refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-machinations
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title: Machinations
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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: [Machinations Diagram, Game Economy Modeling]
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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-modeling, simulation, balancing]
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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: machinations
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---
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# Machinations
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## 매 한 줄
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> **"매 game economy 의 visual flow simulation"**. Joris Dormans 의 PhD work (2009→) — node + connection 의 graph 로 매 resource flow, feedback loop, random event 를 simulate. 2026 의 Machinations.io SaaS + Live integration 으로 매 Unity/Unreal economy 를 매 design-time 에 balance 가능. F2P, gacha, idle 게임의 매 standard tool.
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## 매 핵심
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### 매 핵심 nodes
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- **Pool**: resource 저장 (gold, energy, XP).
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- **Source**: resource 생성.
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- **Drain**: resource 소비.
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- **Converter**: resource 변환 (예: 5 wood → 1 plank).
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- **Trader**: bidirectional exchange.
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- **Gate**: 매 conditional flow router.
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- **End condition**: simulation 종료 trigger.
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### 매 connection types
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- **Resource connection**: rate (per tick) + label.
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- **State connection**: source pool 의 value 가 매 다른 node 의 rate modifier.
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- **Trigger**: event-based fire.
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- **Activator**: enable/disable based on threshold.
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### 매 simulation modes
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- **Deterministic**: fixed rates → predictable.
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- **Stochastic**: random with distribution → Monte Carlo.
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- **Interactive**: player input 의 click on Source/Drain.
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### 매 응용
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1. F2P economy balancing (gacha rates, soft/hard currency).
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2. Idle game progression curves.
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3. RTS resource gathering loop.
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4. Battle-pass / season pass tuning.
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5. Player retention model (LTV simulation).
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6. Educational economic modeling (supply/demand).
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## 💻 패턴
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### 1. Idle game core loop (Machinations-style pseudo)
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```
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[Source: idle] --1/sec--> [Pool: Gold]
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[Pool: Gold] --click 10/click--> [Drain: Spend]
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[Pool: Gold] --on >= 100--> [Converter: BuyUpgrade]
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[Converter] --1--> [Pool: Multiplier]
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[Pool: Multiplier] -- state * 0.5 --> [Source: idle].rate
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# Feedback loop: more multiplier → faster gold → more upgrades
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```
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### 2. JS simulation harness (build your own)
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```typescript
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type Pool = { name: string; value: number };
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type Edge = {
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from: string;
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to: string;
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rate: (state: Record<string, number>) => number;
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};
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class Simulator {
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pools: Pool[] = [];
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edges: Edge[] = [];
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tick = 0;
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step() {
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const state = Object.fromEntries(this.pools.map(p => [p.name, p.value]));
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const deltas: Record<string, number> = {};
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for (const e of this.edges) {
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const r = e.rate(state);
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deltas[e.from] = (deltas[e.from] ?? 0) - r;
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deltas[e.to] = (deltas[e.to] ?? 0) + r;
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}
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for (const p of this.pools) p.value = Math.max(0, p.value + (deltas[p.name] ?? 0));
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this.tick++;
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}
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}
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const sim = new Simulator();
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sim.pools = [{ name: "gold", value: 0 }, { name: "mult", value: 1 }];
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sim.edges = [
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{ from: "_source", to: "gold", rate: s => 1 * s.mult },
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{ from: "gold", to: "mult", rate: s => s.gold >= 100 ? 100 : 0 }, // buy upgrade
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];
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```
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### 3. Monte Carlo balance test
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```typescript
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function runMontecarlo(n = 1000) {
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const completionTimes: number[] = [];
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for (let i = 0; i < n; i++) {
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const sim = buildEconomy({ seed: i });
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while (!sim.done && sim.tick < 100_000) sim.step();
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completionTimes.push(sim.tick);
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}
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completionTimes.sort((a, b) => a - b);
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return {
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p50: completionTimes[Math.floor(n * 0.5)],
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p95: completionTimes[Math.floor(n * 0.95)],
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p99: completionTimes[Math.floor(n * 0.99)],
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};
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}
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// Use to validate "median player reaches L20 in ~2 hours".
