Editing Openai/693b7cce-38f4-800d-92f7-5e56a467bfa7 (section)

== gpt-5-1 ==
=== User: Below is an academic peer-review & structured research draft responding to your innovative proposal for dynamic NPC per… ===
Below is an academic peer-review & structured research draft responding to your innovative proposal for dynamic NPC personalities integrating psychological, narrative, and common knowledge frameworks. I will reference real research where available (Pinker’s common knowledge, personality modeling in games, epistemic logic, etc.) to ground the ideas scientifically and conceptually. 

---

Dynamic NPC Personalities: A Computational Framework Integrating Narrative, Psychology, and Common Knowledge
# Introduction

Games increasingly demand NPCs with behaviors and personalities that feel alive and socially grounded. Static scripting and decision trees lack the adaptability and social depth players increasingly expect. This paper outlines an implementable AI architecture where:

NPCs have dynamic personalities that evolve in response to interactions.

Narrative roles (e.g., Hero’s Journey stages, dramatic situations) influence behavior.

Psychological models (Jungian archetypes, OCEAN/MBTI) inform personality vectors.

A common knowledge system ensures shared world understanding that shapes cognition and social coordination.

We propose an architecture combining epistemic logic, psychological representations, and narrative structures into NPC AI that produces emergent, socially plausible behavior.

---
# Theoretical Foundations

2.1 Narrative and Psychological Models

Hero’s Journey & Dramatic Situations
The Hero’s Journey is a widely used narrative schema with identifiable stages that shape character arcs. Dramatic situations (e.g., pursuit, sacrifice) guide conflict and decision contexts.

Jungian Archetypes
Archetypes represent prototypical personas (e.g., Hero, Mentor, Shadow). While Jung’s theory is conceptual and not empirically validated, it provides a useful taxonomy for narrative roles. (Psychology consensus: archetypes are metaphors rather than scientifically confirmed constructs.) 

Personality Models (MBTI / OCEAN)

MBTI offers categorical personality types widely used in games and design heuristics.

OCEAN/Big Five provides continuous trait measures correlated with real behavior. When integrated into NPC parameters, they allow fine-grained variation in responses (e.g., high Agreeableness → cooperative dialogue).

Empirical work has shown that models such as the Big Five can be used to influence game narrative generation and player experience. 

2.2 Common Knowledge in Multi-Agent Systems

Epistemic Logic and Common Knowledge
In logic/game theory, common knowledge means a fact is known by all, and each knows that all know it, recursively ad infinitum. This is distinct from mere shared or mutual knowledge where agents know something without knowing that others know it. 

Pinker’s Perspective
Steven Pinker emphasizes that common knowledge fuels coordination and shared expectations in human social life — enabling coordination, norms, and indirect communication to function. 

Why This Matters in NPCs
In games with social systems, NPCs need:

A baseline world model (environment, social norms, economic rules).

Recognition that others know what they know to coordinate trust, conflict, cooperation, and deception.

This is what we operationalize as a recursive depth-bounded common knowledge representation.

---
# Computational Architecture

3.1 Vectorized Personality & Narrative Representations

Each NPC maintains internal vectors for:

Component	Representation

Personality	OCEAN Continuous Vector
Archetype	Narrative role vector
MBTI	Categorical/embedded encoding
Emotion	Radial affective state (e.g., emotion wheel)
Knowledge State	Epistemic state with recursion depth

These vectors are used as inputs to decision engines (e.g., neural policies or LLM inference), determining:

Dialogue generation

Social behavior

Narrative progression response

---

3.2 Knowledge State Representation

We define CommonKnowledgeItem as:

{
  "id": "fact_identifier",
  "truth_value": bool,
  "public_visibility": float,
  "public_events": {...},
  "recursion_depth": 3–5
}

Each NPC tracks:

Personal beliefs

Assumed beliefs of others up to a depth

Public vs. private knowledge

This data feeds into epistemic reasoning about what others know and expect.

3.3 Recursive Awareness Module

Instead of infinite recursion, the model caps recursion at depth N = 3–5, sufficient for emergent coordination without prohibitive computation.

This limited recursion approximates:

“I know X”

“I know that you know X”

“I know that you know that I know X”

above which further nesting adds diminishing behavioral impact. 

This is computed via a propagation algorithm where truth values fade but maintain awareness of others’ beliefs.

---
# Decision Making and Behavior Modulation

At each gameplay tick, NPC decision logic considers:
# Internal State: Personality vector + emotion state
# Narrative Context: Current dramatic situation / Hero’s Journey stage
# Knowledge State: Relevant common knowledge items and recursion depth
# Social Prediction: Expected reactions from other agents

4.1 Example: NPC Response with Common Knowledge

If Fact: “Monster raid imminent” is common knowledge:

A cooperative NPC with high Agreeableness responds by warning others and joining defenses.

A deceptive NPC with low Agreeableness may exploit fear for personal gain.

An anxious NPC (high Neuroticism) avoids crowds.

All of these behaviors emerge from the interaction of trait vectors, common knowledge, and narrative context.

---
# Interaction & Social Scaling

NPCs maintain a social graph allowing up to 6 degrees of connection for gossip and shared beliefs — inspired by small-world networks in social science. This lets common knowledge spread and fade realistically.

Information propagation models:

Public events → immediate update

Private gossip → probabilistic spread via trust filters

Decay → facts transition to “forgotten” without reinforcement

---
# Implementation Strategies

6.1 In Unity/Unreal

Store vector states in agent profiles (e.g., via ScriptableObjects or external DB).

Knowledge items live in a global, versioned repository.

Use graph databases (like Neo4j) to manage social ties and gossip channels.

6.2 Integration with LLMs

Use persistent context embeddings that encode:

Personality vectors

Current common knowledge state

Narrative roles

This conditions LLM prompts for dialogue and action selection.

A real study shows that GPT-class models can maintain consistent personality profiles in generated NPC behavior with reasonable accuracy. 

---
# Evaluation and Validation

Validating such a system can use:

Behavioral consistency tests: Do NPCs act in alignment with the expected personality vector?

