An Introduction to the Philosophy of Science

Description

This book guides readers by gradual steps through the central concepts and debates in the philosophy of science. Using concrete examples from the history of science, Kent W. Staley shows how seemingly abstract philosophical issues are relevant to important aspects of scientific practice. Structured in two parts, the book first tackles the central concepts of the philosophy of science, such as the problem of induction, falsificationism, and underdetermination, and important figures and movements, such as the logical empiricists, Thomas Kuhn, and Paul Feyerabend. The second part turns to contemporary debates in the philosophy of science, such as scientific realism, explanation, the role of values in science, the different views of scientific inference, and probability. This broad yet detailed overview will give readers a strong grounding whilst also providing opportunities for further exploration. It will be of particular interest to students of philosophy, the philosophy of science, and science. Cover Half-title Title page Copyright information Dedication Table of contents List of figures List of tables Preface: philosophy of science for philosophers, scientists, and everyone else Acknowledgments List of abbreviations Part I Background and Basic Concepts 1 Some problems of induction 1.1 Introduction 1.2 The problem of induction about doughnuts 1.3 Reasoning about light: Isaac Newton’s induction 1.3.1 Newton’s problems of induction 1.4 Conclusion 2 Falsificationism: science without induction? 2.1 Growth through falsification 2.1.1 The logical structure of generalizations 2.1.2 Falsification revisited: a modus tollens argument 2.1.3 Newton’s experiment as falsification 2.1.4 Corroboration 2.2 Falsificationism, demarcation, and conventionalism 2.2.1 Demarcation and the aim of science 2.2.2 Rules as conventions 2.3 The empirical basis 2.3.1 Avoidable falsifications 2.3.2 Deciding to accept a falsifying statement 2.4 Conclusion 3 Underdetermination 3.1 Introduction 3.2 Duhem’s thesis 3.2.1 A test of Newton’s emission theory of light 3.2.2 Duhem on physics 3.2.3 Duhem’s problem and “good sense” 3.3 Underdetermination revisited: Quine 3.4 A big deal? 3.4.1 An epistemology of experiment 3.5 Conclusion 4 Logical empiricism and scientific theories 4.1 Introduction 4.2 The Vienna Circle and logical empiricism 4.2.1 The Vienna Circle and philosophy of science 4.2.2 Meaning vs. metaphysics 4.3 From sentences to models 4.3.1 Models as idealized structures 4.4 Conclusion 4.5 Appendix: models as structures Sentences, formulas, satisfaction Structures Models 5 Kuhn: scientific revolutions as paradigm changes 5.1 Introduction 5.2 Paradigms and normal science 5.3 Anomaly and crisis 5.4 Revolution 5.5 Incommensurability 5.6 Re-articulation 5.6.1 An example: molecular phylogenetics 5.7 Progress through revolutions? 5.8 Problems with paradigms 5.9 Conclusion 6 Lakatos: scientific research programs 6.1 Introduction 6.2 Falsification and the underdetermination problem 6.3 The methodology of scientific research programs 6.3.1 A tale of two planets 6.4 Criteria for progress 6.4.1 A tale of another planet? 6.4.2 Progress 6.4.3 Novelty 6.5 Conclusion: no “instant rationality” 7 Feyerabend: epistemological anarchism 7.1 Introduction 7.2 Epistemological anarchism 7.3 The argument from progress 7.3.1 Limitations of rules and logic 7.3.2 Proliferation of theories Incompatibility with well-established theory Incompatibility with established facts Galileo on the motion of the earth 7.4 The argument from human flourishing 7.4.1 Feyerabend and Mill 7.5 Conclusion Part II Ongoing Investigations 8 Reasoning with probability: Bayesianism 8.1 Introduction 8.2 Probability theory 8.2.1 Models: representing the problem 8.2.2 Axioms of probability theory 8.3 Probability ideas and science 8.4 Bayesian probability 8.4.1 Prizes 8.4.2 Wagering Example: Samiksha and Tatyana 8.4.3 Rationality 8.5 Bayes’s theorem and updating 8.5.1 Example: fossil origins 8.6 Problems for Bayesianism 8.6.1 Subjectivity and the catch-all hypothesis 8.6.2 Relevance and normativity 8.7 Bayesian philosophy and Bayesian statistics 8.8 Conclusion 8.9 Appendix: solution to the Chevalier’s problem 9 Reasoning with probability: frequentism 9.1 Introduction 9.2 Relative frequency ‘in the long run’ 9.3 Two traditions in frequentist statistics 9.3.1 Fisherian significance testing Example: the Higgs boson? 9.3.2 Neyman–Pearson testing Model assumptions 9.3.3 Error probabilities Size, or significance level Power 9.3.4 Statistical tests: what are they good for? A behavioristic interpretation A measure-of-evidence interpretation 9.4 Error-statistical philosophy of science and severe testing 9.4.1 Severity and evidence 9.4.2 Example: evidence that formaldehyde is carcinogenic Metastatistical severity analysis of the fates of rats and mice 9.5 Conclusion 10 Realism and anti-realism 10.1 Introduction: success, change, and empiricism 10.1.1 Example: the hole theory and anti-matter 10.2 ‘No miracles’: an argument for realism 10.2.1 Circularity? 10.3 The problem of theory change 10.3.1 The pessimistic meta-induction 10.4 The empiricist challenge 10.4.1 Constructive empiricism 10.4.2 What is observable? 10.4.3 Underdetermination 10.5 Strengthening scientific realism 10.5.1 Maturity and success 10.5.2 Divide and conquer 10.6 Experimental routes to realism 10.6.1 Entity realism Independence from theory? 10.6.2 A direct experimental argument for realism? 10.6.3 Experiment and realism about positrons 10.7 Structural routes to realism 10.7.1 “The best of both worlds”: epistemic structural realism Dirac’s hole theory, one more time 10.7.2 Ontic structural realism 10.8 Conclusion 11 Explanation 11.1 Introduction 11.2 The Covering Law model 11.2.1 An example: the Hodgkin and Huxley model of the action potential 11.2.2 Objections to the CL model Explanations without laws Nomic expectability Asymmetry Irrelevance 11.2.3 The Hodgkin–Huxley model as CL explanation? 11.2.4 Another example: curare poisoning 11.3 Achinstein’s pragmatic theory 11.3.1 Illocutionary acts 11.3.2 The ordered pair view 11.3.3 Evaluating explanations 11.4 Unificationist theories 11.4.1 Kitcher’s theory 11.4.2 An objection to the unificationist account 11.5 Causal explanation 11.5.1 Salmon’s causal mechanical account 11.5.2 Woodward’s manipulationist account Circularity? 11.5.3 Mechanistic explanation 11.6 Conclusion 12 Values in science 12.1 Introduction 12.2 Decisions about research directions and methods 12.3 The argument from inductive risk 12.3.1 Example: formaldehyde again 12.3.2 Skeptical responses to the argument from inductive risk 12.3.3 Direct and indirect roles for values 12.4 Values and the objectivity of science 12.4.1 Merton on the ethos of science 12.4.2 Longino on contextual values 12.5 Feminist critiques 12.5.1 Longino’s pluralism in contrast with value-neutrality and standpoint theory 12.6 Conclusion: underdetermination and honesty References Index