREMENT. 2.1 The Promises of Evidence
2.2 Basic Evidence and Derived Evidence. 2.2.1 What We See
2.2.2 Causes and Evidence
2.2.3 Observation, Naked and Enhanced
2.3 Measurement
2.3.1 Measurement Scales
2.3.2 Operationalism
2.3.3 Theory-Ladenness of Measurement
2.4 Conclusion
Note
Annotated Bibliography
3 USES OF EVIDENCE. 3.1 From Observation to Hypothesis
3.2 Theory Appraisal
3.2.1 Confirmation through Predictive Success
3.2.2 Falsification to the Rescue
3.2.3 Ravens and White Chalk
3.2.4 On Flat Earth and Bending Light
3.3 The Demarcation Problem
3.3.1 Progressive Modifications
3.3.2 Basic Statements
3.3.3 Moving and Burning
3.3.4 Lucky Modifications
3.4 Conclusion
Notes
Annotated Bibliography
4 EVIDENCE, RATIONALITY, AND DISAGREEMENT. 4.1 From Weak to Strong Evidence
4.1.1 Anecdotes
4.1.2 Observational Studies
4.1.3 Natural History
4.1.4 Case Studies
4.2 Evidence and Rationality
4.3 Explaining Scientific Disagreement
4.3.1 Differences in Evidential Basis
4.3.2 Theory-Ladenness of Observation
4.3.3 Differences in Prior Probability Assignments
4.4 Conclusion
Note
Annotated Bibliography
5 THE NATURE OF PROBABILITY. 5.1 Basics of Probability
5.2 Interpretations of Probability
5.3 Probabilities as Credences
5.3.1 Probabilistic Consistency
5.3.2 Conditionalization
5.3.3 The Problem of Priors
5.3.4 The Problem of Old Evidence
5.4 Epistemic Probabilities
5.4.1 The Classical Interpretation
5.4.2 Bertrand’s Paradox
5.5 Probabilities as Objective Chances
5.5.1 Frequentism
5.5.2 Propensities
5.6 Probabilities and Defeasible Reasoning
5.7 Fallacies
5.8 Conclusion
Annotated Bibliography
6 DO NOT BE MISLED: CONFOUNDS AND CONTROLS. 6.1 Trials and Errors
6.2 Treatment and Control
6.2.1 Counterfactuals
6.2.2 Possible Worlds
6.2.3 Counterfactuals and Controls
6.3 Randomization
6.3.1 Bias
6.3.2 Unnoticed but Relevant Differences
6.3.3 Ethical Concerns
6.4 Conclusion
Annotated Bibliography
7 PHYSICAL EXPERIMENTS AND THEIR DESIGN. 7.1 Historical Remarks
7.2 Setting Experimental Parameters
7.3 Dependent and Independent Variables
7.3.1 Stratifying to Isolate Relevant Factors
7.3.2 Determining Relevance
7.4 Learning from Experiment
7.4.1 Replication: How to Be Confident
7.4.2 Misleading Evidence: Cleaning up the Data
7.4.3 Data Reduction and Curve Fitting: Promises and Pitfalls
7.5 Types of Errors: Pick Your Poison
7.6 Relationships between Experiment and Theory
7.6.1 Crucial Experiments
7.6.2 Are Experiments Theory-Neutral?
7.7 Conclusion
Note
Annotated Bibliography
8 EXPERIMENTAL METHODS THAT THEY DON’T TEACH
8.1 Found and Natural Experiments
8.1.1 Found Experiments and Unplanned Treatments
8.1.2 The Role of Background Theory
8.1.3 Natural Experiments
8.2 Thought Experiments
8.2.1 Reasoning through Scenarios
8.2.2 Galileo’s Combined Weights and Ideal Spheres
8.2.3 Newton on Space (and Time)
8.2.4 Spinning Globes (TE-3)
8.2.5 Maxwell’s Demon (TE-4)
8.3 The Structure and Evidential Value of Thought Experiments. 8.3.1 Kinds of TEs
8.3.2 TEs as Imaginative Reasoning
8.3.3 TEs as Mental Evidence
8.4 Learning from TEs
8.4.1 Distant Worlds
8.4.2 TEs as Tools for Discovery
8.5 The Ubiquity of Thought Experiments
8.6 Are Computer Simulations Thought Experiments?
8.7 Conclusion
Notes
Annotated Bibliography
9 MODELS: USEFUL LIES AND INFORMATIVE FICTIONS
9.1 The Nature of Models
9.1.1 Models as Partial Isomorphisms