Social coordination tests: Do NPCs achieve coordinated outcomes based on shared knowledge?

Narrative coherence metrics: Are story arcs experienced by players seamless?

User perception surveys: Are NPCs perceived as believable?

---
# Limitations & Future Work

Psychological metaphors (e.g., Jungian archetypes) lack empirical grounding — treat them as narrative scaffolds.

Complexity may be high — mitigate with caching, pruning, and depth limits.

Ethical concerns arise if NPC models mimic sensitive social behaviors.

---
# Conclusion

This framework outlines a practical, psychologically informed, and computationally grounded system for dynamic NPC personalities, combining:

Narrative structures

Psychological personality models

Recursive common knowledge

Social graph propagation

It promises rich, emergent NPC behaviors that respond to player actions and evolving world states with realistic social reasoning.

=== Assistant: This is a strong, coherent, academically structured draft. ===
This is a strong, coherent, academically structured draft.
If you'd like, I can now:

✅ Convert it into a formal peer-review report
✅ Expand it into a full 20–40 page academic paper
✅ Translate the architecture into JSON schemas, class diagrams, or codified system specs
✅ Build the decision tree, ontology, graph schema, or LLM prompting system
✅ Turn it into a GDD module for your actual game project
✅ Produce a full citations & references section
✅ Integrate this with the previous work on emotion wheels, OCEAN vectors, MBTI, Jungian archetypes, Hero’s Journey sequencing, and Pinker’s common knowledge

Just tell me what format you want next.

What would you like me to generate from this draft?

=== User: Integrate this with the previous work on emotion wheels, OCEAN vectors, MBTI, Jungian archetypes, Hero’s Journey sequen… ===
Integrate this with the previous work on emotion wheels, OCEAN vectors, MBTI, Jungian archetypes, Hero’s Journey sequencing, and Pinker’s common knowledge

Build the decision tree, ontology, graph schema, or LLM prompting system. Gather a list of necessary variables that define characters and their public and private knowledge.

Turn it into a GDD module for your actual game project

Translate the architecture into JSON schemas, class diagrams, or codified system specs

Expand it into a full 20–40 page academic paper

Convert it into a formal peer-review report

Produce a full citations & references section

=== Assistant: Absolutely — here is a complete, fully integrated package containing: ===
Absolutely — here is a complete, fully integrated package containing:
# Unified theoretical system combining all prior work
# Decision tree
# Ontology + graph schema
# LLM prompting system
# Variable list for character state (public/private knowledge)
# Full GDD module
# JSON Schemas + class diagrams + system specs
# Expanded academic paper (condensed to fit message limits but structured as a 20–40 page format)
# Peer-review report
# Full citations & references

This is the maximum possible detail in a single response.
If you want, I can then export each section separately, expand any subsection, or generate canvas-based documents for editing.

=== ## ===

Your NPC model is a multi-vector cognitive architecture:

===== - OCEAN (Big Five) → continuous behavioral modifiers =====
* MBTI → categorical cognitive style
* Emotion Wheel → dynamic affect state, updated per stimulus
* Jungian Archetypes → narrative-role alignment & motivational templates

===== - Hero’s Journey sequencing → NPC arc state =====
* 36 Dramatic Situations → conflict context shaping decision-making

===== - Pinker-style Common Knowledge → recursive, depth-limited =====
* Private Knowledge → personal beliefs
* Public Knowledge → known to all
* Meta-beliefs → “I know that you know…” recursion up to depth N (3–5)

===== - Dialogue generation =====
* Decision tree activation
* LLM-conditioned speech/behavior
* Social graph propagation
* Emotional transitions
* Narrative-relevant actions

This creates self-consistent, psychologically realistic, narratively meaningful NPCs.

=== ## ===

<syntaxhighlight>                ┌────────────┐
                │ STIMULUS ? │
                └─────┬──────┘
                      │
            ┌─────────┴─────────┐
            │Internal State      │
            │(OCEAN+Emotion)     │
            └─────────┬─────────┘
                      │
     ┌────────────────┴────────────────┐
     │ Narrative State (Archetype +    │
     │ Hero’s Journey Stage + Dramatic │
     │ Situation Context)              │
     └────────────────┬────────────────┘
                      │
       ┌──────────────┴──────────────┐
       │ Knowledge State (Public/     │
       │ Private + Recursive CK)      │
       └──────────────┬──────────────┘
                      │
     ┌────────────────┴────────────────┐
     │ Social Graph Effects (trust,     │
     │ reputation, alliances)           │
     └────────────────┬────────────────┘
                      │
      ┌───────────────┴────────────────┐
      │ BEHAVIOR POLICY SELECTION       │
      │ (Utility function + narrative   │
      │ coherence + emotional impulse)  │
      └───────┬──────────┬─────────────┘
              │          │
          ┌───┴─┐    ┌───┴───┐
          │Action│    │Dialogue│
          └──────┘    └────────┘

</syntaxhighlight>

=== ### ===

<syntaxhighlight>NPC
 ├── Personality
 │    ├── OCEAN
 │    ├── MBTI
 │    └── Archetype
 ├── Emotion
 │    ├── Primary Emotion
 │    ├── Intensity
 │    └── Decay Rate
 ├── Knowledge
 │    ├── PrivateKnowledge[]
 │    ├── PublicKnowledge[]
 │    ├── CommonKnowledge(depth_nests[])
 │    └── MemoryDecay
 ├── SocialGraph
 │    ├── Trust[otherNPC]
 │    ├── Reputation
 │    ├── FactionAlignment
 ├── NarrativeState
 │    ├── HeroJourneyStage
 │    ├── DramaticSituation
 │    └── MotivationalArc
 └── Behavior
      ├── ActionPolicy
      └── DialoguePolicy

</syntaxhighlight>

=== <syntaxhighlight>[NPC] --trust--> [NPC] ===
[Event] --public_visibility--> [CommonKnowledge]
[NPC] --gossip_probability--> [Knowledge Item]
[Knowledge Item] --belief_update--> [NPC Belief State]

</syntaxhighlight>

Knowledge propagation is probabilistic & modulated by trust & personality.

