docs(10_Wiki): Topic_Business/General/Graphic/Programming을 Topics/ 하위로 이동

최상위 10_Wiki/Topic_*였던 4개 카테고리 폴더를 10_Wiki/Topics/Topic_* 로 재배치. 콘텐츠 변경 없음(순수 폴더 이동) — Topics/ 하위 나머지 폴더는 이미 지난 커밋에서 전부 정리된 상태(잔존 항목은 에이전트 운영 상태 및 사용자가 보존을 요청한 업데이트0615/무제 3.canvas 뿐).
2026-07-05 00:44:01 +09:00
parent e9cbf23ab5
commit 9a135bd19d
6127 changed files with 0 additions and 0 deletions
@@ -0,0 +1,140 @@
+---
+id: wiki-2026-0508-mckinsey-problem-solving-test-ps
+title: McKinsey Problem Solving Test (PST)
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [McKinsey PST, McKinsey Solve, Imbellus]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.9
+verification_status: applied
+tags: [game-design, assessment, gamification, business-strategy, recruitment]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: assessment-design
+  framework: simulation-based-testing
+---
+
+# McKinsey Problem Solving Test (PST)
+
+## 매 한 줄
+> **"매 paper case 의 from → 매 ecosystem simulation game 의 to"**. McKinsey PST 매 originally 60-min paper-based business case test, 2019 매 Imbellus acquisition (now McKinsey Solve) 의 후 매 game-based assessment 의 transition. 매 60-min 의 동안 매 ecosystem-building, redrock-defense, plant-defense scenarios 의 candidate cognitive load + decision pattern 의 measure.
+
+## 매 핵심
+
+### 매 Legacy PST (pre-2019)
+- 매 26 multiple-choice 의 over 60 min — 매 reading + math + logic.
+- 매 case-study format — exhibits, tables, charts 의 analyze.
+- 매 ~70% pass threshold (region-dependent).
+
+### 매 Solve (current, post-Imbellus)
+- **Ecosystem game**: 매 species + terrain 의 select 매 sustainable food chain 의 build.
+- **Redrock study**: 매 disease modeling — natural reserves 의 protect.
+- **Plant defense**: 매 invasive species 의 against 매 strategy 의 deploy.
+- 매 evaluation 매 outcome 만 X — 매 process telemetry (clicks, hesitations, revisions) 의 weighted.
+
+### 매 What's measured (Solve)
+1. **Critical thinking** — 매 incomplete data 의 from inference.
+2. **Decision-making** — 매 trade-off navigation under time pressure.
+3. **Metacognition** — 매 self-correction patterns.
+4. **Situational awareness** — 매 emergent system constraints 의 grasp.
+
+## 💻 패턴
+
+### Ecosystem builder logic (simplified)
+```typescript
+interface Species { id: string; calories: number; eats: string[]; eatenBy: string[]; }
+interface Terrain { temp: number; elevation: number; rainfall: number; }
+
+function isViable(species: Species[], terrain: Terrain): boolean {
+  // 매 8-species ecosystem 의 valid 한 food chain 의 form
+  const producers = species.filter(s => s.eats.length === 0);
+  if (producers.length < 1) return false;
+  const apex = species.filter(s => s.eatenBy.length === 0);
+  if (apex.length !== 1) return false;
+  return checkCalorieBalance(species) && checkTerrainFit(species, terrain);
+}
+```
+
+### Redrock disease propagation
+```typescript
+// SIR model 의 simplified form 의 candidate 의 infer
+class DiseaseModel {
+  constructor(public beta: number, public gamma: number) {}
+
+  step(s: number, i: number, r: number): [number, number, number] {
+    const newInfections = this.beta * s * i;
+    const recoveries = this.gamma * i;
+    return [s - newInfections, i + newInfections - recoveries, r + recoveries];
+  }
+}
+```
+
+### Process telemetry (Imbellus angle)
+```typescript
+interface Action { ts: number; type: 'select' | 'place' | 'undo' | 'submit'; payload: unknown; }
+
+function metacognitionScore(actions: Action[]): number {
+  const undos = actions.filter(a => a.type === 'undo').length;
+  const submits = actions.filter(a => a.type === 'submit').length;
+  // 매 healthy revision pattern: 매 some undos 매 zero 또는 too many 매 X
+  return 1 - Math.abs((undos / Math.max(1, submits)) - 0.3);
+}
+```
+
+### Time-pressure decision quality
+```typescript
+function decisionQualityCurve(timeSpent: number, optimalMs: number): number {
+  // 매 too fast 의 reckless, 매 too slow 의 indecisive
+  const ratio = timeSpent / optimalMs;
+  return Math.exp(-Math.pow(Math.log(ratio), 2));
+}
+```
+
+### Cohort calibration
+```sql
+-- 매 candidate 의 raw score 의 against cohort 의 percentile
+SELECT
+  candidate_id,
+  raw_score,
+  PERCENT_RANK() OVER (PARTITION BY test_window ORDER BY raw_score) AS percentile
+FROM solve_results
+WHERE test_window = '2026-Q2';
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| Pre-2019 candidate | Legacy PST format prep |
+| Post-2019 candidate | Solve game-based prep |
+| Hybrid markets | 매 firm communication 의 verify (some still use legacy) |
+| Game design 의 reference | 매 Solve 의 process-as-signal pattern 의 study |
+
+**기본값**: 매 2026 candidate 매 Solve 의 expect — process telemetry 매 outcome 의 못지않게 weighted.
+
+## 🔗 Graph
+- 변형: [[Imbellus]]
+- Adjacent: [[Algorithmic Rhetoric]] · [[Data-Driven Personalization]]
+
+## 🤖 LLM 활용
+**언제**: Practice case generation, decision rationale review, reasoning pattern feedback.
+**언제 X**: Live test attempt (prohibited + detected), specific Solve scenario predictions.
+
+## ❌ 안티패턴
+- **Outcome-only optimization**: 매 process telemetry 매 ignore 매 Solve era 매 fail.
+- **Speed-running**: 매 reckless click pattern 매 metacognition score 의 destroy.
+- **Memorization**: 매 Solve 매 randomized — 매 brute memorization 매 ineffective.
+- **Legacy prep only**: 매 most firms 매 game-based 의 transitioned 의 ignore.
+
+## 🧪 검증 / 중복
+- Verified (McKinsey official 2024-2025 communications, Management Consulted, IGotAnOffer guides, Imbellus design papers).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — Legacy PST → Solve transition, Imbellus telemetry, prep patterns |