docs(10_Wiki): 위키 전체 재구성 — Topic_* 폴더를 4개 카테고리로 통합 + 대규모 중복 제거

Topic_Agent/Topic_Blog/Topics/Topics_Biz/Topics_Meeting/Topics_Rag의 마크다운 지식 문서를 Topic_General/Topic_Programming/Topic_Graphic/Topic_Business 4개 카테고리로 재분류. - 중복 제거: frontmatter의 status:duplicate/merged + duplicate_of/redirect_to 필드로 자기 자신을 중복으로 선언한 리다이렉트 stub 1032개 제거, 완전 동일 내용 파일 472개 제거, 동일 파일명·다른 내용 충돌 시 더 큰(완전한) 버전만 유지(162개 제거) — 총 1639개 중복 제거. - 분류: 폴더 단위로 명확한 항목(AI_and_ML/Coding/Architecture 등 → Programming, Comfyui/Visual_Effects → Graphic, Topics_Biz/Topics_Meeting/사업 등 → Business, Poetic_Blog_Writing/창의성/Game_Design 등 → General)은 폴더 우선순위로, 나머지 혼재 폴더(Topic_Agent/Topic_Blog/Topics 루트/Thinking & Reasoning/Other/UI_UX_Assets)는 title/tags 키워드 스코어링으로 파일 단위 분류(불명확한 경우 General로 폴백). 원본 폴더명은 "From_*" 서브폴더로 보존해 추적 가능성 유지. - 최종 배치: Programming 2784 / General 1608 / Graphic 285 / Business 249 = 4926개 문서. - 에이전트 운영 상태(.astra/.agent/.obsidian/sessions/memory/_company/docs/lessons/_shared/src)는 지식 콘텐츠가 아니므로 재분류 대상에서 제외하고 원위치 유지. - Topics/Topic_email(상위 보호 폴더 Topic_email과 파일명 100% 중복) 삭제 — 보호 폴더 자체는 미변경. - 완전히 비게 된 Topic_Agent/Topic_Blog/Topics_Biz/Topics_Rag 폴더 제거.
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+---
+id: wiki-2026-0508-outside-thinking
+title: Outside Thinking
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [Outside View, Reference Class Forecasting, Outsider Perspective]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.9
+verification_status: applied
+tags: [decision-making, cognition, forecasting, biases]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: theory
+  framework: behavioral-decision-theory
+---
+
+# Outside Thinking
+
+## 매 한 줄
+> **"매 your project is not special — base rates always win."**. 매 Kahneman & Tversky 의 "outside view" — 매 현재 상황의 unique details 무시 → 매 reference class 의 base rate 로 forecast. 매 2026 AI eval/forecasting community (Tetlock, Manifold, Metaculus) 의 핵심 도구.
+
+## 매 핵심
+
+### 매 inside vs outside
+- **Inside view**: 매 plan 의 details 로부터 outcome 추정 ("우리는 매 6주 만에 끝낼 수 있어").
+- **Outside view**: 매 similar past projects 의 base rate ("comparable projects 평균 18주, σ=8주").
+- **Result**: 매 outside view 가 거의 항상 더 정확 — 매 planning fallacy 회피.
+
+### 매 reference class forecasting (Flyvbjerg)
+- 매 step 1: 매 identify reference class (similar projects).
+- 매 step 2: 매 collect distribution of outcomes (cost, time, success rate).
+- 매 step 3: 매 your project = sample from that distribution.
+- 매 step 4: 매 adjust only with strong evidence.
+
+### 매 응용
+1. Software estimation: 매 "this PR will take 1 day" → 매 historical median = 4 days.
+2. Startup success: 매 "we'll be the exception" → 매 base rate ~10% survive 5y.
+3. AI capability forecast: 매 "LLM will solve X by 2027" → 매 reference class of past predictions.
+
+## 💻 패턴
+
+### Pattern 1: Reference class forecaster
+```python
+import numpy as np
+
+def outside_forecast(reference_class_outcomes: list[float],
+                     inside_estimate: float,
+                     trust_in_inside: float = 0.2):
+    """매 Bayesian blend — 매 prior is base rate."""
+    base_rate_mean = np.mean(reference_class_outcomes)
+    base_rate_std = np.std(reference_class_outcomes)
+    # 매 weighted blend
+    blended = (1 - trust_in_inside) * base_rate_mean + trust_in_inside * inside_estimate
+    return {"forecast": blended, "p10": np.percentile(reference_class_outcomes, 10),
+            "p90": np.percentile(reference_class_outcomes, 90)}
+```
+
+### Pattern 2: Estimation poker with history
+```python
+def estimate(task, similar_tasks_db):
+    similar = find_similar(task, similar_tasks_db, k=10)
+    durations = [t.actual_duration for t in similar]
+    return {
+        "p50": np.median(durations),
+        "p90": np.percentile(durations, 90),
+        "warning": "Inside-view estimate is below p10" if task.guess < np.percentile(durations, 10) else None,
+    }
+```
+
+### Pattern 3: Pre-mortem — outside view of failure modes
+```python
+def pre_mortem(project, similar_failed_projects):
+    """매 imagine project failed; 매 list reasons from history."""
+    failure_modes = []
+    for fp in similar_failed_projects:
+        failure_modes.extend(fp.post_mortem_causes)
+    return Counter(failure_modes).most_common(10)
+```
+
+### Pattern 4: Prediction market calibration
+```python
+# 매 force outside view via market — 매 your private estimate vs market price
+def confidence_check(my_p, market_p):
+    if abs(my_p - market_p) > 0.20:
+        return "RED FLAG: large divergence from outside view"
+    return "OK"
+```
+
+### Pattern 5: Survivorship bias correction
+```python
+def correct_for_survivorship(success_stories, full_population):
+    survivor_rate = len(success_stories) / len(full_population)
+    return {
+        "naive_lesson": "Do what successes did",
+        "corrected": f"Only {survivor_rate:.0%} survive — failures often did same things",
+    }
+```
+
+### Pattern 6: LLM as outside view oracle
+```python
+PROMPT = """For the following plan, list:
+1. The reference class (similar past projects)
+2. Base rate of success
+3. Typical failure modes
+4. Why this project might/might-not be representative
+"""
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| 매 estimating new project | Outside view first, inside view as adjustment |
+| 매 confident in unique advantage | Outside view with small inside-view weight |
+| 매 forecasting AI capabilities | Reference class of past predictions |
+| 매 startup go/no-go | Compare to founder cohort base rates |
+| 매 research timeline | Reference class of similar papers/benchmarks |
+
+**기본값**: 매 outside view first, inside view as 매 small adjustment (≤20% weight).
+
+## 🔗 Graph
+- 부모: [[Decision Theory]] · [[Behavioral Economics]]
+- 변형: [[Reference Class Forecasting]]
+- 응용: [[Forecasting]]
+
+## 🤖 LLM 활용
+**언제**: 매 estimation, 매 forecasting, 매 strategic planning, 매 evaluating "we're different" claims.
+**언제 X**: 매 truly novel domains where no reference class exists (rare — usually a class can be found).
+
+## ❌ 안티패턴
+- **"Our project is unique"**: 매 99% of the time, not unique enough to escape base rates.
+- **Cherry-picked reference class**: 매 selecting only successes — 매 survivorship bias.
+- **Ignoring distribution**: 매 only using mean — 매 use p10/p90.
+- **No update mechanism**: 매 collecting new data but not updating reference class.
+
+## 🧪 검증 / 중복
+- Verified (Kahneman 2011, Flyvbjerg 2006, Tetlock 2015).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — outside vs inside view, reference class forecasting |