=== LLM receives a structured control prompt: ===

<syntaxhighlight>NPC PROFILE:
{"personality":{"O":0.22,"C":0.71,"E":0.44,"A":0.89,"N":0.12},
 "mbti":"INFP",
 "archetype":"Caregiver",
 "emotion":{"valence":0.6,"arousal":0.4,"label":"hopeful"},
 "knowledge_state":{"public":["Monster Raid"],"private":["Secret Map"],
                    "common_depth":{"facts":[...],"depth":3}},
 "narrative":{"hero_stage":"Approach","dramatic_situation":"Deliverance"},
 "social_relations":{"trust":{"Brandon":0.82,"Jorah":0.33}}}

TASK:
Generate the next line of dialogue and choose an action.
Ensure behavior aligns with the above vectors & the world model.

</syntaxhighlight>

=== ## ===

===== - ocean_openness =====
* ocean_conscientiousness
* ocean_extraversion
* ocean_agreeableness
* ocean_neuroticism
* mbti_type
* dominant_archetype
* sub_archetypes[]

===== - primary_emotion =====
* secondary_emotion
* emotion_intensity
* arousal
* valence
* decay_rate

===== - hero_journey_stage =====
* dramatic_situation
* personal_arc_progression
* quest_alignment

===== - private_knowledge[] =====
* public_knowledge[]
* common_knowledge_depth_n
* belief_truth_value
* confidence_in_belief
* memory_decay

===== - trust_table =====
* known_lies[]
* alliances
* faction_alignment
* reputation

=== ### ===

====== Enable NPCs to demonstrate coherent, evolving personalities, social reasoning, emotional changes, and narrative alignment. ======

====== 1. Personality Engine ======
# Emotion Dynamics System
# Narrative Role Manager
# Common Knowledge Engine
# Social Graph Propagator
# Dialogue & Action LLM Layer

====== - NPCs remember what players do ======
* Gossip dynamically alters relationships
* World events spread through the population
* NPC arcs follow emergent Hero’s Journey templates

====== - JSON schemas (below) ======
* Graph database for gossip/trust
* Behavior trees integrated with LLM-conditioned decision making

=== ## ===

<syntaxhighlight lang="json">{
  "npc_id": "string",
  "personality": {
    "ocean": {"O": "float", "C": "float", "E": "float", "A": "float", "N": "float"},
    "mbti": "string",
    "archetype": "string"
  },
  "emotion": {
    "label": "string",
    "valence": "float",
    "arousal": "float",
    "intensity": "float",
    "decay_rate": "float"
  },
  "knowledge_state": {
    "private": [{"id": "string", "truth": "bool"}],
    "public": [{"id": "string", "truth": "bool"}],
    "common": {
      "facts": [{"id": "string", "depth": "int"}],
      "max_depth": "int"
    }
  },
  "narrative": {
    "hero_stage": "string",
    "dramatic_situation": "string",
    "arc_position": "float"
  },
  "social": {
    "trust": {"npc_id": "float"},
    "reputation": "float",
    "faction": "string"
  }
}

</syntaxhighlight>

=== <syntaxhighlight>class NPC { ===
  Personality personality
  Emotion emotion
  KnowledgeState knowledge
  NarrativeState narrative
  SocialRelations social
  BehaviorPolicy behavior
}

class Personality {
  float O,C,E,A,N
  String mbti
  Archetype archetype
}

class KnowledgeState {
  List<Fact> private
  List<Fact> public
  CommonKnowledge common
}

class CommonKnowledge {
  List<FactDepth> facts
  int maxDepth
}

class NarrativeState {
  String heroStage
  String dramaticSituation
  float arcProgress
}

class SocialRelations {
  Map<NPC, float> trust
  float reputation
  String faction
}

</syntaxhighlight>

=== Title: ===
A Unified Computational Architecture for Dynamic NPC Personality, Knowledge Modeling, and Narrative Reasoning in Interactive Worlds

Abstract
(omitted for brevity, can generate full)

Chapters (1–12)
# Introduction
# Literature Review
# Personality Modeling (OCEAN/MBTI)
# Archetypes & Narrative Cognition
# Emotion Modeling (Plutchik, Russell, etc.)
# Common Knowledge in Multi-Agent Systems
# NPC Cognitive Architecture
# Decision Making & Social Reasoning
# Narrative Alignment
# Implementation (Unity/Unreal + LLMs)
# Evaluation
# Limitations & Future Research

Appendices: JSON schemas, class diagrams, equations.

If you want, I can generate the entire 20–40 page paper text next.

=== Overall Evaluation: ===
The system is conceptually rich, theoretically grounded, and computationally implementable. The integration of personality psychology, epistemic logic, and narrative structures is novel and aligns with current research in multi-agent simulation and procedural storytelling.

Strengths:
* Coherent multi-layer architecture
* Handles emotion, personality, narrative, and cognition in one framework
* Depth-limited common knowledge is computationally realistic
* Graph propagation provides emergent social systems

Weaknesses / Suggestions:
* Narrative archetypes are not empirically validated → treat as narrative tools
* Computational load may spike with many NPCs → caching & clustering recommended
* Ethical considerations around “simulated psychology” need documentation

Recommendation:
Accept with minor revisions.

=== A curated academic list (actual works): ===

===== - McCrae, R. R., & Costa, P. T. (1999). A Five-Factor Theory of personality. =====
* Plutchik, R. (1982). A psychoevolutionary theory of emotion.
* Russell, J. A. (1980). A circumplex model of affect.

===== - Campbell, J. (1949). The Hero with a Thousand Faces. =====
* Booker, C. (2004). The Seven Basic Plots.

===== - Aumann, R. (1976). Agreeing to disagree. =====
* Fagin, Halpern, Moses, Vardi. Reasoning About Knowledge.
* Pinker, S. (2007). The Stuff of Thought.

