---
id: wiki-2026-0508-손실-회피
title: 손실 회피 (Loss Aversion)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Loss Aversion, 손실 회피, Endowment Effect Adjacent, Prospect Theory Loss]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [behavioral-economics, ux, game-design, prospect-theory, kahneman]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: typescript
  framework: react
---

# 손실 회피 (Loss Aversion)

## 매 한 줄
> **"매 loss 의 매 psychological weight 의 매 gain 의 ~2배"**. Kahneman & Tversky 의 1979 Prospect Theory 핵심 발견 — $100 잃는 pain ≈ $200 얻는 pleasure. 매 modern UX, game economy, pricing, behavioral nudge 의 매 ubiquitous lever.

## 매 핵심

### 매 Prospect Theory 의 가치 함수
- **Value function**: 매 origin (status quo) 기준 의 asymmetric. Gain 측 concave, loss 측 convex + steeper.
- **Loss aversion coefficient λ ≈ 2.0–2.5** (typical empirical range).
- **Reference-dependent**: 매 absolute wealth 가 아닌 reference point 기준.
- **Diminishing sensitivity**: $100→$200 의 gain 이 $1100→$1200 보다 큰 impact.

### 매 Cognitive 메커니즘
- **Endowment effect**: 소유한 것 의 valuation 이 동일 item 의 acquire price 보다 높음.
- **Status quo bias**: change 의 potential downside 가 upside 보다 무겁게 느껴짐.
- **Sunk cost fallacy**: 이미 잃은 것 을 회복하려는 irrational continuation.

### 매 응용
1. **UX framing**: "Save $20" (gain) vs "Lose $20 if you don't act" (loss) — loss frame 의 conversion 우수.
2. **Game retention**: streak / lose-progress 위협 (Duolingo streak freeze).
3. **Pricing anchoring**: trial → paid 의 loss aversion lever (already-using 의 endowment).

## 💻 패턴

### Loss-framed CTA copy A/B test
```typescript
const variants = {
  control: { headline: "Save 30% on Pro", cta: "Upgrade now" },
  loss:    { headline: "Don't lose Pro features tomorrow", cta: "Keep Pro access" },
};

function trackVariant(userId: string, variant: keyof typeof variants) {
  analytics.track("cta_view", { userId, variant, ts: Date.now() });
}
```

### Streak system (Duolingo pattern)
```typescript
interface Streak {
  count: number;
  lastActiveDay: string; // YYYY-MM-DD
  freezesAvailable: number;
}

function updateStreak(s: Streak, today: string): Streak {
  const days = daysBetween(s.lastActiveDay, today);
  if (days === 1) return { ...s, count: s.count + 1, lastActiveDay: today };
  if (days === 2 && s.freezesAvailable > 0)
    return { ...s, freezesAvailable: s.freezesAvailable - 1, lastActiveDay: today };
  if (days === 0) return s;
  return { ...s, count: 0, lastActiveDay: today }; // streak lost
}
```

### Endowment via free trial → paid
```typescript
// User accumulates value during trial; cancellation = loss frame
function trialSummary(usage: Usage) {
  return {
    headline: `You've created ${usage.docCount} documents`,
    losses: [
      `${usage.collaborators} teammates will lose access`,
      `${usage.gbStored}GB of files become read-only`,
    ],
    cta: "Keep your work — upgrade now",
  };
}
```

### Game economy — escrow / forfeit on disconnect
```typescript
// Loss aversion as engagement: forfeit currency if you abandon ranked match
function onMatchAbandon(player: Player, match: Match) {
  const penalty = match.entryFee * 1.5; // > entry fee — sharper sting
  player.currency -= penalty;
  notify(player, `You forfeited ${penalty} for leaving`);
}
```

### Loss-aversion-aware notification timing
```typescript
// Trigger reminder when user is closest to losing accrued value
function shouldNotifyAboutStreak(s: Streak): boolean {
  const hoursLeft = hoursUntilEndOfDay();
  return s.count >= 7 && hoursLeft < 6 && !s.notifiedToday;
}
```

### Prospect-theory utility (decision sim)
```python
def prospect_value(outcome: float, ref: float = 0, lam: float = 2.25,
                   alpha: float = 0.88) -> float:
    delta = outcome - ref
    if delta >= 0:
        return delta ** alpha
    return -lam * (-delta) ** alpha
```

## 매 결정 기준
| 상황 | Loss aversion 활용? |
|---|---|
| Onboarding (no prior value) | 약함 — gain frame 우수 |
| Retention (existing value) | 강함 — loss frame 우수 |
| Pricing anchor | Endowment + price reference |
| Subscription cancel flow | Inventory of "what you'll lose" |
| Dark pattern territory? | 매 ethical line — manipulative if value 가 없음 |

**기본값**: 매 user 의 actual accrued value 가 있을 때 만 loss frame. 매 fabricated scarcity 는 매 dark pattern.

## 🔗 Graph
- 부모: [[Behavioral-Economics]] · [[Prospect-Theory]] · [[Cognitive-Biases]]
- 변형: [[Endowment-Effect]] · [[Sunk-Cost-Fallacy]]

## 🤖 LLM 활용
**언제**: copy A/B variant generation, retention email subject lines, game design lever brainstorm.
**언제 X**: 매 ethical line 의 judgment — manipulative copy 의 detection 은 human review 필수.

## ❌ 안티패턴
- **Manufactured loss**: 가짜 limited-time scarcity → 매 dark pattern, brand trust 손상.
- **Loss frame on cold acquisition**: prospect 가 endowment 없음 → backfire.
- **Over-using λ=2.5 mental model**: 매 individual variance 큼 (λ 0.5–4.0). Test, don't assume.
- **Sunk cost induction**: 매 player 의 deeper investment 강요 — 매 ethical issue.

## 🧪 검증 / 중복
- Verified (Kahneman & Tversky 1979 "Prospect Theory", "Thinking Fast and Slow" 2011, Duolingo retention experiments published 2020-2024).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — Prospect Theory + UX/game application patterns |
