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philosophy-of-science skill

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This skill helps you master philosophy of science by explaining methods, realism, theory change, and demarcation to improve scientific reasoning.

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---
name: philosophy-of-science
description: "Master philosophy of science - scientific method, explanation, realism, theory change. Use for: scientific methodology, explanation, realism/anti-realism, paradigms. Triggers: 'scientific method', 'falsification', 'Popper', 'Kuhn', 'paradigm', 'scientific explanation', 'scientific realism', 'instrumentalism', 'theory change', 'confirmation', 'induction problem', 'underdetermination', 'demarcation', 'reduction', 'emergence'."
---

# Philosophy of Science Skill

Master the philosophical foundations of science: What is scientific method? What is explanation? Are scientific theories true?

## Core Questions

| Question | Issue |
|----------|-------|
| What distinguishes science from non-science? | Demarcation |
| How do we confirm theories? | Confirmation |
| What is scientific explanation? | Explanation |
| Are theories true or useful fictions? | Realism |
| How does science change? | Theory change |

---

## Scientific Method

### The Problem of Induction

**Hume's Problem**: How do we justify inductive inference?
- Past regularities don't logically guarantee future ones
- Cannot use induction to justify induction (circular)
- Yet science relies on induction

### Falsificationism (Popper)

```
POPPER'S FALSIFICATIONISM
═════════════════════════

DEMARCATION CRITERION
├── Science: Falsifiable claims
├── Pseudo-science: Unfalsifiable
└── Examples: Astrology, Freud (unfalsifiable)

METHOD
├── Bold conjectures
├── Severe tests
├── Refutation → new conjecture
└── Corroboration ≠ confirmation

KEY IDEA:
We never confirm theories
We only fail to falsify them
Asymmetry: One counterexample refutes
```

**Problems**:
- Theories rarely abandoned on single refutation
- Auxiliary hypotheses can absorb refutation
- No purely observational test

### Paradigms (Kuhn)

```
KUHN'S STRUCTURE OF SCIENTIFIC REVOLUTIONS
══════════════════════════════════════════

NORMAL SCIENCE
├── Work within paradigm
├── Puzzle-solving
├── Anomalies accumulate
└── Paradigm defines problems, methods

CRISIS
├── Too many anomalies
├── Alternative paradigms emerge
├── Debate between paradigms
└── Incommensurability

SCIENTIFIC REVOLUTION
├── Paradigm shift
├── Not cumulative progress
├── New worldview
└── Gestalt switch

EXAMPLES:
├── Ptolemy → Copernicus
├── Newton → Einstein
└── Phlogiston → Oxygen
```

---

## Scientific Explanation

### Deductive-Nomological (D-N) Model

```
D-N MODEL (Hempel)
══════════════════

EXPLANATION STRUCTURE:
L₁, L₂, ... Lₙ  (Laws)
C₁, C₂, ... Cₙ  (Conditions)
─────────────────
E              (Explanandum)

REQUIREMENTS:
├── Deductively valid
├── Laws are essential
├── Empirically testable
└── True premises

EXAMPLE:
All metals expand when heated.
This is metal.
This was heated.
∴ This expanded.
```

**Problems**:
- Symmetry problem (flagpole and shadow)
- Irrelevance problem
- Statistical explanation

### Causal-Mechanical Model

- Explanation = tracing causal mechanism
- Not just subsumption under laws
- Mechanisms explain, not just correlate

### Unificationism

- Explanation = unifying diverse phenomena
- Fewer patterns explaining more
- Newton unified celestial and terrestrial motion

---

## Scientific Realism

### The Debate

**Scientific Realism**:
- Mature scientific theories are approximately true
- Theoretical entities (electrons, genes) exist
- Science aims at truth

**Anti-Realism (Instrumentalism)**:
- Theories are useful tools
- Theoretical terms don't refer
- Science aims at empirical adequacy

### Arguments for Realism

**No Miracles Argument**:
- Science's success would be miraculous if theories weren't true
- Best explanation of predictive success is truth
- "The only philosophy that doesn't make science a miracle"

### Arguments Against Realism

**Pessimistic Meta-Induction**:
- Past "successful" theories were false
- Caloric, phlogiston, ether
- Current theories probably also false

**Underdetermination**:
- Multiple theories compatible with same evidence
- Evidence doesn't uniquely determine theory
- Why think ours is true?

