Antigravity Agent
148 lines
5.3 KiB
Markdown
148 lines
5.3 KiB
Markdown
---
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id: wiki-2026-0508-assumptions-vs-facts
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title: Assumptions vs Facts
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Fact-Assumption Distinction, Premise vs Evidence]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [reasoning, epistemology, decision-making, critical-thinking]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: na
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---
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# Assumptions vs Facts
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## 매 한 줄
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> **"매 fact 는 매 verifiable observation, 매 assumption 은 매 unverified premise"**. 매 둘 의 conflation 매 most decision failure 의 root. 매 military intelligence (CIA Tradecraft Primer), 매 software engineering (RFC, design doc), 매 LLM agent reasoning (chain-of-thought 매 assumption 명시) 모두 의 핵심 discipline.
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## 매 핵심
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### 매 정의
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- **Fact**: 매 currently verifiable claim — 매 measurement, 매 reproducible observation, 매 authoritative record.
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- **Assumption**: 매 not verified, 매 taken as true 매 reasoning 진행 위해. 매 implicit / explicit.
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- **Inference**: 매 fact + assumption → 매 conclusion.
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### 매 Verification spectrum
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- **Hard fact**: 매 measurement (e.g., latency = 142ms p95).
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- **Soft fact**: 매 expert testimony / consensus (e.g., "FDA-approved").
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- **Reasonable assumption**: 매 base rate / 매 prior (e.g., "user 매 attention < 10s").
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- **Speculative assumption**: 매 untested premise (e.g., "competitor 매 Q4 launch").
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### 매 응용
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1. **Design doc**: 매 "Assumptions" section 별도 — 매 reviewer 검증.
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2. **Intelligence analysis**: 매 ACH (Analysis of Competing Hypotheses).
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3. **Postmortem**: 매 implicit assumption 적출 — 매 next-time fact 로 verify.
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4. **LLM CoT**: 매 reasoning chain 에서 매 assumption 의 explicit tag.
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## 💻 패턴
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### Pattern 1: 매 Design doc template
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```markdown
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## Facts
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- 매 current p95 latency: 240ms (verified via 매 grafana 2026-05-09).
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- 매 user count: 1.2M MAU (analytics dashboard).
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## Assumptions
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- [A1] 매 traffic grow 30% YoY (prior: 2024-2025 trend).
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- [A2] 매 redis cluster 매 horizontal scale 가능 (vendor docs, untested at our scale).
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## Inferences
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- A1 + Facts → 매 Q4 capacity = 1.56M MAU.
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- A2 + Facts → 매 cache layer 매 bottleneck 의 X.
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## Validation plan
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- A1: 매 monthly reforecast.
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- A2: 매 Q3 load-test 8x current.
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```
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### Pattern 2: 매 ACH (Analysis of Competing Hypotheses)
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```python
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import numpy as np
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hypotheses = ["H1: 매 supply shock", "H2: 매 demand drop", "H3: 매 competitor"]
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evidence = ["E1: price up", "E2: query down", "E3: rival ad spike"]
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# 매 매 evidence × hypothesis: consistent (+1), inconsistent (-1), N/A (0)
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M = np.array([
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# E1, E2, E3
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[+1, 0, 0], # H1
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[-1, +1, 0], # H2
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[ 0, +1, +1], # H3
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])
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scores = M.sum(axis=1)
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for h, s in zip(hypotheses, scores):
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print(h, s)
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# 매 lowest disconfirmed = 매 most likely (CIA tradecraft logic)
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```
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### Pattern 3: 매 Assumption tagging in CoT
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```python
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def reason_with_tags(query: str) -> str:
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return llm(f"""
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Answer step by step. For every claim:
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- Tag [FACT: source] if verifiable.
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- Tag [ASSUMP: confidence 0-1] if untested.
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- Tag [INFER] if derived.
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Q: {query}
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""")
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```
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### Pattern 4: 매 Premortem (assumption stress-test)
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```markdown
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Imagine the project failed in 6 months. List the 5 most likely
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failed assumptions. For each, design a 2-week experiment to test
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it now.
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```
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### Pattern 5: Confidence score 매 calibration
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```python
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predictions = [] # list of (claim, confidence, actual_outcome)
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brier = sum((c - a)**2 for _, c, a in predictions) / len(predictions)
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print(f"Brier score: {brier:.3f}") # 매 lower = better calibration
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```
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## 매 결정 기준
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| 상황 | Treat as |
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|---|---|
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| 매 metric in current dashboard | Fact (with date) |
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| 매 vendor capability claim | Soft fact, 매 verify if critical |
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| 매 future user behavior | Assumption — 매 explicit |
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| 매 "everyone knows" | 매 strong assumption — 매 challenge |
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| 매 LLM output | Assumption until cross-checked |
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**기본값**: 매 reasoning 시작 시 매 explicit "Facts" / "Assumptions" 분리. 매 implicit assumption 의 surface — 매 brittle.
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## 🔗 Graph
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- 부모: [[Belief-Revision]] · [[Bayesian-Updating]]
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- 변형: [[Bayes-Theorem]] · [[Hypostatic-Abstraction]]
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- 응용: [[Problem Solving Process]] · [[Process_Reflection_Template]]
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- Adjacent: [[Big-Picture]] · [[Outside-Thinking]] · [[Anticipation]]
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## 🤖 LLM 활용
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**언제**: 매 agent design — 매 [FACT]/[ASSUMP] tagging 매 hallucination detection 도움. 매 reasoning trace audit.
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**언제 X**: 매 creative ideation — 매 over-tagging 매 flow 방해.
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## ❌ 안티패턴
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- **Implicit assumption**: 매 unmentioned premise — 매 reviewer 못 catch.
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- **Fact inflation**: 매 weak evidence 의 hard fact 처럼 표현.
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- **Confidence theater**: 매 "obviously" / "clearly" — 매 hidden assumption marker.
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- **Single-source fact**: 매 1 source = 매 still soft. 매 triangulate.
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- **Stale fact**: 매 6개월 전 metric — 매 currently fact 인지 재검증.
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## 🧪 검증 / 중복
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- Verified (CIA Tradecraft Primer 2009, Heuer *Psychology of Intelligence Analysis*, Tetlock *Superforecasting*).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — ACH + 매 design-doc pattern + LLM CoT tagging |
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