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Antigravity Agent
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---
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id: wiki-2026-0508-cognitive-biases
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title: Cognitive Biases
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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: [인지 편향, cognitive biases, heuristics, Tversky-Kahneman, Thinking Fast and Slow, debiasing, nudge]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.93
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verification_status: applied
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tags: [psychology, cognitive-bias, kahneman, behavioral-economics, debiasing, nudge, decision-making, ml-bias]
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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: psychology / decision theory
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applicable_to: [Decision Systems, Product Design, ML Bias Mitigation]
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---
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# Cognitive Biases
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## 매 한 줄
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> **"매 thinking 의 shortcut 의 trap"**. Kahneman 의 System 1 (fast / heuristic) vs System 2 (slow / logical). 매 evolutionary 의 useful, 매 modern context 의 misfire. 매 modern AI 의 bias source. 매 design 의 leverage (nudge) or 매 mitigation (debiasing).
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## 매 핵심
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### 매 major bias
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#### Cognitive
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- **Confirmation bias**: 매 belief 의 support 만.
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- **Availability heuristic**: 매 recent / vivid.
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- **Anchoring**: 매 first number.
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- **Representativeness**: 매 stereotype.
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- **Hindsight**: 매 "I knew it".
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- **Survivorship**: 매 winner 만 의 분석.
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- **Sunk cost**: 매 already-invested 의 maintain.
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#### Social
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- **In-group bias**: 매 our group 의 prefer.
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- **Authority bias**: 매 expert 의 over-trust.
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- **Bandwagon**: 매 majority 의 follow.
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- **Halo effect**: 매 1 trait → 매 all.
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#### Self
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- **Dunning-Kruger**: 매 incompetent 의 over-confident.
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- **Fundamental attribution**: 매 others = 매 character, 매 self = 매 situation.
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- **Self-serving**: 매 success = self, 매 failure = environment.
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- **Optimism bias**: 매 future 의 over-rosy.
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#### Loss
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- **Loss aversion**: 매 loss > 매 gain (2× weight).
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- **Endowment effect**: 매 own 의 over-value.
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- **Status quo bias**: 매 default keep.
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### Kahneman: System 1 vs System 2
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| System 1 | System 2 |
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|---|---|
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| Fast | Slow |
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| Automatic | Deliberate |
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| Pattern | Logic |
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| Cheap | Expensive |
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| Bias prone | Bias correct |
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→ 매 모든 해결 의 X. 매 둘 다 needed.
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### 매 history
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- Tversky-Kahneman 1974, "Judgment under Uncertainty".
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- Prospect Theory (1979) — Nobel.
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- Kahneman "Thinking Fast and Slow" (2011).
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- Cialdini "Influence" (1984).
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- Thaler "Nudge" (2008) — Nobel.
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### 매 modern AI 의 응용
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#### Bias 의 ML 의 source
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- 매 training data 의 인간 의 bias 의 reflect.
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- 매 amplification of existing.
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- 매 representation skew.
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#### Debiasing
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- 매 [[Bias-Correction-Algorithm]] 참조.
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- 매 fairness metric.
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- 매 counterfactual.
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#### LLM-specific bias
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- **Sycophancy**: 매 user 의 agree.
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- **Position bias**: 매 first / last 의 prefer.
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- **Recency**: 매 latest token 의 weight ↑.
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- **Anchoring**: 매 example 의 over-weight.
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#### Prompt engineering 의 mitigation
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- 매 chain-of-thought.
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- 매 self-critique.
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- 매 multiple perspective.
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- 매 explicit "consider opposite".
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### Nudge (Thaler-Sunstein)
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- 매 default 의 power.
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- 매 choice architecture.
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- 매 friction 의 control.
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- 매 loss frame vs gain frame.
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### 매 Dark Pattern (anti-nudge)
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- 매 hidden cost.
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- 매 confirm-shaming.
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- 매 forced continuity.
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- 매 misdirection.
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- 매 [[Addiction Neuroscience]] 참조.
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### 매 debiasing 기법
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1. **Premortem** (Klein): 매 imagine failure.
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2. **Red team / devil's advocate**.
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3. **Anonymous voting**.
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4. **Decision journal** (Thaler).
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5. **Outside view** (base rate).
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6. **Multi-perspective** (10 framework).
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7. **Fermi estimation**.
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8. **Evidence-based reasoning**.
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## 💻 패턴
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### Decision journal (Bayesian)
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```python
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class DecisionJournal:
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def __init__(self):
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self.entries = []
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def log(self, decision, alternatives, expected_outcome, confidence, reasoning):
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self.entries.append({
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'date': datetime.now(),
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'decision': decision,
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'alternatives': alternatives,
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'expected_outcome': expected_outcome,
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'confidence': confidence, # 0-1
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'reasoning': reasoning,
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'actual_outcome': None,
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'review_date': None,
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})
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def review(self, idx, actual):
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e = self.entries[idx]
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e['actual_outcome'] = actual
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e['review_date'] = datetime.now()
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# 매 calibration tracking
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return {
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'predicted': e['expected_outcome'],
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'actual': actual,
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'match': actual == e['expected_outcome'],
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'confidence_was': e['confidence'],
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}
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def calibration(self):
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"""매 pred prob ↔ 매 actual frequency."""
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bins = collections.defaultdict(list)
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for e in self.entries:
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if e['actual_outcome'] is None: continue
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bin = int(e['confidence'] * 10) / 10
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bins[bin].append(e['actual_outcome'] == e['expected_outcome'])
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return {b: np.mean(outcomes) for b, outcomes in bins.items()}
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```
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### Premortem
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```python
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def premortem(plan):
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"""매 imagine 1 year future 의 failure."""
