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

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-mckinsey-problem-solving-test-ps	McKinsey Problem Solving Test (PST)	10_Wiki/Topics	verified	self	McKinsey PST McKinsey Solve Imbellus	none	A	0.9	applied	game-design assessment gamification business-strategy recruitment		2026-05-10	pending	language framework assessment-design 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)

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

// 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)

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

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

-- 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