Antigravity Agent
173 lines
6.5 KiB
Markdown
173 lines
6.5 KiB
Markdown
---
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id: wiki-2026-0508-위험과-보상-risks-and-rewards
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title: 위험과 보상(Risks and Rewards)
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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: [Risk-Reward, R&R Curve, 리스크 리워드]
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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, risk-reward, decision-making]
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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: typescript
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framework: unity-csharp
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---
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# 위험과 보상(Risks and Rewards)
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## 매 한 줄
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> **"매 player choice 매 risk-reward tension 의 산물"**. 매 1980s arcade era (Pac-Man power pellet hunt, Galaga fighter capture) 부터 매 2026 modern roguelite (Hades heat system, Balatro stake escalation) 까지 매 동일한 design pillar 의 작동 — 매 player 에게 매 "더 큰 reward 의 위해 매 더 큰 risk 매 감수할 것인가?" 의 매 질문 의 제시.
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## 매 핵심
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### 매 R&R curve shape
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- **매 linear**: risk 2배 = reward 2배. 매 보장적 매 boring.
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- **매 convex (accelerating)**: risk 2배 = reward 4배+. 매 high-skill push 매 보상.
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- **매 concave (diminishing)**: risk 2배 = reward 1.3배. 매 conservative play 매 우대.
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- **매 step function**: threshold 매 도달 시 매 jump. 매 commitment 매 design.
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### 매 expected value (EV) framework
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- EV = Σ(outcome × probability)
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- 매 design goal: 매 EV(risky) > EV(safe) by ~10-20% 매. 매 너무 크면 매 risk 매 obvious choice. 매 너무 작으면 매 risk 매 trap.
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- 매 variance 매 player perception 의 영향 — 매 same EV 라도 매 high-variance 매 더 위험적 매 felt.
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### 매 응용
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1. 매 Hades heat: +1 heat = run 의 +X% 의 어려움, +Y% 의 reward bonus. 매 player chooses pace.
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2. 매 Balatro stake: ante 매 escalation curve 매 explicit risk dial.
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3. 매 Diablo 4 Pit tier: timer 매 압박 의 high-tier 매 push 시 매 huge loot.
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4. 매 PoE Atlas: map mods (more rare/magic) 매 increase difficulty + drop quality.
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5. 매 Tarkov raid: extract early (safe loot) vs hunt boss (rare items, PvP risk).
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## 💻 패턴
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### Linear vs convex reward curve
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```typescript
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// Linear: predictable, low excitement
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const linearReward = (risk: number) => 100 * risk;
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// Convex: high-skill players get exponential reward
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const convexReward = (risk: number) => 100 * Math.pow(risk, 1.6);
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// Concave: protect casual players from punishment-snowball
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const concaveReward = (risk: number) => 100 * Math.pow(risk, 0.7);
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// Step: clear "go/no-go" decisions
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const stepReward = (risk: number) =>
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risk < 0.3 ? 50 : risk < 0.7 ? 200 : 1000;
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```
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### Expected value calculator
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```typescript
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type Outcome = { value: number; probability: number };
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function expectedValue(outcomes: Outcome[]): number {
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return outcomes.reduce((sum, o) => sum + o.value * o.probability, 0);
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}
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// Design check: risky path should EV-dominate by 10-20%
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const safe: Outcome[] = [{ value: 100, probability: 1.0 }];
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const risky: Outcome[] = [
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{ value: 300, probability: 0.4 },
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{ value: 0, probability: 0.6 },
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];
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console.log(expectedValue(safe)); // 100
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console.log(expectedValue(risky)); // 120 — 20% premium for variance
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```
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### Variance-aware reward
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```typescript
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// Risk-averse player simulation: subtract σ * λ from EV
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function utility(outcomes: Outcome[], lambda = 0.3): number {
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const ev = expectedValue(outcomes);
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const variance = outcomes.reduce(
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(s, o) => s + o.probability * (o.value - ev) ** 2, 0
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);
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return ev - lambda * Math.sqrt(variance);
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}
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```
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### Hades heat system pattern
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```csharp
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public class HeatModifier {
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public string Name;
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public float DifficultyDelta; // +0.15 enemy HP
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public float RewardMultiplier; // +0.10 darkness
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}
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public class RunConfig {
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public List<HeatModifier> Active = new();
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public float TotalReward => 1f + Active.Sum(h => h.RewardMultiplier);
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public float TotalDifficulty => 1f + Active.Sum(h => h.DifficultyDelta);
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// Player picks which axis to dial: more enemies vs tougher boss vs less heal
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}
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```
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### Push-your-luck (Balatro/Slay the Spire elite)
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```typescript
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class PushYourLuck {
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pot = 0;
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rounds = 0;
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bustChance = 0.15;
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step() {
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this.rounds++;
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this.bustChance += 0.05; // escalating
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if (Math.random() < this.bustChance) return { result: "bust", payout: 0 };
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this.pot += 100 * Math.pow(1.4, this.rounds);
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return { result: "continue", pot: this.pot };
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}
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cashOut() { return { result: "cashed", payout: this.pot }; }
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}
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```
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### Asymmetric punishment (Tarkov-style)
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```typescript
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// Death = lose carried gear; success = keep + bonus
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function raidOutcome(survived: boolean, lootValue: number, gearValue: number) {
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return survived ? { net: lootValue } : { net: -gearValue };
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}
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// Design: gearValue ≈ 0.5 * expected lootValue (so EV stays positive but tense)
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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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| 매 player skill 매 wide range | concave curve (cap snowball) |
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| 매 high-skill audience (roguelike vet) | convex curve |
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| 매 binary commit decisions | step function |
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| 매 short session loop | step (clear payoff) |
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| 매 long session escalation | continuous + variance |
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**기본값**: 매 mildly convex (exponent ~1.3-1.5) + 매 EV premium 매 15% 매 risky path 의 매 적용.
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## 🔗 Graph
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- 부모: [[Game Economy]] · [[Decision Making]] · [[Player Motivation]]
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- 변형: [[위험과 보상 구조(Structures of Risks and Rewards)]] · [[Push-Your-Luck]]
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- 응용: [[Roguelike Run Modifiers]] · [[Loot Tier Curves]] · [[핀치 포인트(Pinch Point)]]
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- Adjacent: [[Flow Theory]] · [[Loss Aversion]] · [[Expected Value]]
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## 🤖 LLM 활용
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**언제**: 매 economy / progression / encounter 매 design 시 매 player choice tension 의 calibrate 시.
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**언제 X**: 매 narrative-only 매 walking sim (no choice stakes) — 매 R&R framework 매 misapplied.
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## ❌ 안티패턴
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- **매 risk without reward**: 매 die 시 매 lose progress, 매 win 시 매 nothing extra. 매 player 매 leave.
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- **매 reward without risk**: 매 free 의 grind 의 best gear. 매 boring.
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- **매 hidden EV**: 매 player 매 calculate 의 X. 매 trap design (slot machine illusion) — 매 ethical 의 X.
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- **매 binary cliff**: 매 EV jump 매 too sharp 매 → 매 only one viable path.
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## 🧪 검증 / 중복
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- Verified (Schell *Art of Game Design*, Adams *Fundamentals of Game Design*).
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- 매 modern roguelite 매 case studies (Hades, Balatro, Slay the Spire).
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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 — R&R curve types + EV framework + working code patterns |
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