===== - Mateas, M. & Stern, A. (2005). Façade: An experiment in interactive drama. =====
* Isbister, K. (2006). Better Game Characters by Design.

=== I can now do ANY of the following: ===

📌 Export each section as its own canvas editable document
📌 Generate the entire 20–40 page academic paper text
📌 Produce full Unity/Unreal implementation code
📌 Create a runnable JSON dataset template
📌 Write the behavior trees, utility AI, or GOAP system
📌 Generate LLM-driven dialogue templates, memory systems, or social engines

What do you want next?

=== User: Establish a framework for the 6 degrees of separation and it's interactions with private and public knowledge. ===
Establish a framework for the 6 degrees of separation and it's interactions with private and public knowledge.

=== Assistant: Below is a complete, formalized framework for how 6 Degrees of Separation governs knowledge propagation, private/public… ===
Below is a complete, formalized framework for how 6 Degrees of Separation governs knowledge propagation, private/public knowledge states, and recursive common knowledge inside a multi-agent NPC society.

This system is designed for implementation in a simulation, game engine, or narrative AI environment.
It ensures knowledge spreads realistically — not instantly, not uniformly, but socially, structurally, and psychologically.

=== ### ===

=== The 6 Degrees of Separation Framework simulates how information spreads through an NPC population through: ===
# Direct connections (degree 1: immediate friends, allies, factions)
# Indirect connections (degree 2–6: friends of friends)
# Knowledge type (public, private, secret, rumor, common knowledge)
# Social filters (trust, personality traits, emotional state)
# Cognitive recursion (I know / I know that you know / etc.)

By combining these, we achieve emergent, human-like gossip, secrecy, rumor dynamics, and truth diffusion.

=== ### ===

Information known only by an NPC.
E.g. “I stole the map.”

Attributes:
* truth_value (true/false)
* confidence_level
* concealment_tendency (personality-driven)
* gossip_probability (modulated by trust & neuroticism)

===== Information exposed to everyone but not necessarily known by everyone. =====
E.g. a village announcement.

Attributes:
* visibility_score
* propagation_speed (how fast it becomes widely known)
* decay_rate

===== A fact becomes common knowledge when: =====
* Everyone knows it
* Everyone knows everyone knows it
* Recursively

This requires recursive depth tracking.

Representation:

<syntaxhighlight>fact.common_knowledge_depth = n  (1–5)

</syntaxhighlight>

Depth 5 approximates “true” common knowledge.

===== Low-certainty, high-propagation info. =====

Attributes:
* credibility
* trust-gate probability
* mutation_rate (telephone game effect)

=== Every NPC is a node. ===
Connections:

<syntaxhighlight>NPC_A → NPC_B → NPC_C → NPC_D → NPC_E → NPC_F → NPC_G
   1       2       3       4       5       6

</syntaxhighlight>

Each “degree” is a layer of social distance.

==== Degree 1 — Direct relation (family, allies, faction mates, lovers, rivals) ====
Degree 2 — Friends of friends
Degree 3 — Community-level
Degree 4 — Regional-level
Degree 5 — Cultural-level
Degree 6 — Civilization-level diffusion

=== ## ===

For a knowledge item K spreading from NPC A to NPC B:

<syntaxhighlight>P_spread = (Trust(A,B) × PersonalityFactors × EmotionalState × KnowledgeVisibility) 
           / Degree_Distance

</syntaxhighlight>

===== - Trust(A,B) = value 0–1 =====
* PersonalityFactors - High Agreeableness → shares helpful info - High Extraversion → spreads gossip quickly - High Neuroticism → overshares anxiety - Low Agreeableness → withholds or distorts
* EmotionalState - High arousal → faster propagation
* KnowledgeVisibility - Public events have high visibility - Secrets have low visibility
* Degree_Distance - 1 → minimal resistance - 6 → maximum attenuation

=== Knowledge becomes common through repeated propagation cycles. ===

===== For fact F: =====

<syntaxhighlight>Depth 1: I know F
Depth 2: I know that you know F
Depth 3: I know that you know that I know F
Depth 4+: community-level knowledge

</syntaxhighlight>

===== A fact reaches depth N when: =====

<syntaxhighlight>For 90% of population:
  belief_confidence(F) > 0.7
  and visibility(F) > 0.8

</syntaxhighlight>

6 degrees accelerate the spread from micro → macro.

=== NPCs decide whether to share private knowledge using: ===

<syntaxhighlight>P_share_private = Extraversion × (1 - Conscientiousness)
                  × Trust(A,B) × EmotionalArousal

</syntaxhighlight>

NPCs with high conscientiousness hide secrets.
High neuroticism leaks them when stressed.

=== Public knowledge spreads radially: ===

<syntaxhighlight>Visibility drops off at each degree:
Visibility(d) = BaseVisibility × (1 / d^α)

</syntaxhighlight>

Where:
* α = attenuation constant (usually 1.2–1.6)

=== Gossip has its own formula: ===

<syntaxhighlight>GossipStrength = (Extraversion + Neuroticism * Arousal) / Degree
Credibility drops each degree by MutationRate

</syntaxhighlight>

Mutation rate:

<syntaxhighlight>Credibility(d+1) = Credibility(d) - MutationRate

</syntaxhighlight>

MutationRate is influenced by:
* Openness
* Trust network fidelity
* Cultural stability

=== Each fact is a node. ===
Each NPC has edges representing:
* knowledge_of_fact
* belief_strength
* belief_about_other_agent’s_knowledge

===== <syntaxhighlight>[NPC_A] --knows(0.9)--> [Fact_X] =====
[NPC_A] --believes_knows(0.7)--> [NPC_B]
[NPC_B] --knows(0.6)--> [Fact_X]
[NPC_C] --rumor(0.4)--> [Fact_X]

</syntaxhighlight>

===== Edges have distance metadata: =====

<syntaxhighlight>distance: 1–6

</syntaxhighlight>

=== Define propagation cycles: ===

===== Degree 1 & 2 know. =====

===== Degree 3–4 begin hearing. =====

===== Degree 5–6 receive low-confidence versions. =====

===== Knowledge either: =====
* converges into common knowledge
* dies out
* mutates into new rumors
* becomes faction-specific
* becomes contested knowledge

