refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조

에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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+---
+id: wiki-2026-0508-creativity-research
+title: Creativity Research
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [Creativity Studies, Creative Cognition Research]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.9
+verification_status: applied
+tags: [creativity, psychology, cognition, research]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: en
+  framework: research-methods
+---
+
+# Creativity Research
+
+## 매 한 줄
+> **"매 creativity 의 measurable cognitive process — 매 mystical talent 아님"**. 매 1950 Guilford APA address 가 field 의 launch — 매 divergent thinking, fluency, originality 의 quantifiable. 매 2026 의 LLM-augmented co-creation, fMRI 의 default mode network 연구, computational creativity 의 active.
+
+## 매 핵심
+
+### 매 4P framework (Rhodes 1961)
+- **Person**: 매 traits — openness, tolerance for ambiguity, intrinsic motivation.
+- **Process**: 매 stages — preparation → incubation → illumination → verification (Wallas 1926).
+- **Product**: 매 novel + useful (Stein 1953 의 standard definition).
+- **Press**: 매 environment — domain, field gatekeepers (Csikszentmihalyi systems model).
+
+### 매 측정 (psychometrics)
+- **TTCT** (Torrance Tests of Creative Thinking): 매 fluency, flexibility, originality, elaboration.
+- **AUT** (Alternative Uses Task): 매 brick 의 uses 나열 — 매 divergent thinking 의 standard.
+- **CAT** (Consensual Assessment Technique, Amabile): 매 expert judges 의 product rating.
+- **RAT** (Remote Associates): 매 convergent creativity (3 cue → 1 link word).
+
+### 매 응용
+1. K-12 design thinking curriculum.
+2. 매 R&D ideation workshop (IDEO 의 protocols).
+3. 매 LLM prompt engineering 의 creativity scaffolding.
+
+## 💻 패턴
+
+### Divergent thinking score (AUT)
+```python
+def aut_score(responses: list[str], reference_corpus: dict[str, int]) -> dict:
+    """Score divergent-thinking output: fluency, flexibility, originality."""
+    fluency = len(responses)
+    categories = {classify_category(r) for r in responses}
+    flexibility = len(categories)
+    # originality = 1 - frequency in reference corpus (lower freq = more original)
+    total = sum(reference_corpus.values()) or 1
+    originality = sum(
+        1 - (reference_corpus.get(r.lower(), 0) / total) for r in responses
+    ) / max(fluency, 1)
+    return {"fluency": fluency, "flexibility": flexibility, "originality": originality}
+```
+
+### LLM-augmented divergent ideation
+```python
+from anthropic import Anthropic
+
+client = Anthropic()
+
+def co_creative_ideation(prompt: str, n: int = 20) -> list[str]:
+    """Use Claude as a divergent-thinking partner — temperature high for variance."""
+    msg = client.messages.create(
+        model="claude-opus-4-7",
+        max_tokens=2000,
+        temperature=1.0,
+        messages=[{
+            "role": "user",
+            "content": f"Generate {n} maximally diverse, novel uses for: {prompt}. "
+                       f"Span categories. Avoid clichés. One per line."
+        }],
+    )
+    return [line.strip("- ") for line in msg.content[0].text.splitlines() if line.strip()]
+```
+
+### Consensual Assessment (CAT) aggregation
+```python
+import numpy as np
+from scipy.stats import pearsonr
+
+def cat_reliability(ratings: np.ndarray) -> float:
+    """Inter-rater reliability via Cronbach's alpha across expert judges."""
+    k = ratings.shape[1]
+    item_var = ratings.var(axis=0, ddof=1).sum()
+    total_var = ratings.sum(axis=1).var(ddof=1)
+    return (k / (k - 1)) * (1 - item_var / total_var)
+```
+
+### Incubation effect simulation
+```python
+def incubation_benefit(initial_attempt_score: float, incubation_minutes: int) -> float:
+    """Sio & Ormerod 2009 meta-analysis: ~0.3 SD boost after incubation."""
+    if incubation_minutes < 5:
+        return initial_attempt_score
+    return initial_attempt_score + 0.3 * min(incubation_minutes / 30, 1.0)
+```
+
+### Default Mode Network proxy (resting-state correlation)
+```python
+def dmn_creativity_correlation(dmn_connectivity: float, ecn_connectivity: float) -> float:
+    """Beaty et al. 2018: high creativity = strong DMN ↔ ECN coupling."""
+    return dmn_connectivity * ecn_connectivity  # simplified product proxy
+```
+
+### Equivalence-class feature (Mednick RAT)
+```python
+def remote_associates_solve(cues: tuple[str, str, str], assoc_db: dict) -> str | None:
+    """Find a single word that associates with all three cues."""
+    sets = [set(assoc_db.get(c, [])) for c in cues]
+    common = set.intersection(*sets)
+    return next(iter(common), None)
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| Quick classroom screen | TTCT short form |
+| Real-world product creativity | CAT with 3+ domain experts |
+| Lab divergent thinking | AUT + originality corpus |
+| Insight problem solving | RAT or compound remote associates |
+| LLM augmentation | high-temperature ideation + human convergent filter |
+
+**기본값**: 매 AUT + CAT for research; 매 LLM-as-divergent-partner + human-as-convergent-filter for applied work.
+
+## 🔗 Graph
+- 부모: [[Cognitive Psychology]]
+- 변형: [[Divergent Thinking]] · [[Convergent Thinking]] · [[Computational_Creativity|Computational Creativity]]
+- 응용: [[Design Thinking]] · [[Brainstorming]]
+- Adjacent: [[Default Mode Network]]
+
+## 🤖 LLM 활용
+**언제**: 매 divergent ideation phase — 매 broad space exploration, 매 cliché breaking, 매 cross-domain analogies.
+**언제 X**: 매 convergent evaluation alone — 매 LLM 의 novelty calibration 의 약함 (training data bias toward common). 매 originality scoring 시 의 corpus-based metric 결합 필요.
+
+## ❌ 안티패턴
+- **Brainstorming = creativity 의 동일시**: 매 group brainstorming 의 production blocking — 매 nominal groups 가 실제로 더 많은 ideas (Diehl & Stroebe 1987).
+- **Originality 만 추적**: 매 useful 의 손실 — 매 novel + useful 가 정의.
+- **Single judge CAT**: 매 inter-rater reliability 의 unverifiable.
+- **TTCT 만 의 의존**: 매 ecological validity 의 약함 — real-world creative achievement prediction 의 modest (r ≈ 0.2-0.3).
+
+## 🧪 검증 / 중복
+- Verified (Guilford 1950, Torrance 1966, Amabile 1982, Beaty et al. 2018 NeuroImage).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — 4P framework, AUT/CAT/RAT measurement, LLM co-creation patterns 추가 |