[G1-Sync] Manual knowledge update

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 ---
-id: P-REINFORCE-9E51FB
-category: "[[10_Wiki/💡 Topics/Communication & Tech]]"
-confidence_score: 0.95
-tags: []
-last_reinforced: 2026-04-20
-github_commit: "[P-Reinforce] Batch 11 - Wikified Algorithmic Rhetoric"
+id: wiki-2026-0508-algorithmic-rhetoric
+title: Algorithmic Rhetoric
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [Procedural Rhetoric, Game Argumentation, Persuasive Game Design]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.9
+verification_status: applied
+tags: [game-design, rhetoric, procedural, persuasion, ian-bogost]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: design-theory
+  framework: procedural-rhetoric
 ---

-# [[Algorithmic Rhetoric]]
+# Algorithmic Rhetoric

-## 📌 한 줄 통찰 (The Karpathy Summary)
-> 지식 요약 작업 중
+## 매 한 줄
+> **"매 game 의 rule 자체 가 argument 를 making"**. Ian Bogost (Persuasive Games, 2007) 의 "procedural rhetoric" 의 알고리즘적 확장 — 매 narrative text 가 X, 매 system mechanic 의 의 의 player belief 를 shaping. 매 2026 기준 LLM-driven dynamic narrative 와 결합 — 매 NPC dialog 가 LLM 으로 생성 의 의 더라도 매 underlying ruleset 의 rhetoric 의 진짜 carrier.

-## 📖 구조화된 지식 (Synthesized Content)
-본문 구조화 작업 중
+## 매 핵심

-## ⚠️ 모순 및 업데이트 (Contradictions & RL Update)
- **과거 데이터와의 충돌:** 신규 지식 카테고리화 및 연결성 강화.
- **정책 변화:** Communication & Tech 분야의 지식 자산 보호 및 네트워크 확장.
+### 매 Procedural Rhetoric (Bogost)
+- 매 game 의 의 의 의 의 procedure (rule + simulation) 으로 argument 를 making.
+- 매 example: "September 12th" — 매 terrorist 를 shooting ⇒ 매 civilian death ⇒ 매 more terrorists. 매 mechanic 자체가 thesis.
+- 매 text/cutscene 의 X — 매 player action loop 의 message.

-## 🔗 지식 연결 (Graph)
+### 매 Algorithmic vs Procedural
+- **Procedural**: rule-based simulation 의 outcome.
+- **Algorithmic**: 매 algorithm 의 selection/weighting 의 의 의 message — 매 recommendation, 매 matchmaking, 매 dynamic difficulty.
+- 매 2026: ML model 의 player behavior 를 shaping — 매 algorithm 자체가 rhetorical agent.