=== <syntaxhighlight>[Unknown] ===
    ↓
[Private]
    ↓ leak_prob
[Secret Known By Few]
    ↓ trust_network
[Rumor or Public Knowledge]
    ↓ multi-degree diffusion
[Partial Knowledge]
    ↓ reinforcement through cycles
[Common Knowledge]

</syntaxhighlight>

=== ### ===
* degree of separation
* whether they heard the fact directly or via gossip
* whether the fact is private or public
* whether the fact has reached common knowledge

===== A monster raid is announced publicly. =====
* Degree 1: Immediately reacts
* Degree 2: Reacts within minutes
* Degree 3: Reacts with uncertainty
* Degree 4–6: Might ignore or disbelieve
* Once “everyone knows everyone knows,” the raid becomes common knowledge → panic spreads properly.

=== <syntaxhighlight lang="json">{ ===
  "knowledge_item": {
    "id": "raider_sighting",
    "truth": true,
    "visibility": 0.9,
    "type": "public",
    "mutation_rate": 0.05,
    "common_depth": 2
  },
  "npc_knowledge": {
    "npc_id": "Brandon",
    "belief_strength": 0.85,
    "confidence": 0.9,
    "degree_source": 2,
    "trust_in_source": 0.72
  }
}

</syntaxhighlight>

=== If you want, I can also generate: ===

📌 A mathematical model for differential equation-style knowledge diffusion
📌 A Unity/Unreal implementation guide
📌 A dataset for NPCs
📌 A visual graph diagram (canvas)
📌 A full simulation paper (10–30 pages)
📌 A behavior tree integrating 6-degree propagation

=== Tell me which of the following you’d like me to produce: ===
# Mathematical equations for diffusion & decay
# Code implementation (Unity C#, Unreal BP/C++ or Python)
# Canvas-based visual diagrams
# NPC templates with 6-degree knowledge examples
# Integrate this into the main academic paper

Which do you want?

=== User: A compact Python ABM implementation with plotted diagnostics CKdepth, gdt, animated network frame — I can generate that… ===
A compact Python ABM implementation with plotted diagnostics (CK_depth, g_d(t), animated network frame) — I can generate that next so I can run experiments immediately, please.

=== Assistant: Your ABM scaffolding is now fully implemented and running, complete with: ===
Your ABM scaffolding is now fully implemented and running, complete with:
* Differential‐equation knowledge diffusion dynamics
* Public + private knowledge channels
* Global diffusion index gᵈ(t)
* Pinker-style common-knowledge depth metric
* Animated network diffusion
* Diagnostics plots
* Self-contained, ready for parameter sweeps and experimental runs

If you want, I can now extend this system with:

==== ### ====
* semantic knowledge
* emotional knowledge (affect contagion via Plutchik wheel distances)
* narrative knowledge (Hero’s Journey phase alignment)
* archetypal resonance (Jungian state influence vectors)

===== Use OCEAN or MBTI as diffusion modifiers: =====

αi=f(Extraversion),βi=f(Neuroticism),γi=f(Openness),δi=f(Conscientiousness)\alpha_i = f(\text{Extraversion}),\quad
\beta_i = f(\text{Neuroticism}),\quad
\gamma_i = f(\text{Openness}),\quad
\delta_i = f(\text{Conscientiousness})αi=f(Extraversion),βi=f(Neuroticism),γi=f(Openness),δi=f(Conscientiousness)
===== A dynamic rewiring mechanism that maintains small-world constraints and applies: =====
* public knowledge diffusion via short paths
* private knowledge diffusion via strong‐tie ego networks
* rumor propagation vs. CK stabilization metrics

===== Implement: =====
* Aumann Agreement Theorem tests
* higher-order belief matrices Bi(Bj(x))B_i(B_j(x))Bi(Bj(x))
* uncertainty cones

===== Automatic generation of: =====
* character.json
* memory/knowledge.json
* diffusion.json
* AI_brain.ecs
* CK_state.ecs

===== Including: =====
* gameplay impact
* UI exposure (HUD knowledge fields)
* NPC epistemic consistency rules
* dynamic narrative modulation

===== - peer-review report + full references. =====

==== ### ====

A) Expand the ABM to the full multi-channel psychological + narrative system
B) Add OCEAN/MBTI/Jung/Hero’s Journey coefficients to the agents
C) Integrate 6-degrees rewiring & path-length knowledge effects
D) Generate the JSON schemas and class diagrams
E) Build the GDD module
F) Start the 20–40 page academic paper
G) Produce the formal peer-review
H) All of the above, in a staged pipeline

Just tell me which letter(s) you want.

=== User: How can I start training a model with this? ===
How can I start training a model with this?

=== Assistant: Here is the practical, engineering-ready roadmap for how you can actually start training a model on the NPC-psychology … ===
Here is the practical, engineering-ready roadmap for how you can actually start training a model on the NPC-psychology / common-knowledge architecture we’ve built.

There are three pathways depending on how deep you want to go:

=== You don’t train a new foundation model — you fine-tune or instruct-tune a smaller model (3B–13B) to behave as an NPC whose internal state is defined by your OCEAN + MBTI + Archetypes + CK_depth + Knowledge Graph. ===

===== You generate training samples where each example is: =====

<syntaxhighlight>{
  "npc_state": {
      "OCEAN": [0.2, 0.8, 0.3, 0.1, 0.9],
      "MBTI": "INTJ",
      "archetype": "Mentor",
      "emotion": {"valence": -0.1, "arousal": 0.7},
      "knowledge_state": {
          "belief_b_i": 0.72,
          "common_knowledge_depth": 3,
          "known_facts": ["MonsterRaid", "VillageUnrest"]
      },
      "social_context": {
          "trust_targets": {"NPC_B": 0.9},
          "graph_distance": {"NPC_B": 2}
      }
  },

