2nd/10_Wiki/Topics/Domain_General/From_Other/Outside-Thinking.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-outside-thinking	Outside Thinking	10_Wiki/Topics	verified	self	Outside View Reference Class Forecasting Outsider Perspective	none	A	0.9	applied	decision-making cognition forecasting biases		2026-05-10	pending	language framework theory 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

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

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

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

# 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

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

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