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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Antigravity Agent
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---
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id: wiki-2026-0508-게임-경제-설계
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title: 게임 경제 설계
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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: [Game Economy, Virtual Economy Design]
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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: [game-design, economy, monetization, simulation]
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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/TypeScript
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framework: Unity/Unreal/Web3
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---
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# 게임 경제 설계
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## 매 한 줄
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> **"매 faucets × sinks × velocity 의 closed-loop monetary policy"**. 매 게임 경제는 macroeconomic theory 의 micro-application — currency supply, inflation, time-vs-money trade-off의 deliberate engineering. 2026 standard는 dual-currency (soft/hard) + battle-pass 의 retention engine + LiveOps telemetry loop.
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## 매 핵심
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### 매 핵심 component
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- **Faucets** (currency 의 source): quest reward, daily login, drop, refund.
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- **Sinks** (currency 의 destruction): repair cost, upgrade fee, gacha pull, marketplace tax.
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- **Currencies**: soft (gold, earned) / hard (gems, paid) / event (limited).
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- **Items**: consumable / equipment / cosmetic / progression-gated.
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### 매 monetization model
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- **F2P + IAP**: gacha, battle pass, cosmetic shop.
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- **Premium + DLC**: one-time purchase + expansion.
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- **Subscription**: monthly stipend + perks (FFXIV, WoW).
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- **Ad-supported**: rewarded video, interstitial.
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- **Web3 / play-to-earn (decline 2024-)**: NFT, token economy — sustainability issue.
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### 매 응용
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1. Genshin Impact 의 Primogem dual-currency + 5-star pity (90 pull).
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2. Path of Exile 의 currency-as-crafting (no fixed gold) — Chaos Orb deflation.
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3. EVE Online 의 player-driven economy — ISK + PLEX 의 RMT bridge.
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4. Fortnite 의 V-Bucks + battle pass + item shop rotation.
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## 💻 패턴
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### 1. Faucet/Sink ledger
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```python
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from dataclasses import dataclass
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from collections import defaultdict
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@dataclass
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class Transaction:
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user_id: str
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currency: str
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amount: int # positive = faucet, negative = sink
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source: str # "quest:daily", "sink:repair", "shop:gacha"
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timestamp: float
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def analyze_economy(txs: list[Transaction], window_days=7):
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by_currency = defaultdict(lambda: {'faucet': 0, 'sink': 0})
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for tx in txs:
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key = 'faucet' if tx.amount > 0 else 'sink'
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by_currency[tx.currency][key] += abs(tx.amount)
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for cur, d in by_currency.items():
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net = d['faucet'] - d['sink']
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ratio = d['sink'] / max(d['faucet'], 1)
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print(f"{cur}: net={net}, sink/faucet={ratio:.2%}")
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# Healthy: ratio in [0.85, 1.05]
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```
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### 2. Gacha pity system
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```python
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class GachaPity:
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def __init__(self, soft_pity=75, hard_pity=90, base_rate=0.006):
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self.soft = soft_pity
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self.hard = hard_pity
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self.base = base_rate
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self.counter = 0
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def pull(self) -> bool:
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self.counter += 1
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if self.counter >= self.hard:
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self.counter = 0
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return True
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rate = self.base
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if self.counter >= self.soft:
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# Linear ramp: 0.6% → 100% over [75, 90]
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rate += (self.counter - self.soft) * (1 - self.base) / (self.hard - self.soft)
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if random.random() < rate:
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self.counter = 0
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return True
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return False
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# Expected pulls to 5-star ≈ 62 (Genshin verified)
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```
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### 3. Battle pass progression
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```typescript
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type BattlePass = {
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tiers: 100;
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xpPerTier: 1000;
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weeklyXpCap: number; // anti-burnout
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premiumPrice: 950; // Primogem-equivalent
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rewardValue: number; // total reward equiv-currency
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};
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// Healthy ratio: rewardValue / premiumPrice ∈ [1.5, 3.0]
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// Engagement: median user 의 50-70% completion in 70-day cycle.