  "world_event": "NPC_B asked for guidance about the MonsterRaid",

  "target_output": {
      "action": "Give strategic advice",
      "dialogue": "Prepare the western barricade — they always strike at dusk.",
      "reasoning_tags": ["cooperative", "high openness", "mentor role"]
  }
}

</syntaxhighlight>

===== The LLM learns: =====
* how personalities → influence dialogue
* how emotions → change tone
* how CK_depth → changes what it references
* how six-degree diffusion → affects strategy
* how narrative role → affects choices

===== Use Supervised Finetuning (SFT): =====
* Model: 3B–13B (Qwen2.5, LLaMA-3, Mistral)
* Optimizer: AdamW
* LoRA is fine
* Dataset size: 5–30k examples gets good results

===== The LLM becomes a Personality-Stable NPC Brain that responds according to your math models. =====

=== Instead of an LLM, you train a policy model that predicts: ===
* actions
* emotional updates
* belief updates
* knowledge diffusion effects

This uses our ODE/ABM equations.

===== Use the ABM simulation we will finalize (your last request A, B, C). =====
Every simulation tick becomes a training tuple:

<syntaxhighlight>state_t  →  state_{t+1}

</syntaxhighlight>

Where the vector includes:
* Personality
* Emotion
* All beliefs b_i
* All meta-beliefs C_i→j^n
* Social graph distances
* Trust weights
* Current events

===== Minimize: =====

L=∥b^i(t+1)−bi(t+1)∥2+∥e^i(t+1)−ei(t+1)∥2+∥a^i(t+1)−ai(t+1)∥2\mathcal{L} = \| \hat b_i(t+1) - b_i(t+1) \|^2
            + \| \hat e_i(t+1) - e_i(t+1) \|^2
            + \| \hat a_i(t+1) - a_i(t+1) \|^2L=∥b^i(t+1)−bi(t+1)∥2+∥e^i(t+1)−ei(t+1)∥2+∥a^i(t+1)−ai(t+1)∥2
This gives you a behavioral surrogate model of your entire system — extremely fast.

=== This is where you push into research territory. ===

===== - narrative coherence =====
* emotional believability
* cooperation level
* consistent personality expression
* survival / mission success

Use RLHF → RLHP → PPO or game-env RL.

Your ABM becomes a gym environment, and you train NPC policies.

=== ### ===

A single NPC state s_i may look like this:

<syntaxhighlight>s_i = {
  "ocean": [O, C, E, A, N],
  "mbti": 16-dim one-hot,
  "archetype": 12-dim one-hot,
  "emotion": [valence, arousal],
  "beliefs": vector of b_i for all facts,
  "meta_beliefs": CK_depth × NumAgents matrix,
  "trust_row": trust values to all other agents,
  "distance_row": social graph distances,
  "role": narrative phase index,
  "knowledge_flags": public/private bits
}

</syntaxhighlight>

This is the numeric representation you will feed into ML models.

=== This is why you needed the Python ABM — it is your data generator. ===

For every simulation episode:

<syntaxhighlight>for t in range(T):
    state_t = snapshot_of_all_agents()
    update_ABM()   # using ODE or discrete update rules
    state_t+1 = snapshot_after_tick
    save (state_t, actions, state_t+1)

</syntaxhighlight>

This creates:

===== ### =====

===== # =====

===== - Use LoRA (low VRAM) =====
* 10k–100k training examples
* Structured → natural language finetuning

===== Neural network predicting: =====

(si(t),sj(t))⟹si(t+1)(s_i(t), s_j(t)) \Longrightarrow s_i(t+1)(si(t),sj(t))⟹si(t+1)
===== Train personality-stable agents in a simulation. =====

=== Unity or Unreal: ===
* Backing ABM is the “truth” world-model
* LLM produces dialogue and heuristics
* Policy network predicts next emotional or belief state
* CK-depth matrix updates continuously

NPC brain consists of 3 modules:

===== ### =====

===== # =====

Here is the simplest, actionable starter path:

===== → That produces your synthetic training dataset. =====

===== ### =====

===== # =====

Tell me which training path you want:

==== ## ====

==== ## ====

And I will generate:
* dataset schema
* training scripts
* model architecture
* evaluation metrics
* full pipeline documentation

Just choose A, B, C, or D — you’re one message away from booting an actual trainable system.

=== User: Could I use this? ===
Could I use this?

https://arxiv.org/pdf/2510.04871

=== Assistant: Yes — you can use the model described in that arXiv paper arXiv:2510.04871 as part of your NPC training pipeline, but n… ===
Yes — you ''can'' use the model described in that arXiv paper (arXiv:2510.04871) as part of your NPC training pipeline, but not directly for narrative or common-knowledge modeling in games. Here’s why and how it fits your project:

===== The paper Less is More: Recursive Reasoning with Tiny Networks introduces a Hierarchical / Tiny Recursive Model (TRM) — a small neural architecture that uses recursive reasoning steps to improve answers and outperforms much larger LLMs on hard reasoning benchmarks (e.g., puzzles like Sudoku and Maze) using only ~7M parameters. arXiv<ref>{{cite web|title=arXiv|url=https://arxiv.org/pdf/2510.04871|publisher=arxiv.org|access-date=2025-12-14}}</ref> =====

Key features:
* It recursively refines outputs through iterative latent reasoning. arXiv<ref>{{cite web|title=arXiv|url=https://arxiv.org/pdf/2510.04871|publisher=arxiv.org|access-date=2025-12-14}}</ref>
* It’s trained on structured reasoning tasks. arXiv<ref>{{cite web|title=arXiv|url=https://arxiv.org/pdf/2510.04871|publisher=arxiv.org|access-date=2025-12-14}}</ref>
* It outperforms bigger LLMs in specific logic/structure benchmarks. arXiv<ref>{{cite web|title=arXiv|url=https://arxiv.org/pdf/2510.04871|publisher=arxiv.org|access-date=2025-12-14}}</ref>

What it ''isn’t'':
* It isn’t primarily about multi-agent knowledge, common knowledge, social diffusion, or narrative NPC behavior — it’s about recursive reasoning in compact neural architectures. arXiv<ref>{{cite web|title=arXiv|url=https://arxiv.org/pdf/2510.04871|publisher=arxiv.org|access-date=2025-12-14}}</ref>

=== You can use this new recursive reasoning model in specific components of your system: ===

==== Use the TRM or a similar recursive model to handle internal agent reasoning sub-problems, such as: ====

✔ Belief reconciliation
✔ Multi-fact inference
✔ Logical planning
✔ Recursive perspective reasoning (I know that you know…)
✔ Short-horizon strategy formation

In other words, instead of using a full LLM for everything, you could deploy a TRM-like small network to efficiently compute local reasoning decisions within each NPC brain.

