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=== 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. 

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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.

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# 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.

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# 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

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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.

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# 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.

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# 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

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# 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. 

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# 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?

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# 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.

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# 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.