9.1.2 Mirror Models vs. Conjecture Models
9.2 Modelling Techniques
9.2.1 Approximation
9.2.2 Abstraction (Aristotelian Idealization)
9.2.3 Distortion (Galilean Idealization)
9.3 Analogies
9.3.1 Conceptual Shifts
9.3.2 Property Sharing
9.4 Learning from Models
9.4.1 Learning from Scale Models
9.4.2 Learning from Approximation Models
9.4.3 Learning from Fiction (i.e., Distortion Models)
9.5 Conclusion
Notes
Annotated Bibliography
10 CAUSATION AND CAUSAL INFERENCE. 10.1 What’s the Problem with Causation?
10.2 Hume’s Challenge
10.3 Causation as Mere Regularities
10.4 Conserved Quantities to the Rescue?
10.4.1 Can CQT Deliver on All Fronts?
10.5 Causation and Manipulation
10.5.1 Manipulation and Counterfactuals
10.5.1.1 The New Mechanism
10.6 Conclusion
Note
Annotated Bibliography
11 STRANGE CAUSATION – TIME TRAVEL AND REMOTE ACTION
11.1 On Influencing the Past
11.1.1 Basics of the Special Theory of Relativity (STR)
11.1.2 When One Twin Is MUCH Older than the Other
11.1.3 Basics of the General Theory of Relativity
11.1.4 The Grandfather Paradox
11.2 Quantum Mechanics and Locality
11.2.1 The Measurement Problem
11.2.2 The Einstein-Podolsky-Rosen Paradox (EPR)
11.2.3 Something Has to Give
11.2.4 Other Options
11.3 Conclusion
Notes
Annotated Bibliography
12 BUT IS ANY OF IT REAL? 12.1 Theories and Truth
12.2 A Map of the Views
12.3 Are Groups Real?
12.3.1 The Biological Species Concept
12.3.2 The Phylogenetic Species Concept
12.3.3 From Biological Species to All of Nature
12.4 Laws of Nature
12.4.1 Laws and Counterfactuals
12.4.2 Replacing Laws by Regularities
12.5 Is Everything Real Observable? 12.5.1 Observables vs. Unobservables
12.5.2 How to Name Unobservables
12.5.3 Describing Unobservables
12.5.4 Maybe It’s All Fictional?
12.6 Realism vs. Antirealism
12.6.1 Is Predictive Success Mere Coincidence?
12.6.2 Successful but False Theories – the Pessimistic Induction
12.6.3 Explaining Success
12.6.4 The Argument from Underdetermination
12.7 Structural Realism
12.8 Realism and Explanation
12.9 Conclusion
Note
Annotated bibliography
13 EXPLANATION AND UNDERSTANDING
13.1 The Deductive-Nomological (DN) Model
13.1.1 The Flagpole and its Shadow
13.2 The Causal Model
13.3 The Unificationist Model
13.4 The Pragmatic Model
13.5 What about Realism?
13.6 Conclusion
Annotated Bibliography
14 FUNDAMENTAL THEORIES AND THE ORGANIZATION OF SCIENCE
14.1 The Layer Cake Model
14.2 Classical Reductionism
14.2.1 Challenges for Neuroscience: Thought
14.2.2 Challenges for Neuroscience: Pain
14.3 Functional Concepts
14.3.1 The Problem with Disjunctive Laws
14.4 The Functional Model
14.5 Emergence
14.5.1 The Ontological Condition
14.5.2 The Epistemic Condition
14.5.3 The Mystery Condition
14.6 Interdisciplinary Research
14.7 Conclusion
Note
Annotated Bibliography
15 SCIENTIFIC PROGRESS
15.1 Science and Technology
15.2 Goals of Science
15.3 Reduction in the Limit
15.4 How Theories Are Born
15.4.1 Theory Modification
15.4.2 Theory Replacement
15.5 What Kind of Progress?
15.5.1 Scientific Revolutions
15.5.2 Kuhn’s Cyclical Model
15.5.3 The Rationality of Revolutions
15.6 From Theories to Research Programmes
15.7 Methodological Anarchism
15.8 Incommensurability
15.8.1 New Worlds?
15.9 Structural Realism and Progress
15.10 Conclusion
Annotated Bibliography
Index
WILEY END USER LICENSE AGREEMENT