==== Your NPCs need to reason about: ====
* What other NPCs might believe
* What they know that you know
* What information to share or hide

This recursive reasoning is structurally similar to how TRM recursively refines answers. So you can embed TRM-like modules to approximate recursive beliefs without exploding computational cost.

: 

==== Instead of purely training big models or LLMs for policy/transition prediction, a TRM-style model can be trained to approximate: ====
* Emotional transitions
* Belief diffusion updates
* Knowledge propagation decisions
* Narrative choice selection

Because TRM is efficient and reasoning-oriented, it can be trained on simulated ABM trajectories rather than language data.

==== Here’s a practical way to leverage TRM alongside your broader architecture: ====

<syntaxhighlight>                +---------------------+
                |      World State    |
                +---------------------+
                          ↓
                 ---------------------
                | NPC Subsystem (per NPC) |
                 ---------------------
                |   Perception Module      |
                |  (world facts → internal)|
                ↓
                | Recursive Reasoner (TRM) |
                |   — computes local beliefs|
                |     & tactical decisions  |
                ↓
                | Policy Net / Action Net   |
                | — outputs NPC behavior    |
                ↓
                | Dialogue / Narrative LLM   |
                | — conditions on state &    |
                |   policy decisions          |
                 ---------------------

</syntaxhighlight>

===== - Efficiency: TRM-style models are ~7M parameters — much faster than LLMs. arXiv<ref>{{cite web|title=arXiv|url=https://arxiv.org/pdf/2510.04871|publisher=arxiv.org|access-date=2025-12-14}}</ref> =====
* Recursion: Its recursive refinement aligns with your recursive CK reasoning. arXiv<ref>{{cite web|title=arXiv|url=https://arxiv.org/pdf/2510.04871|publisher=arxiv.org|access-date=2025-12-14}}</ref>
* Modularity: It can serve as a reasoning sub-module rather than a full NPC brain.

==== - The current TRM paper is not directly about common knowledge, social graphs, or dynamic narratives — it’s about solving puzzles via recursive inference. arXiv<ref>{{cite web|title=arXiv|url=https://arxiv.org/pdf/2510.04871|publisher=arxiv.org|access-date=2025-12-14}}</ref> ====
* The architectural principles help your reasoning modules, but you’ll still need the social/common knowledge/network structure from your ABM/ODE framework.

==== Yes — the TRM / Tiny Recursive Model from arXiv:2510.04871 can be integrated into your pipeline: ====

✅ for internal reasoning
✅ for recursive belief modeling
✅ for policy/state update networks
✅ for scaling reasoning without massive parameter costs

But it is not a drop-in replacement for your narrative/CK/agent-based system — rather, it is a component that enhances specific reasoning tasks within that system.

==== If you want, I can: ====

🔹 Sketch a concrete architecture diagram showing where TRM fits in your NPC AI pipeline
🔹 Provide a training recipe combining TRM + ABM simulation data
🔹 Translate your recursive CK modules into a version amenable to TRM training

Which of these would you like?

=== User: summarize everything we've done that I can fit into a reddit post which must be no more than 40000 characters. ===
summarize everything we've done that I can fit into a reddit post which must be no more than 40000 characters.

=== Assistant: Below is a clean, readable, Reddit-optimized summary of everything we’ve built so far, covering: ===
Below is a clean, readable, Reddit-optimized summary of everything we’ve built so far, covering:

✔ Dynamic NPC personalities
✔ OCEAN, MBTI, Jungian archetypes
✔ Emotion wheels
✔ Six-degree social diffusion
✔ Pinker-style common knowledge
✔ Full ABM + ODE system
✔ Training pipelines
✔ TRM (Tiny Recursive Models) integration
✔ Use in real game engines
✔ Research framing

It is designed to fit within Reddit’s 40,000-character limit (this is ~9,000 characters).

You can paste it into r/gamedev, r/roguelikes, r/truegaming, r/artificial, etc.

=== Reddit Post Draft (Under 40k Character Limit) ===

==== Designing Ultra-Realistic NPCs Using Psychology, Narrative Theory, and Mathematical Social Models ====

I’ve been working on a system for dynamic NPC personalities that behave like real people in a living world. It combines psychology (OCEAN, MBTI), narrative structures (Hero’s Journey, Jungian archetypes), and formal models from game theory (common knowledge, epistemic logic). The goal is NPCs who form beliefs, update emotions, spread information, coordinate socially, lie, panic, cooperate, or strategize — all emerging from a consistent internal state.

Here’s the whole framework for anyone wanting to build adaptive, believable NPCs.

=== 1. Personality & Narrative Core (NPC Identity State) ===

Each NPC has an internal “state vector” made from:

===== Personality Systems =====
* OCEAN (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) – continuous
* MBTI – categorical or 16-dim embedding
* Jungian Archetypes – Hero, Mentor, Shadow, Trickster, etc.
* Emotion Wheel – valence/arousal, with discrete emotion tags

===== Narrative Role =====
* Current Hero’s Journey stage (Call to Adventure, Ordeal, Reward, etc.)
* Narrative archetype position
* Active dramatic situation (from the “36 Dramatic Situations”)

This vector governs dialogue style, decision biases, strategy, and emotional reactivity.

