2nd/10_Wiki/Topics/Game_Design/McKinsey Problem Solving Test (PST).md

---
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 |