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```
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### 4. Gacha drop rate modeling
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```typescript
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type Rarity = "common" | "rare" | "sr" | "ssr";
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const rates: Record<Rarity, number> = {
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common: 0.79,
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rare: 0.17,
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sr: 0.035,
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ssr: 0.005, // 0.5%
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};
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function pull(): Rarity {
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const r = Math.random();
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let acc = 0;
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for (const [k, p] of Object.entries(rates) as [Rarity, number][]) {
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acc += p;
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if (r < acc) return k;
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}
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return "common";
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}
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function pityCounter() {
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let pulls = 0;
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return () => {
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pulls++;
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if (pulls >= 90) { pulls = 0; return "ssr" as Rarity; }
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return pull();
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};
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}
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// Simulate 10k players → expected SSR per player + variance.
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```
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### 5. Feedback loop classification
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```
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Positive feedback: A → ... → A+ (rich-get-richer, snowball)
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Negative feedback: A → ... → A- (rubber-banding, balance)
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Multiplayer dynamics: Mario Kart blue shell (negative for leader)
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```
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```typescript
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// Detect runaway: if d(pool)/dt grows super-linearly → rebalance
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function detectRunaway(history: number[]): boolean {
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const ratios = history.slice(1).map((v, i) => v / Math.max(history[i], 1));
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return ratios.slice(-10).every(r => r > 1.1); // 10% growth/tick sustained
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}
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```
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### 6. Progression pacing curve
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```typescript
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// Target: L = sqrt(XP / 50) → quadratic XP curve
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function xpForLevel(L: number) { return 50 * L * L; }
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// Sim: drop rate × kill rate × time = level after T
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function levelAtTime(t: number, xpPerSec = 2) {
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const totalXP = xpPerSec * t;
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return Math.floor(Math.sqrt(totalXP / 50));
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}
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// Plot levelAtTime over 0..7200 (2h) and check pacing.
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```
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### 7. Machinations.io live data hook (2026)
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```typescript
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// Fetch live economy params from Machinations.io
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// (designers tune in browser; game pulls latest)
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async function fetchEconomyConfig(diagramId: string) {
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const r = await fetch(`https://api.machinations.io/v2/diagrams/${diagramId}/config`, {
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headers: { Authorization: `Bearer ${process.env.MACH_TOKEN}` },
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});
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return r.json(); // { goldPerSec, ssRate, levelCurve, ... }
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}
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// Update server tunables without redeploy.
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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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| F2P economy balance | Machinations.io diagram + Monte Carlo. |
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| Idle / incremental | Custom JS simulator (lighter). |
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| MMO economy stability | Agent-based simulation (more nodes). |
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| Single-player RPG progression | Spreadsheet + simple sim. |
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| Real-time multiplayer balance | Live telemetry + A/B + small Machinations diagram. |
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| PVP balance (rock-paper-scissors) | Game theory tool, not Machinations. |
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**기본값**: 매 currency-flow heavy game 의 design phase 에 매 Machinations diagram. Pure combat balance 는 매 다른 tool.
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## 🔗 Graph
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- 부모: [[Game Design]]
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- Adjacent: [[Monte Carlo Simulation]] · [[Feedback Loops]]
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## 🤖 LLM 활용
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**언제**: economy diagram review, drop rate sanity check, progression curve drafting, feedback loop identification.
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**언제 X**: 매 combat balance, 매 narrative pacing, 매 art/UX concerns.
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## ❌ 안티패턴
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- **Diagram only, no simulation**: 매 visual 만 그리고 매 numeric Monte Carlo 의 X. 매 deterministic only 는 stochastic player 의 outlier 을 miss.
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- **Over-detailed model**: 매 50+ nodes — designer 가 매 mental model 잃음. 매 module 단위 분리.
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- **Static rates in live game**: 매 Machinations 에서 balanced 후 hardcode. 매 LiveOps tunable 로 expose.
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- **Ignoring positive feedback**: 매 snowball loop 의 매 rich-get-richer — 매 churn driver.
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- **Single-seed simulation**: 매 1 run 으로 결정 — 매 N=1000+ Monte Carlo 필수.
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## 🧪 검증 / 중복
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- Verified (Dormans, *Engineering Emergence: Applied Theory of Game Design* PhD thesis 2012, Machinations.io docs).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — Machinations primitives + Monte Carlo + gacha/idle pattern |
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