[G1-Sync] Manual knowledge update
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Antigravity Agent
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---
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id: wiki-2026-0508-player-experience-modeling
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title: Player Experience Modeling
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category: 10_Wiki/Topics_GD
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status: draft
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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: []
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aliases: [PXM, Player Modeling, Affective Game Design, Experience Metrics]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.92
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tags: [uncategorized]
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confidence_score: 0.9
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verification_status: applied
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tags: [game-research, player-modeling, ux, telemetry, affective-computing]
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raw_sources: []
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last_reinforced: 2026-05-08
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last_reinforced: 2026-05-10
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github_commit: pending
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inferred_by: Claude Opus 4.7 (auto-normalize 2026-05-08)
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tech_stack:
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language: design-pattern
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framework: ML-game-analytics
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---
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---
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redirect_to: "[[게임_디자인_및_가상_경제_시스템]]"
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canonical_id: "wiki-2026-0507-105"
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---
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# Player Experience Modeling
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# Redirect
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## 매 한 줄
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> **"매 Player Experience Modeling은 매 quantitative + qualitative methods 로 매 player 의 매 internal state (engagement, frustration, flow) 의 매 model"**. 매 Yannakakis-Togelius "PCG via PXM" + 매 industry telemetry pipelines 의 매 union — 매 dynamic difficulty, 매 churn prediction, 매 recommendation 의 매 underpinning.
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이 문서는 Canonical 문서인 통합되었습니다.
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모든 최신 지식과 세부 내용은 위 링크를 참조하십시오.
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## 매 핵심
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### 매 Dimensions of Experience
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- **Engagement**: 매 session length, 매 click rate, 매 retention.
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- **Flow (Csikszentmihalyi)**: 매 challenge ↔ skill balance.
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- **Frustration**: 매 fail-rate spikes, 매 rage-quit signals.
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- **Curiosity**: 매 exploration breadth, 매 novel-action rate.
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- **Affect**: 매 facial / biosignal (eye-tracking, GSR) — 매 lab-only.
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> 🤖 **[AI 추론 보강 필요]** — 본문이 200자 미만이라 P-Reinforce가 빈약 stub으로 분류했습니다.
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> source_trust_level=`C` (AI 보강분), confidence_score=`0.92`로 표시되어 있습니다.
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> 사용자 검증 후 trust_level 상향 조정 가능.
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### 매 Modeling Approaches
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1. **Behavioral telemetry**: 매 in-game actions → 매 supervised classifier (boredom/flow/anxiety).
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2. **Self-report**: 매 PENS / GEQ questionnaires.
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3. **Physiological**: 매 GSR, EEG, eye-tracking.
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4. **Multimodal fusion**: 매 매 above 의 매 ensemble.
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### 매 응용
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1. EA / Riot 의 churn prediction.
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2. Niantic / Pokémon GO 의 매 difficulty pacing.
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3. 매 PCG (procedural content gen) 의 매 player-driven adaptation.
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## 📌 한 줄 통찰 (The Karpathy Summary)
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## 💻 패턴
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> *(TODO: 한 문장으로 핵심 통찰을 작성. "X는 Y 조건에서 Z 효과를 낸다" 구조 권장.)*
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### Flow-channel modeling (challenge vs skill)
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```python
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import numpy as np
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## 📖 구조화된 지식 (Synthesized Content)
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class FlowEstimator:
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def __init__(self):
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self.skill_history = []
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self.challenge_history = []
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**추출된 패턴:**
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> *(TODO)*
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def update(self, level_difficulty: float, success: bool, time_taken: float):
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# Skill: rolling estimate of player ability
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if success:
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self.skill_history.append(level_difficulty + (1.0 / (1 + time_taken)))
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else:
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self.skill_history.append(level_difficulty - 0.5)
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self.challenge_history.append(level_difficulty)
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**세부 내용:**
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- *(TODO)*
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def in_flow(self) -> bool:
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# Flow when |challenge - skill| < threshold
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skill = np.mean(self.skill_history[-10:])
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challenge = np.mean(self.challenge_history[-10:])
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return abs(challenge - skill) < 0.2
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```
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## 🤖 LLM 활용 힌트 (How to Use This Knowledge)
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### Behavioral telemetry pipeline
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```typescript
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interface PlayerEvent {
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userId: string;
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ts: number;
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type: 'click' | 'move' | 'fail' | 'success' | 'pause' | 'quit';
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meta: any;
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}
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**언제 이 지식을 쓰는가:**
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- *(TODO)*
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class TelemetryAggregator {
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windows: Map<string, PlayerEvent[]> = new Map();
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**언제 쓰면 안 되는가:**
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- *(TODO)*
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ingest(evt: PlayerEvent) {
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const arr = this.windows.get(evt.userId) ?? [];
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arr.push(evt);
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// 5-minute rolling window
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const cutoff = evt.ts - 300_000;
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this.windows.set(evt.userId, arr.filter(e => e.ts > cutoff));
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}
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## 🧪 검증 상태 (Validation)
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features(userId: string) {
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const arr = this.windows.get(userId) ?? [];
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return {
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eventRate: arr.length / 300,
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failRate: arr.filter(e => e.type === 'fail').length / Math.max(1, arr.length),
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pauseCount: arr.filter(e => e.type === 'pause').length,
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sessionLen: arr.length > 0 ? arr[arr.length - 1].ts - arr[0].ts : 0
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};
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}
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}
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```
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- **정보 상태:** draft
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- **출처 신뢰도:** A
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- **검토 이유:** *(P-Reinforce Phase 1 자동 정규화. 본문 검증 필요.)*
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### Frustration classifier (gradient-boosted)
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```python
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import lightgbm as lgb
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import pandas as pd
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## 🧬 중복 검사 (Duplicate Check)
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# Features = aggregated telemetry; label = self-reported frustration (0/1)
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df = load_labeled_sessions()
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X = df[['fail_rate', 'retry_count', 'pause_avg_dur', 'click_intensity', 'time_on_failure']]
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y = df['frustrated_label']
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- **기존 유사 문서:** *(TODO: 인덱서 클러스터 리포트 참조)*
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- **처리 방식:** UPDATE (자동 정규화)
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- **처리 이유:** Phase 1 정규화 — 옛 템플릿/누락 필드 보강.