### Structural Realism

**Epistemic**: We can know structure, not nature
**Ontic**: Structure is all there is

---

## Reduction and Emergence

### Reductionism

- Higher-level sciences reducible to lower
- Biology → Chemistry → Physics
- Unity of science thesis

### Emergence

- Some properties not reducible
- Whole greater than parts
- Consciousness? Life?

### Multiple Realizability

- Same higher-level state, different lower-level realizations
- Pain in humans ≠ pain in octopi (neurally)
- Blocks type-identity reduction

---

## Key Debates

### Demarcation

- What makes something science?
- Falsifiability? Paradigms? Method?
- Is demarcation possible?

### Theory Choice

- Empirical adequacy
- Simplicity, parsimony
- Explanatory power
- Fruitfulness

### Values in Science

- Value-free ideal achievable?
- Social influences on science
- Science studies, feminist philosophy of science

---

## Key Vocabulary

| Term | Meaning |
|------|---------|
| Falsification | Disproving through counterevidence |
| Paradigm | Shared framework for research |
| Incommensurability | Paradigms can't be compared |
| Confirmation | Evidence supporting theory |
| Underdetermination | Evidence doesn't determine theory |
| Instrumentalism | Theories are tools, not truths |
| Reduction | Higher explained by lower |
| Emergence | Irreducible higher-level properties |
| Demarcation | Distinguishing science from non-science |
| Corroboration | Surviving falsification attempts |

---

## Integration with Repository

### Related Themes
- `thoughts/knowledge/`: Scientific knowledge
- `thoughts/consciousness/`: Neuroscience methodology

Overview

This skill offers a compact masterclass in the philosophy of science: scientific method, explanation, realism, and theory change. It helps users analyze demarcation, confirmation, induction problems, paradigms, reduction, and emergence. Use it to clarify conceptual roots of scientific practice and evaluate methodological claims.

How this skill works

The skill inspects arguments, models, and thought experiments from canonical accounts (Hume, Popper, Kuhn, Hempel) and contemporary responses. It identifies core issues—induction, falsification, paradigm shifts, explanatory models, realism vs. instrumentalism—and maps their implications for methodology and theory choice. It highlights trade-offs (e.g., corroboration vs. confirmation) and shows how auxiliary hypotheses, underdetermination, and multiple realizability affect inference.

When to use it

Designing or critiquing experimental methodology
Evaluating whether a claim qualifies as scientific
Interpreting theory change and historical scientific revolutions
Clarifying types of scientific explanation for teaching or writing
Debating realism, instrumentalism, or structural realism

Best practices

Distinguish empirical evidence from interpretive frameworks before drawing conclusions
Explicitly state auxiliary hypotheses used in tests and how they could be revised
Use plural criteria for theory choice: accuracy, simplicity, explanatory power, and fertility
Be cautious applying falsification naively—examine theory-laden observation and background assumptions
Consider both causal/mechanistic and unificationist accounts when evaluating explanations

Example use cases

Assess whether a proposed diagnostic test provides decisive falsification or only underdetermined support
Explain a theory shift (e.g., Newton→Einstein) in terms of paradigm change and explanatory advantages
Compare realist and instrumentalist readings of a successful but theoretically contested entity (e.g., genes, electrons)
Design instructional materials distinguishing D-N, causal-mechanical, and unificationist explanations
Analyze claims about reduction or emergence in interdisciplinary research (biology ↔ physics)

FAQ

Does falsification solve the problem of induction?

No. Falsification reframes confirmation but does not justify inductive inference; it highlights asymmetry between refuting and confirming hypotheses and encounters issues like auxiliary hypotheses and theory-ladenness.

When should I prefer causal-mechanical explanation over D-N style accounts?

Prefer causal-mechanical accounts when mechanism, process, or interventions matter; use D-N models for law-like subsumptions that yield deductive clarity, but be aware of symmetry and irrelevance problems.