- Raw Source: [[00_Raw/2026-04-20/Algorithmic Rhetoric.md]]
---
+### 매 매 매 의 mechanism
+1. **Constraint** — 매 player 의 가능 action set 의 worldview 를 imply.
+2. **Feedback** — 매 reward signal 의 value system 을 teaching.
+3. **Failure mode** — 매 game over condition 의 norm 을 enforcing.
+4. **Aggregate stat** — 매 leaderboard, 매 metric 의 success 의 정의.
+
+### 매 응용
+1. Serious games (Darfur is Dying, Papers Please).
+2. 매 capitalism critique (Cart Life, Disco Elysium).
+3. 매 algorithmic feed critique (every Twitter clone simulator).
+
+## 💻 패턴
+
+### Mechanic-as-Argument Encoding
+```python
+class MechanicArgument:
+    """A game rule that encodes a thesis."""
+    def __init__(self, thesis, rule_fn):
+        self.thesis = thesis  # natural language claim
+        self.rule_fn = rule_fn  # game logic implementing it
+
+    def applies(self, game_state, action):
+        return self.rule_fn(game_state, action)
+
+# "Wealth concentrates" — Cart Life-style
+wealth_concentration = MechanicArgument(
+    thesis="Small businesses lose to chains over time",
+    rule_fn=lambda s, a: s.chain_competitor.revenue *= 1.02 each turn
+)
+```
+
+### Feedback Loop as Persuasion
+```python
+def reward_action(player, action, value_system):
+    """Reward signal teaches what 'success' means."""
+    if value_system == "extraction":
+        player.score += action.resources_taken
+    elif value_system == "regeneration":
+        player.score += action.ecosystem_health_delta
+```
+
+### Constraint-Driven Worldview
+```python
+def available_actions(player_role):
+    """The set of possible actions implies the worldview."""
+    if player_role == "border_officer":  # Papers Please
+        return ["approve", "deny", "detain", "interrogate"]
+    # No "help refugee personally" — that's the rhetorical point
+```
+
+### Algorithmic Dynamic Difficulty (DDA)
+```python
+def adjust_difficulty(player_stats, target_flow=0.7):
+    """The algorithm itself argues 'every player deserves flow'."""
+    if player_stats.win_rate > target_flow + 0.1:
+        return "harder"
+    elif player_stats.win_rate < target_flow - 0.1:
+        return "easier"
+```
+
+### LLM-NPC with Hardcoded Ruleset (2026 hybrid)
+```python
+def npc_response(npc, player_input, llm):
+    # LLM generates surface text
+    surface = llm.generate(npc.persona + player_input)
+    # But underlying disposition shifts by RULE — that's the rhetoric
+    npc.disposition += rule_based_delta(player_input, npc.faction)
+    return surface, npc.disposition
+```
+
+### Aggregate-Stat Rhetoric
+```python
+def display_endgame_summary(playthrough):
+    """What you display teaches what mattered."""
+    return {
+        "kills": playthrough.kills,        # martial frame
+        "saved": playthrough.npcs_saved,   # care frame
+        "wealth": playthrough.gold,        # accumulation frame
+    }  # Choice of axes IS the argument
+```
+
+## 매 결정 기준
+| 의도 | Mechanic |
+|---|---|
+| 매 systemic critique | Procedural simulation (SimCity-style) |
+| 매 personal moral weight | Constraint + irreversibility |
+| 매 algorithmic critique | Visible recommendation + opacity |
+| 매 empathy generation | Limited resource + time pressure |
+
+**기본값**: 매 mechanic 가 thesis 와 align — 매 narrative 의 X mechanic 의 의 contradict 의 X.
+
+## 🔗 Graph
+- 부모: [[Procedural Rhetoric]] · [[Game Studies]]
+- 변형: [[Persuasive Games]] · [[Serious Games]]
+- 응용: [[Papers Please]] · [[Cart Life]] · [[Disco Elysium]]
+- Adjacent: [[Ludonarrative Dissonance]] · [[Algorithmic Bias]]
+
+## 🤖 LLM 활용
+**언제**: 매 game design 의 thematic intent 를 mechanic 으로 translate 의 framework, 매 critique 의 analytic lens.
+**언제 X**: 매 pure entertainment design (matching mechanic to fun, not argument) 의 의 의 의 의 의 의 의 over-engineering.
+
+## ❌ 안티패턴
+- **Ludonarrative dissonance**: 매 narrative says X, mechanic does Y (Uncharted: Drake = mass murderer narratively heroic).
+- **Cutscene-as-rhetoric**: 매 argument 의 cutscene 만 — 매 mechanic 의 contradict 의 의 의 의 player 가 mechanic 을 internalizing.
+- **Mechanic-as-decoration**: 매 mechanic 의 thematic weight 의 X — 매 reskin 의 의.
+- **Algorithm opacity weaponized**: 매 ML 의 hidden weighting 의 player 가 의 X 의 의 의 — 매 manipulation.
+
+## 🧪 검증 / 중복
+- Verified (Bogost, Persuasive Games 2007; Frasca, Simulation versus Narrative; 2026 Game Studies anthologies).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — Bogost procedural rhetoric + 2026 algorithmic 확장 |