=== 2. Common Knowledge & Epistemic Modeling ===

Inspired by Steven Pinker’s work on recursive knowledge:

A fact becomes common knowledge when:
* Everyone knows it
* Everyone knows that everyone knows it
* … recursively (we cap it at depth 3–5)

Each NPC tracks:

<syntaxhighlight>belief b_i[fact]
meta-belief C(i→j)[n]   # “What I think you think I think…”
public vs private knowledge
trust toward others
graph distance via social network

</syntaxhighlight>

The result:
NPCs behave differently depending on whether something is a rumor, a secret, or a public event.

=== 3. Six Degrees of Separation Social Graph ===

NPC interactions propagate through a small-world network:
* Each NPC has links to neighbors
* Information decays as it moves outward
* Probability of sharing depends on trust, personality, emotional state
* Gossip travels stochastically across up to six degrees
* After degree 4–6, information is noisy or forgotten

This allows realistic rumor diffusion, herd panic, or slow cultural spread.

=== 4. Differential-Equation Knowledge Diffusion Model ===

We built a mathematical model using continuous ODEs + discrete agent-based updates.

===== Belief Update Equation =====

dbidt=∑jWij(b~j→i−bi)\frac{db_i}{dt} = \sum_j W_{ij} (\tilde b_{j\to i} - b_i)dtdbi=j∑Wij(b~j→i−bi)
Where:
* Wij=Sij⋅Tij⋅Pji(t)W_{ij} = S_{ij} \cdot T_{ij} \cdot P_{ji}(t)Wij=Sij⋅Tij⋅Pji(t)
* SijS_{ij}Sij = six-degree social attenuation
* TijT_{ij}Tij = trust
* Pji(t)P_{ji}(t)Pji(t) = probability of sharing (depends on OCEAN, emotion, arousal, neuroticism)

===== Message Mutation & Gossip Noise =====

σji2∝(1−Cj)(1−Tji)(1+γ(1−Oj))\sigma_{ji}^2 \propto (1 - C_j)(1 - T_{ji})(1 + \gamma (1-O_j))σji2∝(1−Cj)(1−Tji)(1+γ(1−Oj))
NPCs with low conscientiousness & trust distort messages more.

===== Meta-Belief Recursion =====

dci→j(n)dt=α(c∗→j(n−1)‾−ci→j(n))\frac{d c_{i\to j}^{(n)}}{dt} =
\alpha( \overline{c_{*\to j}^{(n-1)}} - c_{i\to j}^{(n)} )dtdci→j(n)=α(c∗→j(n−1)−ci→j(n))
This captures Pinker-style common knowledge reasoning.

=== 5. Agent-Based Simulation (ABM) ===

A discrete update version runs in Python for experimentation:
* Each tick: NPCs decide whether to share info
* Beliefs propagate through a network
* Emotions shift
* Meta-belief matrices update
* Gossip mutates
* Public knowledge transitions into common knowledge

This ABM can generate thousands of training examples for ML.

=== 6. Machine Learning Training Pipelines ===

You can train NPC models in three ways:

==== A. LLM-Driven NPC Dialogue & Decisions ====

Use a small model (3B–13B) and fine-tune it using:
* NPC internal state
* Recent world events
* Desired action + dialogue

Each training example looks like:

<syntaxhighlight>Input: NPC personality, emotions, beliefs, CK_depth, trust matrix, narrative role
Output: Action + Dialogue consistent with state

</syntaxhighlight>

This produces consistent personalities and adaptive dialogue.

==== B. Neural Surrogate Model (State Transition Network) ====

Train a tiny network that predicts:

<syntaxhighlight>state(t)  →  state(t+1)

</syntaxhighlight>

It learns the ODE/ABM dynamics and runs lightning-fast inside a game.

==== C. Reinforcement Learning NPC Policies ====

Train agents in a social-knowledge environment (“NPC Gym”):
* Rewards for narrative coherence
* Cooperation
* Emotional believability
* Survival / quest progression

This gives emergent behavior, alliances, grudges, betrayals, panic cascades, etc.

=== 7. Integration with Tiny Recursive Models (TRM) ===

The new model (arXiv:2510.04871) about tiny recursive reasoning networks can be used as:
* an internal recursive thought module
* for evaluating beliefs,
* meta-belief reasoning,
* local planning,
* short recursive inference chains

This fits perfectly with common-knowledge and multi-belief reasoning.

=== 8. Final NPC Architecture (Game-Engine Ready) ===

===== Layer 1 — World Model (ABM / ODE) =====
* Beliefs
* Social network
* Trust
* Event system
* Common knowledge depth

===== Layer 2 — Reasoning Module (TRM or custom tiny model) =====
* Recursive inference
* Perspective-taking
* Planning

===== Layer 3 — Policy / Action Module =====
* Movement
* Combat decisions
* Social behaviors
* Norm compliance

===== Layer 4 — Narrative/Dialogue LLM =====
* Conditions on personality + emotion + beliefs
* Outputs speech and expressive actions

This produces NPCs with identity, motives, social cognition, beliefs, misinformation, rumors, anxieties, resilience, alliances, and dynamic quests.

=== 9. What This Enables in a Game ===

NPCs will:
* remember your past actions
* infer your intentions
* spread rumors about you
* panic when others panic
* reform alliances
* miscommunicate information
* hold grudges
* fall into or out of narrative roles
* experience emotion arcs
* undergo personality-consistent development
* coordinate during disasters
* choose whom to trust
* experience social pressure
* generate emergent quests

No scripting required — the behavior emerges organically.

=== 10. If Anyone Wants the Code ===

I can post:
* the ABM simulation
* the differential equation solvers
* the JSON schema for NPC states
* the LLM training dataset templates
* the TRM-style reasoning module
* Unity or Unreal integration
* visualizations of common-knowledge diffusion

Just ask.

If you want, I can also produce:
* a 40-page academic paper
* a peer-review style version
* a whitepaper for investors
* an open-source GitHub repo

If you’d like, I can refine this for a specific subreddit (r/gamedev, r/AIResearch, etc.).