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Philosophy of Science — Key Points
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1. Central questions
- What distinguishes science from non-science? (Demarcation problem)
- How do scientific theories explain and predict phenomena?
- What is the nature of scientific reasoning (induction, deduction, abduction)?
- How do observation and theory interact (theory-ladenness of observation)?
- What is the status of scientific knowledge (realism vs. anti-realism)?
2. Major positions
- Scientific Realism: Best scientific theories approximately truthfully describe unobservable entities (Putnam, Boyd).
- Instrumentalism/Anti-Realism: Theories are tools for prediction, not necessarily true descriptions (van Fraassen).
- Constructivism and Social Epistemology: Scientific knowledge is shaped by social processes, values, and institutions (Kuhn, Latour).
- Structuralism and Model-Based Views: Science advances via models and structures rather than literal true statements (Suppe, Cartwright).
3. Methodology and reasoning
- Induction: Generalizing from observations—problem of justifying induction (Hume).
- Falsificationism: Popper’s idea that theories are scientific if falsifiable; emphasizes bold conjectures and refutations.
- Bayesianism: Probabilistic updating of belief based on evidence.
- Lakatos’ Research Programmes: Science progresses through competing research programmes with heuristics and protective belts.
4. Explanation and laws
- Covering-law model: Explanations subsume phenomena under general laws (Hempel).
- Causal/mechanistic accounts: Explanations cite causes or mechanisms producing phenomena (Salmon, Craver).
- Pragmatic and pluralist views: Multiple types of explanation depending on context.
5. Values, objectivity, and ethics
- Science aims for objectivity, but choices about methods, significance, and application involve epistemic and non-epistemic values.
- Responsible research requires transparency, reproducibility, and ethical reflection.
6. Contemporary issues
- Replication crisis and reliability of findings.
- Role of models, simulations, and big data.
- Science policy, public trust, and science communication.
Further reading (concise)
- Peter Godfrey-Smith, Theory and Reality (2003)
- Karl Popper, The Logic of Scientific Discovery (1959)
- Thomas Kuhn, The Structure of Scientific Revolutions (1962)
- Bas van Fraassen, The Scientific Image (1980)
If you want, I can explain any one of these points in more detail or compare specific positions (e.g., realism vs. van Fraassen).
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Falsificationism: Science as Bold Conjecture and Risky Test
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Karl Popper proposed that what separates scientific theories from non-scientific ones is falsifiability: a scientific theory must make risky, testable predictions that could in principle be shown false. Rather than seeking verification through confirming instances, science advances by proposing bold conjectures and then attempting rigorous attempts to refute them. A theory survives only provisionally—its status improves the more ways it has withstood serious attempts at falsification, but it is never finally proven true.
Key points
- Falsifiability criterion: A theory is scientific if it rules out possible observational outcomes (i.e., it can be empirically refuted).
- Emphasis on boldness: Good scientific theories are risky—they predict novel, improbable phenomena that, if observed, strongly support the theory; if not observed, they expose the theory to refutation.
- Conjectures and refutations: Progress occurs through a cycle of proposing hypotheses and critically testing them; failed tests lead to rejection or revision.
- Demarcation and critique: Popper used falsificationism to demarcate science from pseudoscience (e.g., he criticized astrology and psychoanalysis for being unfalsifiable).
Limitations (brief)
- Auxiliary hypotheses: Failures can be blamed on background assumptions rather than the core theory (Duhem–Quine problem).
- Historical practice: Scientists often retain theories despite anomalies and modify them rather than immediately discarding them (see Kuhn).
- Some valuable theories are probabilistic or model-based and resist simple binary falsification.
Further reading
- Popper, K. R. The Logic of Scientific Discovery (1959).
- Duhem, P., and Quine, W. V. O. on theory underdetermination; Kuhn, T. S., The Structure of Scientific Revolutions (for contrasting views).
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Some valuable theories are probabilistic or model-based and resist simple binary falsification
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Many scientific theories are not packaged as single, universally true/false claims but as probabilistic statements or collections of interlocking models. For example, a theory might say “smoking increases the probability of lung cancer,” or provide a family of models that approximate complex systems (climate models, epidemic models, particle-physics simulations). Such theories have three features that make simple binary falsification (true vs. false) inappropriate:
- Probabilistic claims admit degrees. A probabilistic theory does not predict a single outcome; it assigns probabilities to outcomes. A low-probability event (e.g., a heavy smoker who never develops cancer) does not falsify the theory, because the theory allows such outcomes. Disconfirmation is a matter of shifting credences, not an immediate refutation.
- Models are idealized and partial. Models simplify, idealize, or isolate mechanisms (neglecting friction, treating agents as rational, averaging over small-scale details). When a model’s predictions fail in some conditions, it may be because the idealizations break down, not because the whole theoretical framework is false. Scientists often revise assumptions, change parameter values, or restrict the domain of applicability rather than discard the theory outright.
- Complex systems and underdetermination. Many model-based theories involve adjustable parameters, auxiliary hypotheses, and measurement uncertainties. Empirical anomalies can often be accommodated by modifying auxiliary assumptions (the “protective belt” in Lakatos’s terms), making a single failed prediction insufficient to falsify the core theory.
Implications
- Evaluation is comparative and evidential: scientists compare model fits, predictive accuracy, and explanatory scope, often using statistical inference or Bayesian updating rather than single-shot refutation.
- Methodological pluralism: different models serve different purposes (prediction, explanation, policy guidance), so success is measured by utility, robustness, and domain-specific adequacy, not only by passing a binary falsification test.
References: Karl Popper (falsificationism) for the contrast; Imre Lakatos (research programmes) and Bas van Fraassen (confirmation and empiricism) for nuances about theory change; Nancy Cartwright and Margaret Morrison for work on models and idealization.
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Falsifiability and the Scientific Criterion
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Emphasis on Boldness in Science
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Conjectures and Refutations
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Demarcation and Popper’s Falsificationism
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Auxiliary Hypotheses and the Duhem–Quine Problem
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Historical Practice: Retaining and Modifying Theories
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Popper’s The Logic of Scientific Discovery (1959)
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Duhem and Quine on Underdetermination — and Kuhn’s Contrast
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