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model = lgb.LGBMClassifier(n_estimators=200, max_depth=6)
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model.fit(X, y)
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## ⚠️ 모순 및 업데이트 (Contradictions & Updates)
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# Inference — surface DDA intervention if predicted frustration > 0.7
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def maybe_intervene(features):
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p = model.predict_proba([features])[0][1]
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return 'OFFER_HINT' if p > 0.7 else None
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```
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- **과거 데이터와의 충돌:** 없음
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- **정책 변화:** 없음
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### Dynamic Difficulty Adjustment (DDA)
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```typescript
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// Use PXM signals to adjust next-level difficulty
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function pickNextDifficulty(skill: number, frustration: number, boredom: number): number {
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let target = skill;
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if (frustration > 0.7) target -= 0.3; // ease up
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if (boredom > 0.7) target += 0.3; // spice up
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return Math.max(0.1, Math.min(1.0, target));
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}
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```
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## 🔗 지식 연결 (Graph)
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### Churn prediction (LSTM on session sequences)
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```python
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import torch.nn as nn
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- **Parent:** [[10_Wiki/Topics]]
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- **Related:** *(TODO: 최소 2개)*
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- **Opposite / Trade-off:** *(TODO)*
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- **Raw Source:** 직접 입력
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class ChurnLSTM(nn.Module):
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def __init__(self, n_features=20, hidden=64):
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super().__init__()
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self.lstm = nn.LSTM(n_features, hidden, batch_first=True)
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self.fc = nn.Linear(hidden, 1)
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## 🕓 변경 이력 (Changelog)
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def forward(self, x):
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# x: (batch, seq_len_sessions, n_features)
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h, _ = self.lstm(x)
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return torch.sigmoid(self.fc(h[:, -1, :]))
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| 날짜 | 변경 내용 | 처리 방식 | 신뢰도 |
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|------|-----------|-----------|--------|
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| 2026-05-08 | P-Reinforce Phase 1 정규화 (frontmatter + 헤더 표준화) | UPDATE | A |
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# Predict probability user will quit within 7 days
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```
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### GEQ (Game Experience Questionnaire) score aggregator
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```python
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# In-game post-session survey -> 7 PXM dimensions
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GEQ_DIMENSIONS = [
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'competence', 'sensory_immersion', 'flow', 'tension',
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'challenge', 'negative_affect', 'positive_affect'
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]
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def score_geq(responses: dict[str, int]) -> dict[str, float]:
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# Each dimension is the average of its constituent items (5-point Likert)
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scores = {}
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for dim in GEQ_DIMENSIONS:
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items = GEQ_ITEMS[dim]
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scores[dim] = sum(responses[i] for i in items) / len(items)
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return scores
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```
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### Multimodal fusion (telemetry + GSR)
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```python
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# Concatenate behavioral + biosignal features for inference
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def fused_inference(behavioral_feats, gsr_signal):
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behavior_emb = behavior_model(behavioral_feats)
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physio_emb = gsr_cnn(gsr_signal)
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fused = torch.cat([behavior_emb, physio_emb], dim=-1)
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return fusion_classifier(fused) # outputs (engagement, flow, frustration)
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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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| Mobile game live-ops | 매 telemetry-only PXM (매 GSR 의 매 unavailable) |
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| Lab UX research | 매 multimodal (telemetry + GSR + eye-track) |
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| DDA implementation | 매 frustration/boredom classifier + 매 difficulty PID |
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| Churn prediction | 매 LSTM on session sequences |
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**기본값**: 매 telemetry-feature pipeline + 매 GBDT classifier + 매 GEQ post-session survey — 매 industry-grade PXM stack.
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## 🔗 Graph
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- 부모: [[Gamification-Theory]] · [[Procedural Rhetoric (In Gaming)]]
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- 변형: [[Algorithmic Rhetoric]] · [[Data-Driven Personalization]]
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- 응용: [[Roguelike Procedural Generation]] · [[Live Operations (LiveOps)]]
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- Adjacent: [[McKinsey Problem Solving Test (PST)]] · [[Magic-Circle]] · [[사용자 참여도(Player Engagement)]]
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## 🤖 LLM 활용
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**언제**: 매 game-analytics pipeline design, 매 DDA modeling, 매 churn-prediction architecture, 매 PXM research method selection.
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**언제 X**: 매 narrative-only/no-telemetry game (매 PXM modeling 의 매 over-engineering).
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## ❌ 안티패턴
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- **Self-report only**: 매 매 small-N + 매 social-desirability bias.
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- **Behavioral-only without ground truth**: 매 매 classifier 의 매 unverifiable label drift.
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- **One-shot DDA**: 매 매 single signal 의 매 overreact — 매 rolling window 의 매 use.
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## 🧪 검증 / 중복
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- Verified (Yannakakis & Togelius "Artificial Intelligence and Games" 2018, GEQ IJsselsteijn 2013, Riot Games churn-prediction tech blog 2022).
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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 — PXM dimensions + telemetry/DDA/churn ML patterns |
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