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return {
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'imagine_state': 'plan failed catastrophically',
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'failure_modes': brainstorm([
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'biggest reason',
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'early warning signs',
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'binding constraint',
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'wrong assumption',
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]),
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'mitigations': [], # 매 each mode 의 plan
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}
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```
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### Anchoring counter
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```python
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def negotiate_without_anchor(target, your_estimate):
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"""매 first number 의 anchor 의 avoid."""
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if get_initial_offer() is None:
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# 매 don't go first
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ask_for_their_offer()
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initial = get_initial_offer()
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# 매 anchor 의 explicit acknowledge 의 mitigate
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print(f'Their anchor: {initial}, my estimate: {your_estimate}')
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if abs(initial - your_estimate) > your_estimate * 0.3:
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# 매 wide gap → 매 reset with reasoning
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reset_with_data(your_estimate)
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return negotiate_around(your_estimate)
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```
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### LLM debiasing prompt
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```python
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def cot_with_devils_advocate(question):
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return f"""Analyze this:
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{question}
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Step 1: Initial answer.
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Step 2: List 3 strongest counter-arguments.
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Step 3: Re-evaluate considering counter-arguments.
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Step 4: Final answer with confidence (0-1).
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Format: JSON only."""
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```
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### Sycophancy detection (LLM)
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```python
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def sycophancy_check(model, prompt):
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"""매 user 의 stated opinion 의 sway?"""
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a = model(f"{prompt}\nWhat do you think?")
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b = model(f"I strongly believe X is correct. {prompt}\nWhat do you think?")
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c = model(f"I strongly believe X is wrong. {prompt}\nWhat do you think?")
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if assesses_X_correct(a) != assesses_X_correct(b) or \
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assesses_X_correct(a) != assesses_X_correct(c):
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return 'WARN: sycophantic'
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return 'OK'
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```
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### Choice architecture (nudge)
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```tsx
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// 매 default 의 power — opt-out 의 organ donor 의 95% vs opt-in 의 15%
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function NewsletterSignup() {
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return (
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<form>
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<label>
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<input type="checkbox" defaultChecked />
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매 newsletter 구독 (opt-out)
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</label>
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</form>
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);
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}
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// 매 ❌ Dark pattern (avoid)
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function CancelSubscription() {
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return (
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<button>
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Yes, cancel and lose all my benefits forever 😢
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</button>
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);
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}
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```
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### Anti-confirmation (red team)
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```python
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def red_team_review(decision):
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return [
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('What evidence would change your mind?', None),
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('What did you NOT consider?', None),
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('Who would disagree, and why?', None),
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('What is the strongest argument against?', None),
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('If you fail, what is the most likely cause?', None),
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]
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```
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### Survivorship bias check
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```python
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def survivorship_audit(success_set, full_set):
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success_traits = traits(success_set)
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base_rate_traits = traits(full_set) # 매 includes failures
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biased_traits = []
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for trait, success_rate in success_traits.items():
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base = base_rate_traits.get(trait, 0)
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if success_rate > base * 1.5:
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biased_traits.append({
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'trait': trait,
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'success_rate': success_rate,
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'base_rate': base,
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'inflation': success_rate / base if base else 'inf',
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})
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return biased_traits
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```
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## 🤔 결정 기준
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| 상황 | Counter-bias |
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|---|---|
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| Big decision | Decision journal + premortem |
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| Negotiation | Don't go first + reset |
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| LLM use | CoT + multiple perspective |
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| Hiring | Structured interview + scorecard |
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| Investing | Outside view + base rate |
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| Group meeting | Anonymous voting |
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| Strategy | Red team |
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| Daily | Mindfulness + slow down |
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**기본값**: 매 explicit slow-down + 매 system 2 의 invoke + 매 evidence-based.
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## 🔗 Graph
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- 부모: [[Psychology]] · [[Decision Theory]] · [[Behavioral-Economics]]
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- 변형: [[Confirmation Bias]] · [[Loss-Aversion]]
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- 응용: [[Nudge]] · [[Debiasing]]
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- Adjacent: [[Bounded_Rationality|Bounded-Rationality]] · [[Bias-Correction-Algorithm]] · [[Algorithmic Fairness]] · [[Beliefs]] · [[Addiction Neuroscience]] (dark pattern)
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- 사상가: [[Kahneman]]
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## 🤖 LLM 활용
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**언제**: 매 decision design. 매 product UX. 매 negotiation prep. 매 LLM bias mitigation. 매 hiring.
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**언제 X**: 매 dark pattern (manipulation). 매 specific medical / mental health.
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## ❌ 안티패턴
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- **Bias 의 fix 의 unrealistic**: 매 always present.
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- **Awareness 의 only**: 매 actual 의 reduce 의 limited.
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- **모든 bias 의 fight**: 매 some 의 useful (heuristic).
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- **Dark pattern 의 leverage**: 매 short-term gain, 매 long-term loss.
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- **No calibration**: 매 confidence 의 wrong.
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- **Sycophantic LLM 의 trust**: 매 false validation.
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## 🧪 검증 / 중복
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- Verified (Tversky-Kahneman, Kahneman "Thinking", Cialdini "Influence", Thaler "Nudge").
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- 신뢰도 A.
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- Related: [[Bounded_Rationality|Bounded-Rationality]] · [[Beliefs]] · [[Bias-Correction-Algorithm]] · [[Algorithmic Fairness]] · [[Decision Theory]] · [[Addiction Neuroscience]].
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — bias catalog + Kahneman + LLM-specific + 매 decision journal / premortem / CoT code |
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