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```
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### 4. Inflation simulation
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```python
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import numpy as np
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def simulate_inflation(faucet_rate, sink_rate, initial_supply, days=365):
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supply = [initial_supply]
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for d in range(days):
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new = supply[-1] + faucet_rate - sink_rate
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supply.append(max(0, new))
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# CPI-equivalent: track price of standard basket
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return np.array(supply)
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# Target: weekly inflation < 2%, annual < 50%
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```
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### 5. Whale vs F2P parity check
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```python
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def progression_gap(whale_spend_usd, f2p_hours):
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whale_progress = whale_spend_usd * SPEND_TO_PROGRESS # currency conversion
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f2p_progress = f2p_hours * GRIND_RATE
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return whale_progress / max(f2p_progress, 1)
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# Healthy: ratio < 5x for "fair F2P"; ratio > 50x → P2W backlash
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```
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### 6. Marketplace tax (sink design)
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```python
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def list_item(seller_id, item, price):
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listing_fee = price * 0.05 # 5% upfront sink
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deduct_currency(seller_id, listing_fee)
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create_listing(item, price)
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def buy_item(buyer_id, listing):
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market_tax = listing.price * 0.10 # 10% sink on sale
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seller_receives = listing.price - market_tax
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transfer(buyer_id, listing.seller, seller_receives)
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burn_currency(market_tax) # explicit sink
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```
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### 7. Dynamic pricing (event-aware)
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```python
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def event_price(base_price, demand_multiplier, scarcity):
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# demand from telemetry; scarcity 0..1
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return round(base_price * demand_multiplier * (1 + scarcity))
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# Limited-time skin: scarcity=1 → 2x base price acceptable
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```
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### 8. Cohort LTV calculation
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```python
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def cohort_ltv(cohort_users, days=180):
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revenue = sum(u.total_spend(days) for u in cohort_users)
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return revenue / len(cohort_users)
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# Segments: whale (>$100/mo), dolphin ($10-100), minnow (<$10), F2P ($0)
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# Healthy mobile F2P: top 1% generate 50%+ revenue (Pareto)
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```
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### 9. Engagement-monetization correlation
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```python
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import scipy.stats as stats
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# Avoid extracting from disengaged users
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session_minutes = [...]
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spend_usd = [...]
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r, p = stats.pearsonr(session_minutes, spend_usd)
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# Healthy: r > 0.3 (engagement → spend), not r < 0 (whale-only burn)
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Mobile F2P | Dual-currency + gacha + battle pass + soft pity |
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| MMO | Marketplace + crafting sinks + subscription |
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| Premium PC | Cosmetic shop + DLC, no gacha |
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| Competitive esport | No P2W, cosmetic-only monetization |
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| Live service | LiveOps event cycle + battle pass + limited shop |
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| Web3 (caution) | Dual-token + sustainable yield (rare success) |
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**기본값**: dual-currency + 70-day battle pass + 90-pull pity + 5-10% marketplace tax sink + telemetry-driven LiveOps.
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## 🔗 Graph
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- 부모: [[Game_Design]] · [[Monetization]]
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- 응용: [[Game_Balancing]] · [[LiveOps]] · [[Player_Retention]]
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- Adjacent: [[Behavioral_Economics]] · [[Pareto_Distribution]]
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## 🤖 LLM 활용
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**언제**: gacha rate sheet 설계 검토, sink/faucet ledger anomaly detection, balance hypothesis brainstorming.
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**언제 X**: 최종 monetization decision (regulatory + ethical 의 designer judgment), 미성년자 gacha disclosure (법적 의무).
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## ❌ 안티패턴
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- **Pay-to-win** (competitive): F2P churn + whale-only retention 의 short-lived.
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- **Inflation neglect**: faucet > sink 의 무관심 → currency 의 worthless.
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- **Hidden gacha rates**: 법적 risk (China, Korea 의 mandated disclosure).
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- **No spending limits**: 미성년자 protection 의 lack — refund + lawsuit.
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- **Predatory FOMO**: limited-time + dark pattern 의 abuse — brand damage.
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- **Currency proliferation**: 5+ currency 의 player confusion.
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- **No data telemetry**: economy 의 blind tuning — disaster.
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## 🧪 검증 / 중복
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- Verified (GDC talks 2022-2025, Riot/miHoYo/Supercell economy postmortems, Deconstructor of Fun analyses).
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- 신뢰도 A.
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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 — game economy design full content with patterns |
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